Tag Archives: Health

Applying Deep Learning to Metastatic Breast Cancer Detection

Posted by Martin Stumpe, Technical Lead and Craig Mermel, Product Manager, Healthcare, Google AI

A pathologist’s microscopic examination of a tumor in patients is considered the gold standard for cancer diagnosis, and has a profound impact on prognosis and treatment decisions. One important but laborious aspect of the pathologic review involves detecting cancer that has spread (metastasized) from the primary site to nearby lymph nodes. Detection of nodal metastasis is relevant for most cancers, and forms one of the foundations of the widely-used TNM cancer staging.

In breast cancer in particular, nodal metastasis influences treatment decisions regarding radiation therapy, chemotherapy, and the potential surgical removal of additional lymph nodes. As such, the accuracy and timeliness of identifying nodal metastases has a significant impact on clinical care. However, studies have shown that about 1 in 4 metastatic lymph node staging classifications would be changed upon second pathologic review, and detection sensitivity of small metastases on individual slides can be as low as 38% when reviewed under time constraints.

Last year, we described our deep learning–based approach to improve diagnostic accuracy (LYmph Node Assistant, or LYNA) to the 2016 ISBI Camelyon Challenge, which provided gigapixel-sized pathology slides of lymph nodes from breast cancer patients for researchers to develop computer algorithms to detect metastatic cancer. While LYNA achieved significantly higher cancer detection rates (Liu et al. 2017) than had been previously reported, an accurate algorithm alone is insufficient to improve pathologists’ workflow or improve outcomes for breast cancer patients. For patient safety, these algorithms must be tested in a variety of settings to understand their strengths and weaknesses. Furthermore, the actual benefits to pathologists using these algorithms had not been previously explored and must be assessed to determine whether or not an algorithm actually improves efficiency or diagnostic accuracy.

In “Artificial Intelligence Based Breast Cancer Nodal Metastasis Detection: Insights into the Black Box for Pathologists” (Liu et al. 2018), published in the Archives of Pathology and Laboratory Medicine and “Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer” (Steiner, MacDonald, Liu et al. 2018) published in The American Journal of Surgical Pathology, we present a proof-of-concept pathologist assistance tool based on LYNA, and investigate these factors.

In the first paper, we applied our algorithm to de-identified pathology slides from both the Camelyon Challenge and an independent dataset provided by our co-authors at the Naval Medical Center San Diego. Because this additional dataset consisted of pathology samples from a different lab using different processes, it improved the representation of the diversity of slides and artifacts seen in routine clinical practice. LYNA proved robust to image variability and numerous histological artifacts, and achieved similar performance on both datasets without additional development.
Left: sample view of a slide containing lymph nodes, with multiple artifacts: the dark zone on the left is an air bubble, the white streaks are cutting artifacts, the red hue across some regions are hemorrhagic (containing blood), the tissue is necrotic (decaying), and the processing quality was poor. Right: LYNA identifies the tumor region in the center (red), and correctly classifies the surrounding artifact-laden regions as non-tumor (blue).
In both datasets, LYNA was able to correctly distinguish a slide with metastatic cancer from a slide without cancer 99% of the time. Further, LYNA was able to accurately pinpoint the location of both cancers and other suspicious regions within each slide, some of which were too small to be consistently detected by pathologists. As such, we reasoned that one potential benefit of LYNA could be to highlight these areas of concern for pathologists to review and determine the final diagnosis.

In our second paper, 6 board-certified pathologists completed a simulated diagnostic task in which they reviewed lymph nodes for metastatic breast cancer both with and without the assistance of LYNA. For the often laborious task of detecting small metastases (termed micrometastases), the use of LYNA made the task subjectively “easier” (according to pathologists’ self-reported diagnostic difficulty) and halved average slide review time, requiring about one minute instead of two minutes per slide.
Left: sample views of a slide containing lymph nodes with a small metastatic breast tumor at progressively higher magnifications. Right: the same views when shown with algorithmic “assistance” (LYmph Node Assistant, LYNA) outlining the tumor in cyan.
This suggests the intriguing potential for assistive technologies such as LYNA to reduce the burden of repetitive identification tasks and to allow more time and energy for pathologists to focus on other, more challenging clinical and diagnostic tasks. In terms of diagnostic accuracy, pathologists in this study were able to more reliably detect micrometastases with LYNA, reducing the rate of missed micrometastases by a factor of two. Encouragingly, pathologists with LYNA assistance were more accurate than either unassisted pathologists or the LYNA algorithm itself, suggesting that people and algorithms can work together effectively to perform better than either alone.

With these studies, we have made progress in demonstrating the robustness of our LYNA algorithm to support one component of breast cancer TNM staging, and assessing its impact in a proof-of-concept diagnostic setting. While encouraging, the bench-to-bedside journey to help doctors and patients with these types of technologies is a long one. These studies have important limitations, such as limited dataset sizes and a simulated diagnostic workflow which examined only a single lymph node slide for every patient instead of the multiple slides that are common for a complete clinical case. Further work will be needed to assess the impact of LYNA on real clinical workflows and patient outcomes. However, we remain optimistic that carefully validated deep learning technologies and well-designed clinical tools can help improve both the accuracy and availability of pathologic diagnosis around the world.

Source: Google AI Blog


Deep Learning for Electronic Health Records

Posted by Alvin Rajkomar MD, Research Scientist and Eyal Oren PhD, Product Manager, Google AI

When patients get admitted to a hospital, they have many questions about what will happen next. When will I be able to go home? Will I get better? Will I have to come back to the hospital? Having precise answers to those questions helps doctors and nurses make care better, safer, and faster — if a patient’s health is deteriorating, doctors could be sent proactively to act before things get worse.

Predicting what will happen next is a natural application of machine learning. We wondered if the same types of machine learning that predict traffic during your commute or the next word in a translation from English to Spanish could be used for clinical predictions. For predictions to be useful in practice they should be, at least:
  1. Scalable: Predictions should be straightforward to create for any important outcome and for different hospital systems. Since healthcare data is very complicated and requires much data wrangling, this requirement is not straightforward to satisfy.
  2. Accurate: Predictions should alert clinicians to problems but not distract them with false alarms. With the widespread adoption of electronic health records, we set out to use that data to create more accurate prediction models.
Together with colleagues at UC San Francisco, Stanford Medicine, and The University of Chicago Medicine, we published “Scalable and Accurate Deep Learning with Electronic Health Records” in Nature Partner Journals: Digital Medicine, which contributes to these two aims.
We used deep learning models to make a broad set of predictions relevant to hospitalized patients using de-identified electronic health records. Importantly, we were able to use the data as-is, without the laborious manual effort typically required to extract, clean, harmonize, and transform relevant variables in those records. Our partners had removed sensitive individual information before we received it, and on our side, we protected the data using state-of-the-art security including logical separation, strict access controls, and encryption of data at rest and in transit.

Scalability
Electronic health records (EHRs) are tremendously complicated. Even a temperature measurement has a different meaning depending on if it’s taken under the tongue, through your eardrum, or on your forehead. And that's just a simple vital sign. Moreover, each health system customizes their EHR system, making the data collected at one hospital look different than data on a similar patient receiving similar care at another hospital. Before we could even apply machine learning, we needed a consistent way to represent patient records, which we built on top of the open Fast Healthcare Interoperability Resources (FHIR) standard as described in an earlier blog post.

Once in a consistent format, we did not have to manually select or harmonize the variables to use. Instead, for each prediction, a deep learning model reads all the data-points from earliest to most recent and then learns which data helps predict the outcome. Since there are thousands of data points involved, we had to develop some new types of deep learning modeling approaches based on recurrent neural networks (RNNs) and feedforward networks.
Data in a patient's record is represented as a timeline. For illustrative purposes, we display various types of clinical data (e.g. encounters, lab tests) by row. Each piece of data, indicated as a little grey dot, is stored in FHIR, an open data standard that can be used by any healthcare institution. A deep learning model analyzed a patient's chart by reading the timeline from left to right, from the beginning of a chart to the current hospitalization, and used this data to make different types of predictions.
Thus we engineered a computer system to render predictions without hand-crafting a new dataset for each task, in a scalable manner. But setting up the data is only one part of the work; the predictions also need to be accurate.

Prediction Accuracy
The most common way to assess accuracy is by a measure called the area-under-the-receiver-operator curve, which measures how well a model distinguishes between a patient who will have a particular future outcome compared to one who will not. In this metric, 1.00 is perfect, and 0.50 is no better than random chance, so higher numbers mean the model is more accurate. By this measure, the models we reported in the paper scored 0.86 in predicting if patients will stay long in the hospital (traditional logistic regression scored 0.76); they scored 0.95 in predicting inpatient mortality (traditional methods were 0.86), and they scored 0.77 in predicting unexpected readmissions after patients are discharged (traditional methods were 0.70). These gains were statistically significant.

We also used these models to identify the conditions for which the patients were being treated. For example, if a doctor prescribed ceftriaxone and doxycycline for a patient with an elevated temperature, fever and cough, the model could identify these as signals that the patient was being treated for pneumonia. We emphasize that the model is not diagnosing patients — it picks up signals about the patient, their treatments and notes written by their clinicians, so the model is more like a good listener than a master diagnostician.

An important focus of our work includes the interpretability of the deep learning models used. An “attention map” of each prediction shows the important data points considered by the models as they make that prediction. We show an example as a proof-of-concept and see this as an important part of what makes predictions useful for clinicians.
A deep learning model was used to render a prediction 24 hours after a patient was admitted to the hospital. The timeline (top of figure) contains months of historical data and the most recent data is shown enlarged in the middle. The model "attended" to information highlighted in red that was in the patient's chart to "explain" its prediction. In this case-study, the model highlighted pieces of information that make sense clinically. Figure from our paper.
What does this mean for patients and clinicians?
The results of this work are early and on retrospective data only. Indeed, this paper represents just the beginning of the work that is needed to test the hypothesis that machine learning can be used to make healthcare better. Doctors are already inundated with alerts and demands on their attention — could models help physicians with tedious, administrative tasks so they can better focus on the patient in front of them or ones that need extra attention? Can we help patients get high-quality care no matter where they seek it? We look forward to collaborating with doctors and patients to figure out the answers to these questions and more.

Source: Google AI Blog


An Augmented Reality Microscope for Cancer Detection

Posted by Martin Stumpe, Technical Lead and Craig Mermel, Product Manager, Google Brain Team

Applications of deep learning to medical disciplines including ophthalmology, dermatology, radiology, and pathology have recently shown great promise to increase both the accuracy and availability of high-quality healthcare to patients around the world. At Google, we have also published results showing that a convolutional neural network is able to detect breast cancer metastases in lymph nodes at a level of accuracy comparable to a trained pathologist. However, because direct tissue visualization using a compound light microscope remains the predominant means by which a pathologist diagnoses illness, a critical barrier to the widespread adoption of deep learning in pathology is the dependence on having a digital representation of the microscopic tissue.

Today, in a talk delivered at the Annual Meeting of the American Association for Cancer Research (AACR), with an accompanying paper “An Augmented Reality Microscope for Real-time Automated Detection of Cancer” (under review), we describe a prototype Augmented Reality Microscope (ARM) platform that we believe can possibly help accelerate and democratize the adoption of deep learning tools for pathologists around the world. The platform consists of a modified light microscope that enables real-time image analysis and presentation of the results of machine learning algorithms directly into the field of view. Importantly, the ARM can be retrofitted into existing light microscopes found in hospitals and clinics around the world using low-cost, readily-available components, and without the need for whole slide digital versions of the tissue being analyzed.
Modern computational components and deep learning models, such as those built upon TensorFlow, will allow a wide range of pre-trained models to run on this platform. As in a traditional analog microscope, the user views the sample through the eyepiece. A machine learning algorithm projects its output back into the optical path in real-time. This digital projection is visually superimposed on the original (analog) image of the specimen to assist the viewer in localizing or quantifying features of interest. Importantly, the computation and visual feedback updates quickly — our present implementation runs at approximately 10 frames per second, so the model output updates seamlessly as the user scans the tissue by moving the slide and/or changing magnification.
Left: Schematic overview of the ARM. A digital camera captures the same field of view (FoV) as the user and passes the image to an attached compute unit capable of running real-time inference of a machine learning model. The results are fed back into a custom AR display which is inline with the ocular lens and projects the model output on the same plane as the slide. Right: A picture of our prototype which has been retrofitted into a typical clinical-grade light microscope.
In principle, the ARM can provide a wide variety of visual feedback, including text, arrows, contours, heatmaps, or animations, and is capable of running many types of machine learning algorithms aimed at solving different problems such as object detection, quantification, or classification.

As a demonstration of the potential utility of the ARM, we configured it to run two different cancer detection algorithms: one that detects breast cancer metastases in lymph node specimens, and another that detects prostate cancer in prostatectomy specimens. These models can run at magnifications between 4-40x, and the result of a given model is displayed by outlining detected tumor regions with a green contour. These contours help draw the pathologist’s attention to areas of interest without obscuring the underlying tumor cell appearance.
Example view through the lens of the ARM. These images show examples of the lymph node metastasis model with 4x, 10x, 20x, and 40x microscope objectives.
While both cancer models were originally trained on images from a whole slide scanner with a significantly different optical configuration, the models performed remarkably well on the ARM with no additional re-training. For example, the lymph node metastasis model had an area-under-the-curve (AUC) of 0.98 and our prostate cancer model had an AUC of 0.96 for cancer detection in the field of view (FoV) when run on the ARM, only slightly decreased performance than obtained on WSI. We believe it is likely that the performance of these models can be further improved by additional training on digital images captured directly from the ARM itself.

We believe that the ARM has potential for a large impact on global health, particularly for the diagnosis of infectious diseases, including tuberculosis and malaria, in developing countries. Furthermore, even in hospitals that will adopt a digital pathology workflow in the near future, ARM could be used in combination with the digital workflow where scanners still face major challenges or where rapid turnaround is required (e.g. cytology, fluorescent imaging, or intra-operative frozen sections). Of course, light microscopes have proven useful in many industries other than pathology, and we believe the ARM can be adapted for a broad range of applications across healthcare, life sciences research, and material science. We’re excited to continue to explore how the ARM can help accelerate the adoption of machine learning for positive impact around the world.


An Augmented Reality Microscope for Cancer Detection

Posted by Martin Stumpe, Technical Lead and Craig Mermel, Product Manager, Google Brain Team

Applications of deep learning to medical disciplines including ophthalmology, dermatology, radiology, and pathology have recently shown great promise to increase both the accuracy and availability of high-quality healthcare to patients around the world. At Google, we have also published results showing that a convolutional neural network is able to detect breast cancer metastases in lymph nodes at a level of accuracy comparable to a trained pathologist. However, because direct tissue visualization using a compound light microscope remains the predominant means by which a pathologist diagnoses illness, a critical barrier to the widespread adoption of deep learning in pathology is the dependence on having a digital representation of the microscopic tissue.

Today, in a talk delivered at the Annual Meeting of the American Association for Cancer Research (AACR), with an accompanying paper “An Augmented Reality Microscope for Real-time Automated Detection of Cancer” (under review), we describe a prototype Augmented Reality Microscope (ARM) platform that we believe can possibly help accelerate and democratize the adoption of deep learning tools for pathologists around the world. The platform consists of a modified light microscope that enables real-time image analysis and presentation of the results of machine learning algorithms directly into the field of view. Importantly, the ARM can be retrofitted into existing light microscopes found in hospitals and clinics around the world using low-cost, readily-available components, and without the need for whole slide digital versions of the tissue being analyzed.
Modern computational components and deep learning models, such as those built upon TensorFlow, will allow a wide range of pre-trained models to run on this platform. As in a traditional analog microscope, the user views the sample through the eyepiece. A machine learning algorithm projects its output back into the optical path in real-time. This digital projection is visually superimposed on the original (analog) image of the specimen to assist the viewer in localizing or quantifying features of interest. Importantly, the computation and visual feedback updates quickly — our present implementation runs at approximately 10 frames per second, so the model output updates seamlessly as the user scans the tissue by moving the slide and/or changing magnification.
Left: Schematic overview of the ARM. A digital camera captures the same field of view (FoV) as the user and passes the image to an attached compute unit capable of running real-time inference of a machine learning model. The results are fed back into a custom AR display which is inline with the ocular lens and projects the model output on the same plane as the slide. Right: A picture of our prototype which has been retrofitted into a typical clinical-grade light microscope.
In principle, the ARM can provide a wide variety of visual feedback, including text, arrows, contours, heatmaps, or animations, and is capable of running many types of machine learning algorithms aimed at solving different problems such as object detection, quantification, or classification.

As a demonstration of the potential utility of the ARM, we configured it to run two different cancer detection algorithms: one that detects breast cancer metastases in lymph node specimens, and another that detects prostate cancer in prostatectomy specimens. These models can run at magnifications between 4-40x, and the result of a given model is displayed by outlining detected tumor regions with a green contour. These contours help draw the pathologist’s attention to areas of interest without obscuring the underlying tumor cell appearance.
Example view through the lens of the ARM. These images show examples of the lymph node metastasis model with 4x, 10x, 20x, and 40x microscope objectives.
While both cancer models were originally trained on images from a whole slide scanner with a significantly different optical configuration, the models performed remarkably well on the ARM with no additional re-training. For example, the lymph node metastasis model had an area-under-the-curve (AUC) of 0.98 and our prostate cancer model had an AUC of 0.96 for cancer detection in the field of view (FoV) when run on the ARM, only slightly decreased performance than obtained on WSI. We believe it is likely that the performance of these models can be further improved by additional training on digital images captured directly from the ARM itself.

We believe that the ARM has potential for a large impact on global health, particularly for the diagnosis of infectious diseases, including tuberculosis and malaria, in developing countries. Furthermore, even in hospitals that will adopt a digital pathology workflow in the near future, ARM could be used in combination with the digital workflow where scanners still face major challenges or where rapid turnaround is required (e.g. cytology, fluorescent imaging, or intra-operative frozen sections). Of course, light microscopes have proven useful in many industries other than pathology, and we believe the ARM can be adapted for a broad range of applications across healthcare, life sciences research, and material science. We’re excited to continue to explore how the ARM can help accelerate the adoption of machine learning for positive impact around the world.


Source: Google AI Blog


Making Healthcare Data Work Better with Machine Learning

Posted by Patrik Sundberg, Software Engineer and Eyal Oren, Product Manager, Google Brain Team

Over the past 10 years, healthcare data has moved from being largely on paper to being almost completely digitized in electronic health records. But making sense of this data involves a few key challenges. First, there is no common data representation across vendors; each uses a different way to structure their data. Second, even sites that use the same vendor may differ significantly, for example, they typically use different codes for the same medication. Third, data can be spread over many tables, some containing encounters, some containing lab results, and yet others containing vital signs.

The Fast Healthcare Interoperability Resources (FHIR) standard addresses most of these challenges: it has a solid yet extensible data-model, is built on established Web standards, and is rapidly becoming the de-facto standard for both individual records and bulk-data access. But to enable large-scale machine learning, we needed a few additions: implementations in various programming languages, an efficient way to serialize large amounts of data to disk, and a representation that allows analyses of large datasets.

Today, we are happy to open source a protocol buffer implementation of the FHIR standard, which addresses these issues. The current version supports Java, and support for C++, Go, and Python will follow soon. Support for profiles will follow shortly as well, plus tools to help convert legacy data into FHIR.

FHIR as the core data model
Over the past few years, as we’ve been partnering with academic medical centers to apply machine learning to de-identified medical records, it became clear that we needed to address the complexity of healthcare data head-on. Indeed, for machine learning to be effective on medical data, we need a holistic view of what happened to each patient over time. And as a bonus, we want a data representation that is directly applicable in a clinical setting.

While the FHIR standard addresses most of our needs, making healthcare data substantially easier to manage than “legacy” data structures and enabling large-scale machine-learning independent of vendors, we believe the introduction of protocol buffers can help both application developers and (machine-learning) researchers use FHIR.

Current release of protocol buffers
We’ve taken care to make our protocol buffer representation suitable for both programmatic access and database queries. One of the provided examples shows how to upload FHIR data into Google Cloud BigQuery and have it available for querying, and we are adding other examples that upload directly from bulk data export. Our protocol buffers adhere to the FHIR standard (they are in fact auto-generated from it) but make for more elegant queries.

The current release does not yet include support for training TensorFlow models, but keep an eye out for future updates. We aim to open-source as much as possible of our recent work, to help make our research more reproducible and applicable to real-world scenarios. Furthermore, we are working closely with our colleagues in Google Cloud on more tools for managing healthcare data at scale.

Acknowledgements
We enjoyed great discussions and helpful feedback from the FHIR community, including Grahame Grieve, Ewout Kramer, Josh Mandel and others. Thanks to our colleagues at DeepMind, the Google Brain team and our academic collaborators.

Making Healthcare Data Work Better with Machine Learning

Posted by Patrik Sundberg, Software Engineer and Eyal Oren, Product Manager, Google Brain Team

Over the past 10 years, healthcare data has moved from being largely on paper to being almost completely digitized in electronic health records. But making sense of this data involves a few key challenges. First, there is no common data representation across vendors; each uses a different way to structure their data. Second, even sites that use the same vendor may differ significantly, for example, they typically use different codes for the same medication. Third, data can be spread over many tables, some containing encounters, some containing lab results, and yet others containing vital signs.

The Fast Healthcare Interoperability Resources (FHIR) standard addresses most of these challenges: it has a solid yet extensible data-model, is built on established Web standards, and is rapidly becoming the de-facto standard for both individual records and bulk-data access. But to enable large-scale machine learning, we needed a few additions: implementations in various programming languages, an efficient way to serialize large amounts of data to disk, and a representation that allows analyses of large datasets.

Today, we are happy to open source a protocol buffer implementation of the FHIR standard, which addresses these issues. The current version supports Java, and support for C++, Go, and Python will follow soon. Support for profiles will follow shortly as well, plus tools to help convert legacy data into FHIR.

FHIR as the core data model
Over the past few years, as we’ve been partnering with academic medical centers to apply machine learning to de-identified medical records, it became clear that we needed to address the complexity of healthcare data head-on. Indeed, for machine learning to be effective on medical data, we need a holistic view of what happened to each patient over time. And as a bonus, we want a data representation that is directly applicable in a clinical setting.

While the FHIR standard addresses most of our needs, making healthcare data substantially easier to manage than “legacy” data structures and enabling large-scale machine-learning independent of vendors, we believe the introduction of protocol buffers can help both application developers and (machine-learning) researchers use FHIR.

Current release of protocol buffers
We’ve taken care to make our protocol buffer representation suitable for both programmatic access and database queries. One of the provided examples shows how to upload FHIR data into Google Cloud BigQuery and have it available for querying, and we are adding other examples that upload directly from bulk data export. Our protocol buffers adhere to the FHIR standard (they are in fact auto-generated from it) but make for more elegant queries.

The current release does not yet include support for training TensorFlow models, but keep an eye out for future updates. We aim to open-source as much as possible of our recent work, to help make our research more reproducible and applicable to real-world scenarios. Furthermore, we are working closely with our colleagues in Google Cloud on more tools for managing healthcare data at scale.

Acknowledgements
We enjoyed great discussions and helpful feedback from the FHIR community, including Grahame Grieve, Ewout Kramer, Josh Mandel and others. Thanks to our colleagues at DeepMind, the Google Brain team and our academic collaborators.

Assessing Cardiovascular Risk Factors with Computer Vision

Posted by Lily Peng MD PhD, Product Manager, Google Brain Team

Heart attacks, strokes and other cardiovascular (CV) diseases continue to be among the top public health issues. Assessing this risk is critical first step toward reducing the likelihood that a patient suffers a CV event in the future. To do this assessment, doctors take into account a variety of risk factors — some genetic (like age and sex), some with lifestyle components (like smoking and blood pressure). While most of these factors can be obtained by simply asking the patient, others factors, like cholesterol, require a blood draw. Doctors also take into account whether or not a patient has another disease, such as diabetes, which is associated with significantly increased risk of CV events.

Recently, we’ve seen many examples [1–4] of how deep learning techniques can help to increase the accuracy of diagnoses for medical imaging, especially for diabetic eye disease. In “Prediction of Cardiovascular Risk Factors from Retinal Fundus Photographs via Deep Learning,” published in Nature Biomedical Engineering, we show that in addition to detecting eye disease, images of the eye can very accurately predict other indicators of CV health. This discovery is particularly exciting because it suggests we might discover even more ways to diagnose health issues from retinal images.

Using deep learning algorithms trained on data from 284,335 patients, we were able to predict CV risk factors from retinal images with surprisingly high accuracy for patients from two independent datasets of 12,026 and 999 patients. For example, our algorithm could distinguish the retinal images of a smoker from that of a non-smoker 71% of the time. In addition, while doctors can typically distinguish between the retinal images of patients with severe high blood pressure and normal patients, our algorithm could go further to predict the systolic blood pressure within 11 mmHg on average for patients overall, including those with and without high blood pressure.
LEFT: image of the back of the eye showing the macula (dark spot in the middle), optic disc (bright spot at the right), and blood vessels (dark red lines arcing out from the bright spot on the right). RIGHT: retinal image in gray, with the pixels used by the deep learning algorithm to make predictions about the blood pressure highlighted in shades of green (heatmap). We found that each CV risk factor prediction uses a distinct pattern, such as blood vessels for blood pressure, and optic disc for other predictions.
In addition to predicting the various risk factors (age, gender, smoking, blood pressure, etc) from retinal images, our algorithm was fairly accurate at predicting the risk of a CV event directly. Our algorithm used the entire image to quantify the association between the image and the risk of heart attack or stroke. Given the retinal image of one patient who (up to 5 years) later experienced a major CV event (such as a heart attack) and the image of another patient who did not, our algorithm could pick out the patient who had the CV event 70% of the time. This performance approaches the accuracy of other CV risk calculators that require a blood draw to measure cholesterol.

More importantly, we opened the “black box” by using attention techniques to look at how the algorithm was making its prediction. These techniques allow us to generate a heatmap that shows which pixels were the most important for a predicting a specific CV risk factor. For example, the algorithm paid more attention to blood vessels for making predictions about blood pressure, as shown in the image above. Explaining how the algorithm is making its prediction gives doctor more confidence in the algorithm itself. In addition, this technique could help generate hypotheses for future scientific investigations into CV risk and the retina.

At the broadest level, we are excited about this work because it may represent a new method of scientific discovery. Traditionally, medical discoveries are often made through a sophisticated form of guess and test — making hypotheses from observations and then designing and running experiments to test the hypotheses. However, with medical images, observing and quantifying associations can be difficult because of the wide variety of features, patterns, colors, values and shapes that are present in real images. Our approach uses deep learning to draw connections between changes in the human anatomy and disease, akin to how doctors learn to associate signs and symptoms with the diagnosis of a new disease. This could help scientists generate more targeted hypotheses and drive a wide range of future research.

With these promising results, a lot of scientific work remains. Our dataset had many images labeled with smoking status, systolic blood pressure, age, gender and other variables, but it only had a few hundred examples of CV events. We look forward to developing and testing our algorithm on larger and more comprehensive datasets. To make this useful for patients, we will be seeking to understand the effects of interventions such as lifestyle changes or medications on our risk predictions and we will be generating new hypotheses and theories to test.


References

[1] Gulshan, V. et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA 316, 2402–2410 (2016).

[2] Ting, D. S. W. et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA 318, 2211–2223 (2017).

[3] Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature (2017). doi:10.1038/nature21056

[4] Ehteshami Bejnordi, B. et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA 318, 2199–2210 (2017).

DeepVariant: Highly Accurate Genomes With Deep Neural Networks

Crossposted on the Google Research Blog

Across many scientific disciplines, but in particular in the field of genomics, major breakthroughs have often resulted from new technologies. From Sanger sequencing, which made it possible to sequence the human genome, to the microarray technologies that enabled the first large-scale genome-wide experiments, new instruments and tools have allowed us to look ever more deeply into the genome and apply the results broadly to health, agriculture and ecology.

One of the most transformative new technologies in genomics was high-throughput sequencing (HTS), which first became commercially available in the early 2000s. HTS allowed scientists and clinicians to produce sequencing data quickly, cheaply, and at scale. However, the output of HTS instruments is not the genome sequence for the individual being analyzed — for humans this is 3 billion paired bases (guanine, cytosine, adenine and thymine) organized into 23 pairs of chromosomes. Instead, these instruments generate ~1 billion short sequences, known as reads. Each read represents just 100 of the 3 billion bases, and per-base error rates range from 0.1-10%. Processing the HTS output into a single, accurate and complete genome sequence is a major outstanding challenge. The importance of this problem, for biomedical applications in particular, has motivated efforts such as the Genome in a Bottle Consortium (GIAB), which produces high confidence human reference genomes that can be used for validation and benchmarking, as well as the precisionFDA community challenges, which are designed to foster innovation that will improve the quality and accuracy of HTS-based genomic tests.

CAPTION: For any given location in the genome, there are multiple reads among the ~1 billion that include a base at that position. Each read is aligned to a reference, and then each of the bases in the read is compared to the base of the reference at that location. When a read includes a base that differs from the reference, it may indicate a variant (a difference in the true sequence), or it may be an error.

Today, we announce the open source release of DeepVariant, a deep learning technology to reconstruct the true genome sequence from HTS sequencer data with significantly greater accuracy than previous classical methods. This work is the product of more than two years of research by the Google Brain team, in collaboration with Verily Life Sciences. DeepVariant transforms the task of variant calling, as this reconstruction problem is known in genomics, into an image classification problem well-suited to Google's existing technology and expertise.

CAPTION: Each of the four images above is a visualization of actual sequencer reads aligned to a reference genome. A key question is how to use the reads to determine whether there is a variant on both chromosomes, on just one chromosome, or on neither chromosome. There is more than one type of variant, with SNPs and insertions/deletions being the most common. A: a true SNP on one chromosome pair, B: a deletion on one chromosome, C: a deletion on both chromosomes, D: a false variant caused by errors. It's easy to see that these look quite distinct when visualized in this manner.

We started with GIAB reference genomes, for which there is high-quality ground truth (or the closest approximation currently possible). Using multiple replicates of these genomes, we produced tens of millions of training examples in the form of multi-channel tensors encoding the HTS instrument data, and then trained a TensorFlow-based image classification model to identify the true genome sequence from the experimental data produced by the instruments. Although the resulting deep learning model, DeepVariant, had no specialized knowledge about genomics or HTS, within a year it had won the the highest SNP accuracy award at the precisionFDA Truth Challenge, outperforming state-of-the-art methods. Since then, we've further reduced the error rate by more than 50%.


DeepVariant is being released as open source software to encourage collaboration and to accelerate the use of this technology to solve real world problems. To further this goal, we partnered with Google Cloud Platform (GCP) to deploy DeepVariant workflows on GCP, available today, in configurations optimized for low-cost and fast turnarounds using scalable GCP technologies like the Pipelines API. This paired set of releases provides a smooth ramp for users to explore and evaluate the capabilities of DeepVariant in their current compute environment while providing a scalable, cloud-based solution to satisfy the needs of even the largest genomics datasets.

DeepVariant is the first of what we hope will be many contributions that leverage Google's computing infrastructure and ML expertise to both better understand the genome and to provide deep learning-based genomics tools to the community. This is all part of a broader goal to apply Google technologies to healthcare and other scientific applications, and to make the results of these efforts broadly accessible.

By Mark DePristo and Ryan Poplin, Google Brain Team

DeepVariant: Highly Accurate Genomes With Deep Neural Networks

Crossposted on the Google Research Blog

Across many scientific disciplines, but in particular in the field of genomics, major breakthroughs have often resulted from new technologies. From Sanger sequencing, which made it possible to sequence the human genome, to the microarray technologies that enabled the first large-scale genome-wide experiments, new instruments and tools have allowed us to look ever more deeply into the genome and apply the results broadly to health, agriculture and ecology.

One of the most transformative new technologies in genomics was high-throughput sequencing (HTS), which first became commercially available in the early 2000s. HTS allowed scientists and clinicians to produce sequencing data quickly, cheaply, and at scale. However, the output of HTS instruments is not the genome sequence for the individual being analyzed — for humans this is 3 billion paired bases (guanine, cytosine, adenine and thymine) organized into 23 pairs of chromosomes. Instead, these instruments generate ~1 billion short sequences, known as reads. Each read represents just 100 of the 3 billion bases, and per-base error rates range from 0.1-10%. Processing the HTS output into a single, accurate and complete genome sequence is a major outstanding challenge. The importance of this problem, for biomedical applications in particular, has motivated efforts such as the Genome in a Bottle Consortium (GIAB), which produces high confidence human reference genomes that can be used for validation and benchmarking, as well as the precisionFDA community challenges, which are designed to foster innovation that will improve the quality and accuracy of HTS-based genomic tests.

CAPTION: For any given location in the genome, there are multiple reads among the ~1 billion that include a base at that position. Each read is aligned to a reference, and then each of the bases in the read is compared to the base of the reference at that location. When a read includes a base that differs from the reference, it may indicate a variant (a difference in the true sequence), or it may be an error.

Today, we announce the open source release of DeepVariant, a deep learning technology to reconstruct the true genome sequence from HTS sequencer data with significantly greater accuracy than previous classical methods. This work is the product of more than two years of research by the Google Brain team, in collaboration with Verily Life Sciences. DeepVariant transforms the task of variant calling, as this reconstruction problem is known in genomics, into an image classification problem well-suited to Google's existing technology and expertise.

CAPTION: Each of the four images above is a visualization of actual sequencer reads aligned to a reference genome. A key question is how to use the reads to determine whether there is a variant on both chromosomes, on just one chromosome, or on neither chromosome. There is more than one type of variant, with SNPs and insertions/deletions being the most common. A: a true SNP on one chromosome pair, B: a deletion on one chromosome, C: a deletion on both chromosomes, D: a false variant caused by errors. It's easy to see that these look quite distinct when visualized in this manner.

We started with GIAB reference genomes, for which there is high-quality ground truth (or the closest approximation currently possible). Using multiple replicates of these genomes, we produced tens of millions of training examples in the form of multi-channel tensors encoding the HTS instrument data, and then trained a TensorFlow-based image classification model to identify the true genome sequence from the experimental data produced by the instruments. Although the resulting deep learning model, DeepVariant, had no specialized knowledge about genomics or HTS, within a year it had won the the highest SNP accuracy award at the precisionFDA Truth Challenge, outperforming state-of-the-art methods. Since then, we've further reduced the error rate by more than 50%.


DeepVariant is being released as open source software to encourage collaboration and to accelerate the use of this technology to solve real world problems. To further this goal, we partnered with Google Cloud Platform (GCP) to deploy DeepVariant workflows on GCP, available today, in configurations optimized for low-cost and fast turnarounds using scalable GCP technologies like the Pipelines API. This paired set of releases provides a smooth ramp for users to explore and evaluate the capabilities of DeepVariant in their current compute environment while providing a scalable, cloud-based solution to satisfy the needs of even the largest genomics datasets.

DeepVariant is the first of what we hope will be many contributions that leverage Google's computing infrastructure and ML expertise to both better understand the genome and to provide deep learning-based genomics tools to the community. This is all part of a broader goal to apply Google technologies to healthcare and other scientific applications, and to make the results of these efforts broadly accessible.

By Mark DePristo and Ryan Poplin, Google Brain Team

DeepVariant: Highly Accurate Genomes With Deep Neural Networks

Crossposted on the Google Research Blog

Across many scientific disciplines, but in particular in the field of genomics, major breakthroughs have often resulted from new technologies. From Sanger sequencing, which made it possible to sequence the human genome, to the microarray technologies that enabled the first large-scale genome-wide experiments, new instruments and tools have allowed us to look ever more deeply into the genome and apply the results broadly to health, agriculture and ecology.

One of the most transformative new technologies in genomics was high-throughput sequencing (HTS), which first became commercially available in the early 2000s. HTS allowed scientists and clinicians to produce sequencing data quickly, cheaply, and at scale. However, the output of HTS instruments is not the genome sequence for the individual being analyzed — for humans this is 3 billion paired bases (guanine, cytosine, adenine and thymine) organized into 23 pairs of chromosomes. Instead, these instruments generate ~1 billion short sequences, known as reads. Each read represents just 100 of the 3 billion bases, and per-base error rates range from 0.1-10%. Processing the HTS output into a single, accurate and complete genome sequence is a major outstanding challenge. The importance of this problem, for biomedical applications in particular, has motivated efforts such as the Genome in a Bottle Consortium (GIAB), which produces high confidence human reference genomes that can be used for validation and benchmarking, as well as the precisionFDA community challenges, which are designed to foster innovation that will improve the quality and accuracy of HTS-based genomic tests.

CAPTION: For any given location in the genome, there are multiple reads among the ~1 billion that include a base at that position. Each read is aligned to a reference, and then each of the bases in the read is compared to the base of the reference at that location. When a read includes a base that differs from the reference, it may indicate a variant (a difference in the true sequence), or it may be an error.

Today, we announce the open source release of DeepVariant, a deep learning technology to reconstruct the true genome sequence from HTS sequencer data with significantly greater accuracy than previous classical methods. This work is the product of more than two years of research by the Google Brain team, in collaboration with Verily Life Sciences. DeepVariant transforms the task of variant calling, as this reconstruction problem is known in genomics, into an image classification problem well-suited to Google's existing technology and expertise.

CAPTION: Each of the four images above is a visualization of actual sequencer reads aligned to a reference genome. A key question is how to use the reads to determine whether there is a variant on both chromosomes, on just one chromosome, or on neither chromosome. There is more than one type of variant, with SNPs and insertions/deletions being the most common. A: a true SNP on one chromosome pair, B: a deletion on one chromosome, C: a deletion on both chromosomes, D: a false variant caused by errors. It's easy to see that these look quite distinct when visualized in this manner.

We started with GIAB reference genomes, for which there is high-quality ground truth (or the closest approximation currently possible). Using multiple replicates of these genomes, we produced tens of millions of training examples in the form of multi-channel tensors encoding the HTS instrument data, and then trained a TensorFlow-based image classification model to identify the true genome sequence from the experimental data produced by the instruments. Although the resulting deep learning model, DeepVariant, had no specialized knowledge about genomics or HTS, within a year it had won the the highest SNP accuracy award at the precisionFDA Truth Challenge, outperforming state-of-the-art methods. Since then, we've further reduced the error rate by more than 50%.


DeepVariant is being released as open source software to encourage collaboration and to accelerate the use of this technology to solve real world problems. To further this goal, we partnered with Google Cloud Platform (GCP) to deploy DeepVariant workflows on GCP, available today, in configurations optimized for low-cost and fast turnarounds using scalable GCP technologies like the Pipelines API. This paired set of releases provides a smooth ramp for users to explore and evaluate the capabilities of DeepVariant in their current compute environment while providing a scalable, cloud-based solution to satisfy the needs of even the largest genomics datasets.

DeepVariant is the first of what we hope will be many contributions that leverage Google's computing infrastructure and ML expertise to both better understand the genome and to provide deep learning-based genomics tools to the community. This is all part of a broader goal to apply Google technologies to healthcare and other scientific applications, and to make the results of these efforts broadly accessible.

By Mark DePristo and Ryan Poplin, Google Brain Team