Posted by Yuan Liu, PhD, Software Engineer and Peggy Bui, MD, Technical Program Manager, Google Health

An estimated 1.9 billion people worldwide suffer from a skin condition at any given time, and due to a shortage of dermatologists, many cases are seen by general practitioners instead. In the United States alone, up to 37% of patients seen in the clinic have at least one skin complaint and more than half of those patients are seen by non-dermatologists. However, studies demonstrate a significant gap in the accuracy of skin condition diagnoses between general practitioners and dermatologists, with the accuracy of general practitioners between 24% and 70%, compared to 77-96% for dermatologists. This can lead to suboptimal referrals, delays in care, and errors in diagnosis and treatment.

Existing strategies for non-dermatologists to improve diagnostic accuracy include the use of reference textbooks, online resources, and consultation with a colleague. Machine learning tools have also been developed with the aim of helping to improve diagnostic accuracy. Previous research has largely focused on early screening of skin cancer, in particular, whether a lesion is malignant or benign, or whether a lesion is melanoma. However, upwards of 90% of skin problems are not malignant, and addressing these more common conditions is also important to reduce the global burden of skin disease.

In “A Deep Learning System for Differential Diagnosis of Skin Diseases,” we developed a deep learning system (DLS) to address the most common skin conditions seen in primary care. Our results showed that a DLS can achieve an accuracy across 26 skin conditions that is on par with U.S. board-certified dermatologists, when presented with identical information about a patient case (images and metadata). This study highlights the potential of the DLS to augment the ability of general practitioners who did not have additional specialty training to accurately diagnose skin conditions.

DLS Design
Clinicians often face ambiguous cases for which there is no clear cut answer. For example, is this patient’s rash stasis dermatitis or cellulitis, or perhaps both superimposed? Rather than giving just one diagnosis, clinicians generate a differential diagnosis, which is a ranked list of possible diagnoses. A differential diagnosis frames the problem so that additional workup (laboratory tests, imaging, procedures, consultations) and treatments can be systematically applied until a diagnosis is confirmed. As such, a deep learning system (DLS) that produces a ranked list of possible skin conditions for a skin complaint closely mimics how clinicians think and is key to prompt triage, diagnosis and treatment for patients.

To render this prediction, the DLS processes inputs, including one or more clinical images of the skin abnormality and up to 45 types of metadata (self-reported components of the medical history such as age, sex, symptoms, etc.). For each case, multiple images were processed using the Inception-v4 neural network architecture and combined with feature-transformed metadata, for use in the classification layer. In our study, we developed and evaluated the DLS with 17,777 de-identified cases that were primarily referred from primary care clinics to a teledermatology service. Data from 2010-2017 were used for training and data from 2017-2018 for evaluation. During model training, the DLS leveraged over 50,000 differential diagnoses provided by over 40 dermatologists.

To evaluate the DLS’s accuracy, we compared it to a rigorous reference standard based on the diagnoses from three U.S. board-certified dermatologists. In total, dermatologists provided differential diagnoses for 3,756 cases (“Validation set A”), and these diagnoses were aggregated via a voting process to derive the ground truth labels. The DLS’s ranked list of skin conditions was compared with this dermatologist-derived differential diagnosis, achieving 71% and 93% top-1 and top-3 accuracies, respectively.

Schematic of the DLS and how the reference standard (ground truth) was derived via the voting of three board-certified dermatologists for each case in the validation set.

Comparison to Professional Evaluations
In this study, we also compared the accuracy of the DLS to that of three categories of clinicians on a subset of the validation A dataset (“Validation set B”): dermatologists, primary care physicians (PCPs), and nurse practitioners (NPs) — all chosen randomly and representing a range of experience, training, and diagnostic accuracy. Because typical differential diagnoses provided by clinicians only contain up to three diagnoses, we compared only the top three predictions by the DLS with the clinicians. The DLS achieved a top-3 diagnostic accuracy of 90% on the validation B dataset, which was comparable to dermatologists and substantially higher than primary care physicians (PCPs) and nurse practitioners (NPs)—75%, 60%, and 55%, respectively, for the 6 clinicians in each group. This high top-3 accuracy suggests that the DLS may help prompt clinicians (including dermatologists) to consider possibilities that were not originally in their differential diagnoses, thus improving diagnostic accuracy and condition management.

The DLS’s leading (top-1) differential diagnosis is substantially higher than PCPs and NPs, and on par with dermatologists. This accuracy increases substantially when we look at the DLS’s top-3 accuracy, suggesting that in the majority of cases the DLS’s ranked list of diagnoses contains the correct ground truth answer for the case.

Assessing Demographic Performance
Skin type, in particular, is highly relevant to dermatology, where visual assessment of the skin itself is crucial to diagnosis. To evaluate potential bias towards skin type, we examined DLS performance based on the Fitzpatrick skin type, which is a scale that ranges from Type I (“pale white, always burns, never tans”) to Type VI (“darkest brown, never burns”). To ensure sufficient numbers of cases on which to draw convincing conclusions, we focused on skin types that represented at least 5% of the data — Fitzpatrick skin types II through IV. On these categories, the DLS’s accuracy was similar, with a top-1 accuracy ranging from 69-72%, and the top-3 accuracy from 91-94%. Encouragingly, the DLS also remained accurate in patient subgroups for which significant numbers (at least 5%) were present in the dataset based on other self-reported demographic information: age, sex, and race/ethnicities. As further qualitative analysis, we assessed via saliency (explanation) techniques that the DLS was reassuringly “focusing” on the abnormalities instead of on skin tone.

Left: An example of a case with hair loss that was challenging for non-specialists to arrive at the specific diagnosis, which is necessary for determining appropriate treatment. Right: An image with regions highlighted in green showing the areas that the DLS identified as important and used to make its prediction. Center: The combined image, which indicates that the DLS mostly focused on the area with hair loss to make this prediction, instead of on forehead skin color, for example, which may indicate potential bias.

Incorporating Multiple Data Types
We also studied the effect of different types of input data on the DLS performance. Much like how having images from several angles can help a teledermatologist more accurately diagnose a skin condition, the accuracy of the DLS improves with increasing number of images. If metadata (e.g., the medical history) is missing, the model does not perform as well. This accuracy gap, which may occur in scenarios where no medical history is available, can be partially mitigated by training the DLS with only images. Nevertheless, this data suggests that providing the answers to a few questions about the skin condition can substantially improve the DLS accuracy.

The DLS performance improves when more images (blue line) or metadata (blue compared with red line) are present. In the absence of metadata as input, training a separate DLS using images alone leads to a marginal improvement compared to the current DLS (green line).

Future Work and Applications
Though these results are very promising, much work remains ahead. First, as reflective of real-world practice, the relative rarity of skin cancer such as melanoma in our dataset hindered our ability to train an accurate system to detect cancer. Related to this, the skin cancer labels in our dataset were not biopsy-proven, limiting the quality of the ground truth in this regard. Second, while our dataset did contain a variety of Fitzpatrick skin types, some skin types were too rare in this dataset to allow meaningful training or analysis. Finally, the validation dataset was from one teledermatology service. Though 17 primary care locations across two states were included, additional validation on cases from a wider geographical region will be critical. We believe these limitations can be addressed by including more cases of biopsy-proven skin cancers in the training and validation sets, and including cases representative of additional Fitzpatrick skin types and from other clinical centers.

The success of deep learning to inform the differential diagnosis of skin disease is highly encouraging of such a tool’s potential to assist clinicians. For example, such a DLS could help triage cases to guide prioritization for clinical care or could help non-dermatologists initiate dermatologic care more accurately and potentially improve access. Though significant work remains, we are excited for future efforts in examining the usefulness of such a system for clinicians. For research collaboration inquiries, please contact [email protected].

Acknowledgements
This work involved the efforts of a multidisciplinary team of software engineers, researchers, clinicians and cross functional contributors. Key contributors to this project include Yuan Liu, Ayush Jain, Clara Eng, David H. Way, Kang Lee, Peggy Bui, Kimberly Kanada, Guilherme de Oliveira Marinho, Jessica Gallegos, Sara Gabriele, Vishakha Gupta, Nalini Singh, Vivek Natarajan, Rainer Hofmann-Wellenhof, Greg S. Corrado, Lily H. Peng, Dale R. Webster, Dennis Ai, Susan Huang, Yun Liu, R. Carter Dunn and David Coz. The authors would like to acknowledge William Chen, Jessica Yoshimi, Xiang Ji and Quang Duong for software infrastructure support for data collection. Thanks also go to Genevieve Foti, Ken Su, T Saensuksopa, Devon Wang, Yi Gao and Linh Tran. Last but not least, this work would not have been possible without the participation of the dermatologists, primary care physicians, nurse practitioners who reviewed cases for this study, Sabina Bis who helped to establish the skin condition mapping and Amy Paller who provided feedback on the manuscript.

Posted by Rory Sayres PhD and Jonathan Krause PhD, Google AI, Healthcare

Two years ago, we announced our inaugural work in training deep learning models for diabetic retinopathy (DR), a complication of diabetes that is one of the fasting growing causes of vision loss. Based on this research, we set out to apply our technology to improve health outcomes in the world. At the same time, we’ve continued our efforts to improve the model’s performance, explainability, and applicability in clinical settings. Today, we are sharing our research progress toward these goals, as well as announcing a new partner in Thailand.

Improving Model Performance with High-quality Labels
The performance of DR deep learning models is critically important, especially when subtle errors have the potential to generate a misdiagnosis. Earlier this year we published a paper in the journal Ophthalmology that looked at how we could improve our model by 1) moving toward a more granular 5-point grading scale (versus the previous 2-class system) and 2) incorporating adjudication by a panel of retinal specialists. During the adjudication process, a group of retinal specialists debated any case with disagreement until everyone agreed on the final grade. Compared to simply taking a majority vote, this method of resolving disagreements was more accurate and allowed for the identification of subtle findings, such as microaneurysms.

To increase the efficiency of the adjudication process, we carefully selected a small subset (0.22%) of images to use as a tuning set, substantially improving model performance by optimizing model hyperparameters on this more accurate reference standard. When we subsequently measured the rate of agreement against a test set of images with an adjudicated reference standard, the kappa scores (a measurement of agreement that ranges from 0 [random] to 1 [perfect agreement]) for individual retinal specialists, ophthalmologists, and the algorithm ranged from 0.82-0.91, 0.80-0.84, and 0.84, respectively.

Making our Models More Transparent
As we deploy this technology, it is important that we take the proper steps to ensure that it is transparent and trusted. To that end, we have been exploring ways to explain how the model is making its predictions, with the goal of making the DR model a better diagnostic tool and aid for doctors.

In our latest study, to be published today in Ophthalmology, we demonstrate methods by which explanations of deep learning algorithms can be shown to ophthalmologists to increase both the accuracy and confidence of their grading for diabetic eye disease. Using the results of the model trained and validated on high quality labels from our earlier study, we generated different forms of potential assistance for general ophthalmologists. We presented to the physicians the algorithm’s predicted scores for different DR severity levels as well as heatmaps highlighting image regions that most strongly drove its predictions. Using this assistance, we saw a significant increase in physicians’ diagnostic accuracy, as well as improved confidence in their diagnosis.

We saw clear evidence that showing model predictions could help physicians catch pathology they otherwise might have missed. In the retinal image below, our adjudication panel found signs of vision-threatening DR. This was missed by 2 of 3 doctors who graded it without assistance; but caught by all 3 doctors who graded it when they saw the model predictions (which accurately detected the pathology).

On the left is a fundus image graded as having proliferative (vision-threatening) DR by an adjudication panel of ophthalmologists (ground truth). On the top right is an illustration of our deep learning model’s predicted scores (“P” = proliferative, the most severe form of DR). On the bottom right is the set of grades given by physicians without assistance (“Unassisted”) and those who saw the model’s predictions (“Grades Only”).

We also saw evidence that physicians and the model can work together in a way that provides more accuracy than either individually. In the retinal image below, our adjudication panel of retina specialists considered it to have moderate DR. Without assistance, two out of three ophthalmologists grading the image marked it as no DR. In real-world settings, this situation could result in a patient missing a needed referral to a specialist.

On the left is a retinal fundus image graded as having moderate DR (“Mo”) by an adjudication panel of ophthalmologists (ground truth). On the top right is an illustration of the predicted scores (“N” = no DR, “Mi” = Mild DR, “Mo” = Moderate DR) from the model. On the bottom right is the set of scores given by physicians without assistance (“Unassisted”) and those who saw the model’s predictions (“Grades Only”).

In this particular case, our model also indicated evidence for no DR. However, when ophthalmologists saw the model’s predictions, all three gave the correct answer. Seeing that the model saw some evidence for Moderate -- even if it wasn’t the highest score -- may prompt doctors to examine particular cases more carefully for pathology they may otherwise miss. We are excited to develop assistance that works like this, where human and machine learning abilities complement each other.

A New Partner in our Global Efforts
With the help of screening programs and in collaboration with Verily, we have laid a robust foundation for the implementation of these highly accurate systems in real world clinical settings. Working with doctors at Aravind Eye Hospitals and Sankara Nethralaya in India, and now, through our new partnership with the Rajavithi Hospital, affiliated with the Department of Medical Services, Ministry of Public Health in Thailand, we are validating the model performance with patients from broad screening programs. Given the positive results of our model on their real patient population, we are now beginning to pilot the model in their screening programs. We’re looking forward to a very busy 2019!

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

Approximately 1 in 9 men in the United States will develop prostate cancer in their lifetime, making it the most common cancer in males. Despite being common, prostate cancers are frequently non-aggressive, making it challenging to determine if the cancer poses a significant enough risk to the patient to warrant treatment such as surgical removal of the prostate (prostatectomy) or radiation therapy. A key factor that helps in the “risk stratification” of prostate cancer patients is the Gleason grade, which classifies the cancer cells based on how closely they resemble normal prostate glands when viewed on a slide under a microscope.

However, despite its widely recognized clinical importance, Gleason grading of prostate cancer is complex and subjective, as evidenced by studies reporting inter-pathologist disagreements ranging from 30-53% [1][2]. Furthermore, there are not enough speciality trained pathologists to meet the global demand for prostate cancer pathology, especially outside the United States. Recent guidelines also recommend that pathologists report the percentage of tumor of different Gleason patterns in their final report, which adds to the workload and is yet another subjective challenge for the pathologist [3]. Overall, these issues suggest an opportunity to improve the diagnosis and clinical management of prostate cancer using deep learning–based models, similar to how Google and others used such techniques to demonstrate the potential to improve metastatic breast cancer detection.

In “Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer”, we explore whether deep learning could improve the accuracy and objectivity of Gleason grading of prostate cancer in prostatectomy specimens. We developed a deep learning system (DLS) that mirrors a pathologist’s workflow by first categorizing each region in a slide into a Gleason pattern, with lower patterns corresponding to tumors that more closely resemble normal prostate glands. The DLS then summarizes an overall Gleason grade group based on the two most common Gleason patterns present. The higher the grade group, the greater the risk of further cancer progression and the more likely the patient is to benefit from treatment.

Visual examples of Gleason patterns, which are used in the Gleason system for grading prostate cancer. Individual cancer patches are assigned a Gleason pattern based on how closely the cancer resembles normal prostate tissue, with lower numbers corresponding to more well differentiated tumors. Image Source: National Institutes of Health.

To develop and validate the DLS, we collected de-identified images of prostatectomy samples which contain a greater amount and diversity of prostate cancer than needle core biopsies, even though the latter is the more common clinical procedure. On the training data, a cohort of 32 pathologists provided detailed annotations of Gleason patterns (resulting in over 112 million annotated image patches) and an overall Gleason grade group for each image. To overcome the previously referenced variability in Gleason grading, each slide in the validation set was independently graded by 3 to 5 general pathologists (selected from a cohort of 29 pathologists) and had a final Gleason grade assigned by a genitourinary-specialist pathologist to obtain the ground-truth label for that slide.

In the paper, we show that our DLS achieved an overall accuracy of 70%, compared to an average accuracy of 61% achieved by US board-certified general pathologists in our study. Of 10 high-performing individual general pathologists who graded every slide in the validation set, the DLS was more accurate than 8. The DLS was also more accurate than the average pathologist at Gleason pattern quantitation. These improvements in Gleason grading translated into better clinical risk stratification: the DLS better identified patients at higher risk for disease recurrence after surgery than the average general pathologist, potentially enabling doctors to use this information to better match patients to therapy.

Comparison of scoring performance of the DLS with pathologists. a: Accuracy of the DLS (in red) compared with the mean accuracy among a cohort-of-29 pathologists (in green). Error bars indicate 95% confidence intervals. b: Comparison of risk stratification provided by the DLS, the cohort-of-29 pathologists, and the genitourinary specialist pathologists. Patients are divided into low and high risk groups based on their Gleason grade group, where a larger separation between the Kaplan-Meier curves of these risk groups indicates better stratification.

We also found that the DLS was able to characterize tissue morphology that appeared to lie at the cusp of two Gleason patterns, which is one reason for the disagreements in Gleason grading observed between pathologists, suggesting the possibility of creating finer grained “precision grading” of prostate cancer. While the clinical significance of these intermediate patterns (e.g. Gleason pattern 3.3 or 3.7) is not known, the increased precision of the DLS will enable further research into this interesting question.

Assessing the region-level classification of the DLS. a: Annotations from 3 pathologists compared to DLS predictions. The pathologists show general concordance on the location and the extent of tumor areas, but poor agreement in classifying Gleason patterns. The DLS’s precision Gleason pattern for each region is represented by interpolating between the DLS’s prediction patterns for Gleason patterns 3 (green), 4 (yellow), and 5 (red). b: DLS prediction patterns compared to the distribution of pathologists’ Gleason pattern classifications on 41 million annotated image patches from the test dataset. On patches where pathologists are discordant, where the tissue is more likely to be on the cusp of two patterns, the DLS reflects this ambiguity in it's prediction scores.

While these initial results are encouraging, there is much more work to be done before systems like our DLS can be used to improve the care of prostate cancer patients. First, the accuracy of the model can be further improved with additional training data and should be validated on independent cohorts containing a larger number and more diverse group of patients. In addition, we are actively working on refining our DLS system to work on diagnostic needle core biopsies, which occur prior to the decision to undergo surgery and where Gleason grading therefore has a significantly greater impact on clinical decision-making. Further work will be needed to assess how to best integrate our DLS into the pathologist’s diagnostic workflow and the impact of such artificial-intelligence based assistance on the overall efficiency, accuracy, and prognostic ability of Gleason grading in clinical practice. Nonetheless, we are excited about the potential of technologies like this to significantly improve cancer diagnostics and patient care.

Acknowledgements
This work involved the efforts of a multidisciplinary team of software engineers, researchers, clinicians and logistics support staff. Key contributors to this project include Kunal Nagpal, Davis Foote, Yun Liu, Po-Hsuan (Cameron) Chen, Ellery Wulczyn, Fraser Tan, Niels Olson, Jenny L. Smith, Arash Mohtashamian, James H. Wren, Greg S. Corrado, Robert MacDonald, Lily H. Peng, Mahul B. Amin, Andrew J. Evans, Ankur R. Sangoi, Craig H. Mermel, Jason D. Hipp and Martin C. Stumpe. We would also like to thank Tim Hesterberg, Michael Howell, David Miller, Alvin Rajkomar, Benny Ayalew, Robert Nagle, Melissa Moran, Krishna Gadepalli, Aleksey Boyko, and Christopher Gammage. Lastly, this work would not have been possible without the aid of the pathologists who annotated data for this study.

References

1. Interobserver Variability in Histologic Evaluation of Radical Prostatectomy Between Central and Local Pathologists: Findings of TAX 3501 Multinational Clinical Trial, Netto, G. J., Eisenberger, M., Epstein, J. I. & TAX 3501 Trial Investigators, Urology 77, 1155–1160 (2011).
2. Phase 3 Study of Adjuvant Radiotherapy Versus Wait and See in pT3 Prostate Cancer: Impact of Pathology Review on Analysis, Bottke, D., Golz, R., Störkel, S., Hinke, A., Siegmann, A., Hertle, L., Miller, K., Hinkelbein, W., Wiegel, T., Eur. Urol. 64, 193–198 (2013).
3. Utility of Quantitative Gleason Grading in Prostate Biopsies and Prostatectomy Specimens, Sauter, G. Steurer, S., Clauditz, T. S., Krech, T., Wittmer, C., Lutz, F., Lennartz, M., Janssen, T., Hakimi, N., Simon, R., von Petersdorff-Campen, M., Jacobsen, F., von Loga, K., Wilczak, W., Minner, S., Tsourlakis, M. C., Chirico, V., Haese, A., Heinzer, H., Beyer, B., Graefen, M., Michl, U., Salomon, G., Steuber, T., Budäus, L. H., Hekeler, E., Malsy-Mink, J., Kutzera, S., Fraune, C., Göbel, C., Huland, H., Schlomm, T., Clinical Eur. Urol. 69, 592–598 (2016).