Last year we presented results demonstrating that a deep learning system (DLS) can be trained to analyze external eye photos and predict a person’s diabetic retinal disease status and elevated glycated hemoglobin (or HbA1c, a biomarker that indicates the three-month average level of blood glucose). It was previously unknown that external eye photos contained signals for these conditions. This exciting finding suggested the potential to reduce the need for specialized equipment since such photos can be captured using smartphones and other consumer devices. Encouraged by these findings, we set out to discover what other biomarkers can be found in this imaging modality.

In “A deep learning model for novel systemic biomarkers in photos of the external eye: a retrospective study”, published in Lancet Digital Health, we show that a number of systemic biomarkers spanning several organ systems (e.g., kidney, blood, liver) can be predicted from external eye photos with an accuracy surpassing that of a baseline logistic regression model that uses only clinicodemographic variables, such as age and years with diabetes. The comparison with a clinicodemographic baseline is useful because risk for some diseases could also be assessed using a simple questionnaire, and we seek to understand if the model interpreting images is doing better. This work is in the early stages, but it has the potential to increase access to disease detection and monitoring through new non-invasive care pathways.

A model generating predictions for an external eye photo.

Model development and evaluation

To develop our model, we worked with partners at EyePACS and the Los Angeles County Department of Health Services to create a retrospective de-identified dataset of external eye photos and measurements in the form of laboratory tests and vital signs (e.g., blood pressure). We filtered down to 31 lab tests and vitals that were more commonly available in this dataset and then trained a multi-task DLS with a classification “head” for each lab and vital to predict abnormalities in these measurements.

Importantly, evaluating the performance of many abnormalities in parallel can be problematic because of a higher chance of finding a spurious and erroneous result (i.e., due to the multiple comparisons problem). To mitigate this, we first evaluated the model on a portion of our development dataset. Then, we narrowed the list down to the nine most promising prediction tasks and evaluated the model on our test datasets while correcting for multiple comparisons. Specifically, these nine tasks, their associated anatomy, and their significance for associated diseases are listed in the table below.

Key results

As in our previous work, we compared our external eye model to a baseline model (a logistic regression model taking clinicodemographic variables as input) by computing the area under the receiver operator curve (AUC). The AUC ranges from 0 to 100%, with 50% indicating random performance and higher values indicating better performance. For all but one of the nine prediction tasks, our model statistically outperformed the baseline model. In terms of absolute performance, the model’s AUCs ranged from 62% to 88%. While these levels of accuracy are likely insufficient for diagnostic applications, it is in line with other initial screening tools, like mammography and pre-screening for diabetes, used to help identify individuals who may benefit from additional testing. And as a non-invasive accessible modality, taking photographs of the external eye may offer the potential to help screen and triage patients for confirmatory blood tests or other clinical follow-up.

Results on the EyePACS test set, showing AUC performance of our DLS compared to a baseline model. The variable “n” refers to the total number of datapoints, and “N” refers to the number of positives. Error bars show 95% confidence intervals computed using the DeLong method. †Indicates that the target was pre-specified as secondary analysis; all others were pre-specified as primary analysis.

The external eye photos used in both this and the prior study were collected using table top cameras that include a head rest for patient stabilization and produce high quality images with good lighting. Since image quality may be worse in other settings, we wanted to explore to what extent the DLS model is robust to quality changes, starting with image resolution. Specifically, we scaled the images in the dataset down to a range of sizes, and measured performance of the DLS when retrained to handle the downsampled images.

Below we show a selection of the results of this experiment (see the paper for more complete results). These results demonstrate that the DLS is fairly robust and, in most cases, outperforms the baseline model even if the images are scaled down to 150x150 pixels. This pixel count is under 0.1 megapixels, much smaller than the typical smartphone camera.

Conclusion and future directions

Our previous research demonstrated the promise of the external eye modality. In this work, we performed a more exhaustive search to identify the possible systemic biomarkers that can be predicted from these photos. Though these results are promising, many steps remain to determine whether technology like this can help patients in the real world. In particular, as we mention above, the imagery in our studies were collected using large tabletop cameras in a setting that controlled factors such as lighting and head positioning. Furthermore, the datasets used in this work consist primarily of patients with diabetes and did not have sufficient representation of a number of important subgroups – more focused data collection for DLS refinement and evaluation on a more general population and across subgroups will be needed before considering clinical use.

We are excited to explore how these models generalize to smartphone imagery given the potential reach and scale that this enables for the technology. To this end, we are continuing to work with our co-authors at partner institutions like Chang Gung Memorial Hospital in Taiwan, Aravind Eye Hospital in India, and EyePACS in the United States to collect datasets of imagery captured on smartphones. Our early results are promising and we look forward to sharing more in the future.

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: Boris Babenko, Ilana Traynis, Christina Chen, Preeti Singh, Akib Uddin, Jorge Cuadros, Lauren P. Daskivich, April Y. Maa, Ramasamy Kim, Eugene Yu-Chuan Kang, Yossi Matias, Greg S. Corrado, Lily Peng, Dale R. Webster, Christopher Semturs, Jonathan Krause, Avinash V Varadarajan, Naama Hammel and Yun Liu. We also thank Dave Steiner, Yuan Liu, and Michael Howell for their feedback on the manuscript; Amit Talreja for reviewing code for the paper; Elvia Figueroa and the Los Angeles County Department of Health Services Teleretinal Diabetic Retinopathy Screening program staff for data collection and program support; Andrea Limon and Nikhil Kookkiri for EyePACS data collection and support; Dr. Charles Demosthenes for extracting the data and Peter Kuzmak for getting images for the VA data. Last but not least, a special thanks to Tom Small for the animation used in this blog post.

When a patient is diagnosed with cancer, one of the most important steps is examination of the tumor under a microscope by pathologists to determine the cancer stage and to characterize the tumor. This information is central to understanding clinical prognosis (i.e., likely patient outcomes) and for determining the most appropriate treatment, such as undergoing surgery alone versus surgery plus chemotherapy. Developing machine learning (ML) tools in pathology to assist with the microscopic review represents a compelling research area with many potential applications.

Previous studies have shown that ML can accurately identify and classify tumors in pathology images and can even predict patient prognosis using known pathology features, such as the degree to which gland appearances deviate from normal. While these efforts focus on using ML to detect or quantify known features, alternative approaches offer the potential to identify novel features. The discovery of new features could in turn further improve cancer prognostication and treatment decisions for patients by extracting information that isn’t yet considered in current workflows.

Today, we’d like to share progress we’ve made over the past few years towards identifying novel features for colorectal cancer in collaboration with teams at the Medical University of Graz in Austria and the University of Milano-Bicocca (UNIMIB) in Italy. Below, we will cover several stages of the work: (1) training a model to predict prognosis from pathology images without specifying the features to use, so that it can learn what features are important; (2) probing that prognostic model using explainability techniques; and (3) identifying a novel feature and validating its association with patient prognosis. We describe this feature and evaluate its use by pathologists in our recently published paper, “Pathologist validation of a machine-learned feature for colon cancer risk stratification”. To our knowledge, this is the first demonstration that medical experts can learn new prognostic features from machine learning, a promising start for the future of this “learning from deep learning” paradigm.

Training a prognostic model to learn what features are important

One potential approach to identifying novel features is to train ML models to directly predict patient outcomes using only the images and the paired outcome data. This is in contrast to training models to predict “intermediate” human-annotated labels for known pathologic features and then using those features to predict outcomes.

Initial work by our team showed the feasibility of training models to directly predict prognosis for a variety of cancer types using the publicly available TCGA dataset. It was especially exciting to see that for some cancer types, the model's predictions were prognostic after controlling for available pathologic and clinical features. Together with collaborators from the Medical University of Graz and the Biobank Graz, we subsequently extended this work using a large de-identified colorectal cancer cohort. Interpreting these model predictions became an intriguing next step, but common interpretability techniques were challenging to apply in this context and did not provide clear insights.

Interpreting the model-learned features

To probe the features used by the prognostic model, we used a second model (trained to identify image similarity) to cluster cropped patches of the large pathology images. We then used the prognostic model to compute the average ML-predicted risk score for each cluster.

One cluster stood out for its high average risk score (associated with poor prognosis) and its distinct visual appearance. Pathologists described the images as involving high grade tumor (i.e., least-resembling normal tissue) in close proximity to adipose (fat) tissue, leading us to dub this cluster the “tumor adipose feature” (TAF); see next figure for detailed examples of this feature. Further analysis showed that the relative quantity of TAF was itself highly and independently prognostic.

A prognostic ML model was developed to predict patient survival directly from unannotated giga-pixel pathology images. A second image similarity model was used to cluster cropped patches of pathology images. The prognostic model was used to compute the average model-predicted risk score for each cluster. One cluster, dubbed the “tumor adipose feature” (TAF) stood out in terms of its high average risk score (associated with poor survival) and distinct visual appearance. Pathologists learned to identify TAF and pathologist scoring for TAF was shown to be prognostic.

Validating that the model-learned feature can be used by pathologists

These studies provided a compelling example of the potential for ML models to predict patient outcomes and a methodological approach for obtaining insights into model predictions. However, there remained the intriguing questions of whether pathologists could learn and score the feature identified by the model while maintaining demonstrable prognostic value.

In our most recent paper, we collaborated with pathologists from the UNIMIB to investigate these questions. Using example images of TAF from the previous publication to learn and understand this feature of interest, UNIMIB pathologists developed scoring guidelines for TAF. If TAF was not seen, the case was scored as “absent”, and if TAF was observed, then “unifocal”, “multifocal”, and “widespread” categories were used to indicate the relative quantity. Our study showed that pathologists could reproducibly identify the ML-derived TAF and that their scoring for TAF provided statistically significant prognostic value on an independent retrospective dataset. To our knowledge, this is the first demonstration of pathologists learning to identify and score a specific pathology feature originally identified by an ML-based approach.

Putting things in context: learning from deep learning as a paradigm

Our work is an example of people “learning from deep learning”. In traditional ML, models learn from hand-engineered features informed by existing domain knowledge. More recently, in the deep learning era, a combination of large-scale model architectures, compute, and datasets has enabled learning directly from raw data, but this is often at the expense of human interpretability. Our work couples the use of deep learning to predict patient outcomes with interpretability methods, to extract new knowledge that could be applied by pathologists. We see this process as a natural next step in the evolution of applying ML to problems in medicine and science, moving from the use of ML to distill existing human knowledge to people using ML as a tool for knowledge discovery.

Traditional ML focused on engineering features from raw data using existing human knowledge. Deep learning enables models to learn features directly from raw data at the expense of human interpretability. Coupling deep learning with interpretability methods provides an avenue for expanding the frontiers of scientific knowledge by learning from deep learning.

Acknowledgements

This work would not have been possible without the efforts of coauthors Vincenzo L'Imperio, Markus Plass, Heimo Muller, Nicolò' Tamini, Luca Gianotti, Nicola Zucchini, Robert Reihs, Greg S. Corrado, Dale R. Webster, Lily H. Peng, Po-Hsuan Cameron Chen, Marialuisa Lavitrano, David F. Steiner, Kurt Zatloukal, Fabio Pagni. We also appreciate the support from Verily Life Sciences and the Google Health Pathology teams – in particular Timo Kohlberger, Yunnan Cai, Hongwu Wang, Kunal Nagpal, Craig Mermel, Trissia Brown, Isabelle Flament-Auvigne, and Angela Lin. We also appreciate manuscript feedback from Akinori Mitani, Rory Sayres, and Michael Howell, and illustration help from Abi Jones. This work would also not have been possible without the support of Christian Guelly, Andreas Holzinger, Robert Reihs, Farah Nader, the Biobank Graz, the efforts of the slide digitization team at the Medical University Graz, the participation of the pathologists who reviewed and annotated cases during model development, and the technicians of the UNIMIB team.