Skin conditions are the fourth-largest cause of nonfatal disease worldwide, but access to dermatological care is sparse. A new study shows that a neural network can do front-line diagnostic work.
What’s new: Researchers at Google Health, UCSF, MIT, and the Medical University of Graz trained a model to examine patient records and predict the likelihood of 26 common skin diseases. The researchers believe that their system could improve the diagnostic performance of primary-care centers for skin disease.
Key insight: The system is designed to mimic the typical diagnostic process in a teledermatology setting. It accepts a patient’s medical history and up to six images, and returns a differential diagnosis, or a ranked list of likely diagnoses.
How it works: Yuan Liu and her colleagues collected anonymized patient histories and images from a dermatology service serving 17 sites across two U.S. states. They trained on data collected a few years ago, and they tested on data generated more recently to approximate real-world conditions. The system includes:
- A separate Inception-v4 convolutional neural network for each patient image, all of which used the same weights.
- A module that converts patient metadata into a consistent format via predefined rules. A fully connected layer combines images and metadata to predict the probability of a particular disease or an “other” class.
Results: The model classified diseases more accurately than primary care physicians and nurse practitioners. Allowed three guesses, it was more accurate than dermatologists by 10 percent. The system proved robust to skin color and type and its performance remained consistent across variations.
Why it matters: Most previous models consider only a single image and classify a single disease. Inspired by current medical practices, this model uses a variable number of input images, makes use of non-visual patient information as well, and classifies a variety of conditions. The research also shows how to establish model robustness by comparing performance across characteristics like skin color, age, and sex.
Yes, but: This study drew data from a limited geographic area. It remains to be seen whether the results generalize to other regions or whether such systems need to be trained or fine-tuned to account for specific geographic areas.
We’re thinking: Computer vision has been making great progress in dermatology. Still, there are many difficult steps between encouraging results and deployment in clinical settings.