Background/Purpose: Scleroderma (SSc) is a rare autoimmune fibrosing disease with high rates of morbidity and mortality. Scleroderma is usually diagnosed by rheumatologists and/or dermatologists. However, a delay in disease recognition may occur due to a delay in referral, as internists and family physicians are not familiar with the disease and its features. Despite the fact that the facial features of the disease are characteristic for scleroderma, diagnosis is usually based on other signs and symptoms (e.g., skin thickening on proximal extremities,), and confirmed with investigations such as autoantibody tests, HRCT, and endoscopies. Late presentation of SSc  pts to rheumatologists is  commonly reported1; patients with diffuse scleroderma subtype (dcSSc) generally presented to their primary health care Practitioner(HCP) after symptoms had persisted for up to 1 year. We hypothesize that facial features of SSc patients are distinctive and can be detected by a trained AI system after processing a mobile phone picture of SSc patient’s face through Convolutional Neural Networks (CNN). This system could be used by family practioner and internists,  aiding them  to increase suspicion of SSc and refer patients in a timely manner.In a pilot study,   we aim to examine the ability of an AI facial recognition system to identify SSc related facial features

Methods: Images of 60 SSc pts from the internet were compared to a group of age and sex matched normal faces . Artificial intelligence (AI)   algorithms evaluated all the  pixels in the facial map and identified their   utility in the facial recognition prediction models. using the Viceron company AI system. This is a well-established and experienced algorithm for AI facial feature recognition based in Denmark and has been used for facial recognition in  > 1 million  general public faces  

Results: AI evaluated multiple layers of mathematical models, either isolated or pooled to generate a predictive model and eliminate unnecessary data. Smoothing and uniformity protocols were established for the obtained through preprocessing for the Convolutional Neural Networks (CNN).Multiple AI models  were used as a training set in the first 40 patients and as a partial validation step in the other 20 pts.

We developed multiple models among the 60 SSc facial images and matched controls that were  able to identify SSc distinctive facial features with variable  specificity and sensitivity. The  best model, pre-trained on the development set  and fine-tuned on the training set, achieved 80-90% accuracy on the respective datasets.

However, AI programs, when used on small numbers of patients, develop solutions tailored to the individual patients in the examined data set (termed “over fitted”). A more realistic estimate of accuracy can be derived when estimated for a larger data set, allowing for more generalized facial recognition. The latter is planned, given the initial reasonable success from the pilot study

Conclusion: Automated preprocessing and the application of AI algorithms in SSc face identification,   gave encouraging pilot results(80-90% accuracy), encouraging further testing to develop a larger protocol to establish the effects of race/ethnicity, sex, age, disease duration, disease activity. 

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/a-novel-role-for-artificial-intelligence-in-detecting-distinctive-scleroderma-facial-features/