Introduction: Computer vision extracts meaning from pixelated images and holds promise in automating clinical tasks. Convolutional neural networks (CNN), deep learning networks used therein, have shown promise in X-ray images as well as joint photographs. We studied the performance of a CNN on standardized smartphone photographs in detecting inflammation in three hand joints. Methods: We enrolled consecutive patients with inflammatory arthritis of less than two years duration and excluded those with deformities. Each patient was examined by a rheumatologist and the presence of synovitis in each joint was recorded. Hand photographs were taken in a standardized manner and anonymized. Images were cropped to include joints of interest. A reNrt-101 backbone modified for two class outputs (inflamed or not) was used for training. We also tested a hue augmented dataset. We report accuracy, sensitivity and specificity for three joints: wrist, index finger proximal interphalangeal (IFPIP), middle finger interphalangeal (MFPIP). Results: The cohort had a mean age of 49.7 years; most had rheumatoid arthritis(n=68). The wrist (62.5%), MFPIP (47%) and IFPIP (41.5%) were the three most commonly inflamed joints. The CNN achieved the highest accuracy in being able to detect synovitis in the MFPIP (83%) followed by the IFPIP (74%) and the wrist (65%). Discussion: We show that computer vision was able to detect inflammation in three joints of the hand with reasonable accuracy on standardized photographs despite a small dataset. Feature engineering was not required, and the CNN worked despite a diversity in clinical diagnosis. Larger datasets are likely to improve accuracy and help explain the basis of classification. These data suggest a potential use of computer vision in screening and follow-up of inflammatory arthritis.

SP and PG are co-inventors on a provisional patent application at the Indian Patent Office that includes some material used in this paper

This study did not receive any funding

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Ethics committee of KEM Hospital Research Centre gave ethical approval for this work.(KEMHRCEC/2018) Ethics committee of Indian Institute of Science Education and Research waived ethical approval for this work.

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