Please use this identifier to cite or link to this item: http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/8338
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dc.contributor.authorPhatak, Sanaten_US
dc.contributor.authorCHAKRABORTY, SOMASHREEen_US
dc.contributor.authorGOEL, PRANAYen_US
dc.date.accessioned2023-12-19T11:01:31Z
dc.date.available2023-12-19T11:01:31Z
dc.date.issued2023-11en_US
dc.identifier.citationFrontiers in Medicine, 10.en_US
dc.identifier.issn2296-858Xen_US
dc.identifier.urihttps://doi.org/10.3389/fmed.2023.1280462en_US
dc.identifier.urihttp://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/8338
dc.description.abstractIntroduction: Computer vision extracts meaning from pixelated images and holds promise in automating various clinical tasks. Convolutional neural networks (CNNs), a deep learning network used therein, have shown promise in analyzing X-ray images and joint photographs. We studied the performance of a CNN on standardized smartphone photographs in detecting inflammation in three hand joints and compared it to a rheumatologist’s diagnosis.Methods: We enrolled 100 consecutive patients with inflammatory arthritis with an onset period of less than 2 years, excluding 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, anonymized, and cropped to include joints of interest. A ResNet-101 backbone modified for two class outputs (inflamed or not) was used for training. We also tested a hue-augmented dataset. We reported accuracy, sensitivity, and specificity for three joints: wrist, index finger proximal interphalangeal (IFPIP), and middle finger proximal interphalangeal (MFPIP), taking the rheumatologist’s opinion as the gold standard.Results: The cohort consisted of 100 individuals, of which 22 of them were men, with a mean age of 49.7 (SD 12.9) years. The majority of the cohort (n = 68, 68%) had rheumatoid arthritis. The wrist (125/200, 62.5%), MFPIP (94/200, 47%), and IFPIP (83/200, 41.5%) were the three most commonly inflamed joints. The CNN achieved the highest accuracy, sensitivity, and specificity in detecting synovitis in the MFPIP (83, 77, and 88%, respectively), followed by the IFPIP (74, 74, and 75%, respectively) and the wrist (62, 90, and 21%, respectively).Discussion: We have demonstrated 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.en_US
dc.language.isoenen_US
dc.publisherFrontiers Media S.A.en_US
dc.subjectArtificial intelligenceen_US
dc.subjectInflammatory arthritisen_US
dc.subjectDigital healthen_US
dc.subjectComputer visionen_US
dc.subjectScreeningen_US
dc.subject2023-DEC-WEEK1en_US
dc.subjectTOC-DEC-2023en_US
dc.subject2023en_US
dc.titleComputer vision detects inflammatory arthritis in standardized smartphone photographs in an Indian patient cohorten_US
dc.typeArticleen_US
dc.contributor.departmentDept. of Biologyen_US
dc.identifier.sourcetitleFrontiers in Medicineen_US
dc.publication.originofpublisherForeignen_US
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