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Previously, we demonstrated the potential of neural network-based digital pathology applied to SSc skin biopsies as a quantitative outcome. Here, we leverage deep learning and histologic analyses of clinical trial biopsies to decipher SSc skin features \u2018seen\u2019 by artificial intelligence (AI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Adults with diffuse cutaneous SSc\u2009\u2264\u20096\u00a0years were enrolled in an open-label trial of belumosudil [a Rho-associated coiled-coil containing protein kinase 2 (ROCK2) inhibitor]. Participants underwent serial mRSS and arm biopsies at week (W) 0, 24 and 52. Two blinded dermatopathologists scored stained sections (e.g., Masson\u2019s trichrome, hematoxylin and eosin, CD3, \u03b1-smooth muscle actin) for 16 published SSc dermal pathological parameters. We applied our deep learning model to generate QIF signatures\/biopsy and obtain \u2018Fibrosis Scores\u2019. Associations between Fibrosis Score and mRSS (Spearman correlation), and between Fibrosis Score and mRSS versus histologic parameters [odds ratios (OR)], were determined.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Only ten patients were enrolled due to early study termination, and of those, five had available biopsies due to fixation issues. Median, interquartile range (IQR) for mRSS change (0\u201352 W) for the ten participants was -2 (-9\u20147.5) and for the five with biopsies was -2.5 (-11\u20147.5). The correlation between Fibrosis Score and mRSS was R\u2009=\u20090.3;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.674. Per 1-unit mRSS change (0\u201352 W), histologic parameters with the greatest associated changes were (OR, 95% CI,\n                      <jats:italic>p<\/jats:italic>\n                      -value): telangiectasia (2.01, [(1.31\u20143.07], 0.001), perivascular CD3\u2009+\u2009(0.99, [0.97\u20141.02], 0.015), and % of CD8\u2009+\u2009among CD3\u2009+\u2009(0.95, [0.89\u20141.01], 0.031). Likewise, per 1-unit Fibrosis Score change, parameters with greatest changes were (OR,\n                      <jats:italic>p<\/jats:italic>\n                      -value): hyalinized collagen (1.1, [1.04 \u2013 1.16],\u2009&lt;\u20090.001), subcutaneous (SC) fat loss (1.47, [1.19\u20141.81],\u2009&lt;\u20090.001), thickened intima (1.21, [1.06\u20141.38], 0.005), and eccrine entrapment (1.14, [1\u20141.31], 0.046).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Belumosudil was associated with non-clinically meaningful mRSS improvement. The histologic features that significantly correlated with Fibrosis Score changes (\n                      <jats:italic>e.g.,<\/jats:italic>\n                      hyalinized collagen, SC fat loss) were distinct from those associated with mRSS changes (\n                      <jats:italic>e.g.,<\/jats:italic>\n                      telangiectasia and perivascular CD3\u2009+). These data suggest that AI applied to SSc biopsies may be useful for quantifying pathologic features of SSc beyond skin thickness.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13075-025-03508-9","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T04:56:26Z","timestamp":1744347386000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Neural network analysis as a novel skin outcome in a trial of belumosudil in patients with systemic sclerosis"],"prefix":"10.1186","volume":"27","author":[{"given":"Ilayda","family":"Gunes","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elana J.","family":"Bernstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shawn E.","family":"Cowper","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gauri","family":"Panse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niki","family":"Pradhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucy Duran","family":"Camacho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Page","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth","family":"Bundschuh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alyssa","family":"Williams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary","family":"Carns","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathleen","family":"Aren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah","family":"Fantus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth R.","family":"Volkmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heather","family":"Bukiri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chase","family":"Correia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijaya B.","family":"Kolachalama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"F. 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