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Opt. Express"],"published-print":{"date-parts":[[2022,6,1]]},"abstract":"<jats:p>Dynamic contrast-enhanced fluorescence imaging (DCE-FI) classification of tissue viability in twelve adult patients undergoing below knee leg amputation is presented. During amputation and with the distal bone exposed, indocyanine green contrast-enhanced images were acquired sequentially during baseline, following transverse osteotomy and following periosteal stripping, offering a uniquely well-controlled fluorescence dataset. An unsupervised classification machine leveraging 21 different spatiotemporal features was trained and evaluated by cross-validation in 3.5 million regions-of-interest obtained from 9 patients, demonstrating accurate stratification into normal, suspicious, and compromised regions. The machine learning (ML) approach also outperformed the standard method of using fluorescence intensity only to evaluate tissue perfusion by a two-fold increase in accuracy. 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