{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T22:50:08Z","timestamp":1791499808975,"version":"4.3.4"},"reference-count":34,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"\n Background<\/jats:title>\n Early detection and monitoring of wound-related complications after total knee arthroplasty (TKA) are critical to optimize outcomes and preserve implants. Conventional postoperative monitoring relies on in-person assessment and patient-reported symptoms, which may delay recognition. Noncontact imaging acquired in the clinic or at home offers a potential route to earlier and more consistent assessment.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to evaluate the technical feasibility and performance of a multimodal AI system integrating electro-optical (EO) and infrared imaging for postoperative wound assessment following TKA and to explore clinical factors associated with early complications.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We conducted a single-center prospective cohort study of 749 patients undergoing primary TKA. Patients underwent standardized multimodal data collection at scheduled postoperative visits. EO-based models localized surgical landmarks and segmented complication-oriented tissue states; a fine-tuned vision-language model provided protocol verification and contextual visual reasoning. EO landmarks mapped to thermal space via deterministic alignment, followed by infrared region-of-interest segmentation and thermal classification. Each module was evaluated on a defined held-out set. The thermal classifier was assessed out-of-fold under 5-fold cross-validation with folds grouped by patient, in knees imaged on postoperative day 14 or later. Discrimination for clinician-adjudicated infection-related complications served as the primary outcome. Comorbidities and baseline laboratory values were explored for associations with complications.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Among 749 patients, 26 (3.5%) had a clinician-adjudicated infection-related complication. On held-out evaluation, the EO landmark detector reached a sensitivity of 0.967 (95% CI 0.954-0.979) and positive predictive value of 0.955 (95% CI 0.940-0.970), and the wound-tissue segmentation model reached a sensitivity of 0.845 (95% CI 0.833-0.857) and positive predictive value of 0.759 (95% CI 0.744-0.772). Infrared region-of-interest extraction isolated the surgical field with a mean intersection-over-union of 0.985 (SD 0.008; 95% CI 0.984-0.986) across 178 held-out patients. The trained thermal classifier discriminated infected scans with an area under the receiver operating characteristic curve (AUROC) of 0.978 (95% CI 0.959-0.992) in out-of-fold, patient-grouped cross-validation. At a representative operating point, sensitivity was 0.808 (95% CI 0.65-0.92), specificity 0.974 (95% CI 0.96-0.99), positive predictive value 0.618 (95% CI 0.49-0.78), and negative predictive value 0.990 (95% CI 0.98-1.00). Exploratory analyses showed nominal associations of chronic kidney disease and diabetes mellitus with postoperative complications that did not remain significant after multiplicity correction.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n In this formative study, a multimodal EO and infrared imaging pipeline demonstrated technical feasibility for postoperative wound assessment following TKA, providing noncontact discrimination of infection-related complications that compared favorably with established adjuncts. This work provides proof-of-concept for multimodal imaging-based assessment and defines the technical requirements for the prospective, multicenter external validation needed to establish clinical utility.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/99244","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T22:05:06Z","timestamp":1791497106000},"page":"e99244","source":"Crossref","is-referenced-by-count":0,"title":["Technical Feasibility of a Multimodal Electro-Optical and Infrared Imaging Pipeline for Postoperative Wound Assessment After Total Knee Arthroplasty: Formative Evaluation Study"],"prefix":"10.2196","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4211-937X","authenticated-orcid":false,"given":"Tej S","family":"Ranade","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5262-8217","authenticated-orcid":false,"given":"Emily","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2419-5793","authenticated-orcid":false,"given":"Kadambari","family":"Beelwar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5750-3221","authenticated-orcid":false,"given":"Sandeep","family":"Boddeda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1659-2587","authenticated-orcid":false,"given":"Praharsha","family":"Mulpur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9463-8950","authenticated-orcid":false,"given":"Adarsh","family":"Annapareddy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2207-8004","authenticated-orcid":false,"given":"A V","family":"Gurava Reddy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.2106\/JBJS.17.01617"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2020.12.005"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1186\/s13018-024-05099-8"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1302\/2633-1462.35.BJO-2022-0011.R1"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2024.01.032"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1001\/jamasurg.2023.0616"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1001\/jamasurg.2024.0133"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.artd.2025.101737"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2017.06.041"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcot.2019.01.007"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1097\/SLA.0000000000006826"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01419-8"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2025.05.121"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1186\/s42836-024-00282-y"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.5194\/jbji-9-137-2024"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18632\/oncotarget.22215"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.2196\/52880"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2009.03.043"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2012.03.007"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1177\/193229681000400414"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1118\/1.4907969"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0315384"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.knee.2011.02.012"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1177\/0300060516655237"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2474-14-7"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-33556-7"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2024.06.001"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.g7594"},{"key":"ref29","unstructured":"National Institute for Health and Care ExcellenceSurgical Site Infections: Prevention and Treatment (NG125)20202025-10-01LondonNICEhttps:\/\/www.nice.org.uk\/guidance\/ng125"},{"key":"ref30","year":"2018","journal-title":"Global Guidelines for the Prevention of Surgical Site Infection"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2025.05.093"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1088\/0967-3334\/33\/3\/r33"},{"key":"ref33","unstructured":"SchwartzRJanCGuidelines for point-of-care medical thermography20242024-09-15https:\/\/irp.cdn-website.com\/e1c76e4d\/files\/uploaded\/Guidelines-for-Point-of-Care-Medical-Thermography.pdf"},{"key":"ref34","unstructured":"Thermography guidelines: standards and protocolsInternational Academy of Clinical Thermology20192026-09-15https:\/\/www.iact-org.org\/professionals\/thermog-guidelines.html"}],"container-title":["JMIR Formative Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/formative.jmir.org\/2026\/1\/e99244","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T22:05:08Z","timestamp":1791497108000},"score":1,"resource":{"primary":{"URL":"https:\/\/formative.jmir.org\/2026\/1\/e99244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,8]]},"references-count":34,"URL":"https:\/\/doi.org\/10.2196\/99244","relation":{"has-preprint":[{"id-type":"doi","id":"10.2196\/preprints.99244","asserted-by":"object"}]},"ISSN":["2561-326X"],"issn-type":[{"value":"2561-326X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,8]]}}}