{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T08:06:45Z","timestamp":1779782805309,"version":"3.53.1"},"reference-count":35,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T00:00:00Z","timestamp":1779494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T00:00:00Z","timestamp":1779494400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Surg Innov"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>In minimally invasive surgery (MIS), maintaining consistent optical clarity is essential for surgical safety and efficiency. However, lens contamination events (LCEs) such as fogging and debris accumulation continue to disrupt visualization, requiring frequent camera removal and cleaning that prolongs operative time and is linked to surgical injuries.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Determine performance of an artificial intelligence (AI) model, LUCID, for the detection of laparoscopic lens contamination.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Surgical video footage was collected from the central Texas region and annotated to classify the lens state as \u201cclean\u201d or \u201cdirty.\u201d An AI model was trained on this dataset to detect lens contamination. 9 additional laparoscopic procedures were analyzed by the model and validated by human reviewers to determine true model performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Across the 9 cases analyzed by LUCID and validated by reviewers, the model achieved an overall accuracy of 90.42%, with a precision of 94.45%, sensitivity of 91.19%, and specificity of 88.80%, demonstrating potential for the use of LUCID to assess optical clarity in MIS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI-enhanced visualization assessment presents a promising approach to enable objective detection of visual clarity in MIS. With compromised vision being a known problem with serious safety concerns, and future applications for AI-enhanced surgery requiring clear visualization for high performance, the importance of clear visualization is paramount more than ever before. Future work may look to standardize visual clarity across operative settings to mitigate variability in clinical outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/15533506261451747","type":"journal-article","created":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T14:12:26Z","timestamp":1779545546000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["LUCID: An AI-Powered System for Real-Time Laparoscopic Lens Cleanliness Classification"],"prefix":"10.1177","author":[{"given":"Eshan","family":"Sayani","sequence":"first","affiliation":[{"name":"Texas A&M Naresh K. 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