{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T06:05:19Z","timestamp":1786687519369,"version":"build-2736575974"},"reference-count":42,"publisher":"Walter de Gruyter GmbH","issue":"3","license":[{"start":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T00:00:00Z","timestamp":1742774400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec id=\"j_bmt-2024-0431_abs_001\">\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>The operating room is a fast-paced and demanding environment. Among the various factors involved in its optimization, predicting surgery duration is critical for scheduling and resource organization, ultimately resulting in improved quality of surgical care.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_bmt-2024-0431_abs_002\">\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We design, implement and evaluate a semi-automated machine learning method that takes as input the current phase and tools employed and provides prediction of the Remain Surgery Duration (RSD) in laparoscopic cholecystectomy operations. We use the annotated information of tools and phases provided in the publicly available dataset Cholec80. The method is based on a Random Forest regression model that considers two data streams: the surgical phase and the type of tools employed, at each time-frame of the operation. The data were split into Training-, Validation- and Test-sets. The Mean Absolute Error (MAE) was used as the performance metric for the various models examined.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_bmt-2024-0431_abs_003\">\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Our approach managed to achieve a MAE=5.89\u00a0min across the overall duration of the surgeries in the test-set and MAE=4.61\u00a0min at 20\u00a0min before the end of the operation.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_bmt-2024-0431_abs_004\">\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>The employment of two separate regression models switched at a specific elapsed time threshold provides significant improvement in RSD prediction compared to other methods that process the video from the endoscope.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1515\/bmt-2024-0431","type":"journal-article","created":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T12:28:27Z","timestamp":1742560107000},"page":"229-239","source":"Crossref","is-referenced-by-count":6,"title":["Prediction of remaining surgery duration based on machine learning methods and laparoscopic annotation data"],"prefix":"10.1515","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0223-2502","authenticated-orcid":false,"given":"Spiros","family":"Kostopoulos","sequence":"first","affiliation":[{"name":"Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering , 523391 University of West Attica , Athens , Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dionisis","family":"Cavouras","sequence":"additional","affiliation":[{"name":"Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering , 523391 University of West Attica , Athens , Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitris","family":"Glotsos","sequence":"additional","affiliation":[{"name":"Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering , 523391 University of West Attica , Athens , Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7879-7329","authenticated-orcid":false,"given":"Constantinos","family":"Loukas","sequence":"additional","affiliation":[{"name":"Laboratory of Medical Physics, Medical School, National and Kapodistrian University of Athens , Athens , Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2025,3,24]]},"reference":[{"key":"2025060123233406193_j_bmt-2024-0431_ref_001","doi-asserted-by":"crossref","unstructured":"Omling, E, Jarnheimer, A, Rose, J, Bj\u00f6rk, J, Meara, JG, Hagander, L. 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BMC Med Inf Decis Making 2023;23:80. https:\/\/doi.org\/10.1186\/s12911-023-02160-0.","DOI":"10.1186\/s12911-023-02160-0"},{"key":"2025060123233406193_j_bmt-2024-0431_ref_035","doi-asserted-by":"crossref","unstructured":"Shinozuka, K, Turuda, S, Fujinaga, A, Nakanuma, H, Kawamura, M, Matsunobu, Y, et al.. Artificial intelligence software available for medical devices: surgical phase recognition in laparoscopic cholecystectomy. Surg Endosc 2022;36:7444\u201352. https:\/\/doi.org\/10.1007\/s00464-022-09160-7.","DOI":"10.1007\/s00464-022-09160-7"},{"key":"2025060123233406193_j_bmt-2024-0431_ref_036","doi-asserted-by":"crossref","unstructured":"Jalal, NA, Alshirbaji, TA, Docherty, PD, Arabian, H, Laufer, B, Krueger-Ziolek, S, et al.. Laparoscopic video analysis using temporal, attention,\u00a0and multi-feature fusion based-approaches. Sensors 2023;23:1958. https:\/\/doi.org\/10.3390\/s23041958.","DOI":"10.3390\/s23041958"},{"key":"2025060123233406193_j_bmt-2024-0431_ref_037","doi-asserted-by":"crossref","unstructured":"Hasan, MK, Calvet, L, Rabbani, N, Bartoli, A. Detection, segmentation, and 3D pose estimation of surgical tools using convolutional neural\u00a0networks and algebraic geometry. Med Image Anal 2021;70:101994. https:\/\/doi.org\/10.1016\/j.media.2021.101994.","DOI":"10.1016\/j.media.2021.101994"},{"key":"2025060123233406193_j_bmt-2024-0431_ref_038","doi-asserted-by":"crossref","unstructured":"Arabian, H, Abdulbaki Alshirbaji, T, Jalal, NA, Krueger-Ziolek, S, Moeller, K. P-CSEM: an attention module for improved laparoscopic surgical tool detection. Sensors (Basel) 2023;23:7257. https:\/\/doi.org\/10.3390\/s23167257.","DOI":"10.3390\/s23167257"},{"key":"2025060123233406193_j_bmt-2024-0431_ref_039","doi-asserted-by":"crossref","unstructured":"Glaser, B, D\u00e4nzer, S, Neumuth, T. 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