{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T01:22:39Z","timestamp":1772068959397,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T00:00:00Z","timestamp":1624665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014440","name":"Ministerio de Ciencia, Innovaci\u00f3n y Universidades","doi-asserted-by":"publisher","award":["PI18\/01625"],"award-info":[{"award-number":["PI18\/01625"]}],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014440","name":"Ministerio de Ciencia, Innovaci\u00f3n y Universidades","doi-asserted-by":"publisher","award":["202050E304"],"award-info":[{"award-number":["202050E304"]}],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004587","name":"Instituto de Salud Carlos III","doi-asserted-by":"publisher","award":["PI18\/01625"],"award-info":[{"award-number":["PI18\/01625"]}],"id":[{"id":"10.13039\/501100004587","id-type":"DOI","asserted-by":"publisher"}]},{"name":"European Regional Development Fund \u201cUna manera de hacer Europa\u201d","award":["PI18\/01625"],"award-info":[{"award-number":["PI18\/01625"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Deep learning is a recent technology that has shown excellent capabilities for recognition and identification tasks. This study applies these techniques in open cranial vault remodeling surgeries performed to correct craniosynostosis. The objective was to automatically recognize surgical tools in real-time and estimate the surgical phase based on those predictions. For this purpose, we implemented, trained, and tested three algorithms based on previously proposed Convolutional Neural Network architectures (VGG16, MobileNetV2, and InceptionV3) and one new architecture with fewer parameters (CranioNet). A novel 3D Slicer module was specifically developed to implement these networks and recognize surgical tools in real time via video streaming. The training and test data were acquired during a surgical simulation using a 3D printed patient-based realistic phantom of an infant\u2019s head. The results showed that CranioNet presents the lowest accuracy for tool recognition (93.4%), while the highest accuracy is achieved by the MobileNetV2 model (99.6%), followed by VGG16 and InceptionV3 (98.8% and 97.2%, respectively). Regarding phase detection, InceptionV3 and VGG16 obtained the best results (94.5% and 94.4%), whereas MobileNetV2 and CranioNet presented worse values (91.1% and 89.8%). Our results prove the feasibility of applying deep learning architectures for real-time tool detection and phase estimation in craniosynostosis surgeries.<\/jats:p>","DOI":"10.3390\/e23070817","type":"journal-article","created":{"date-parts":[[2021,6,27]],"date-time":"2021-06-27T22:24:57Z","timestamp":1624832697000},"page":"817","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1802-2884","authenticated-orcid":false,"given":"Alicia","family":"Pose D\u00edez de la Lastra","sequence":"first","affiliation":[{"name":"Departamento de Bioingenier\u00eda e Ingenier\u00eda Aeroespacial, Universidad Carlos III de Madrid, 28911 Legan\u00e9s, Spain"},{"name":"Instituto de Investigaci\u00f3n Sanitaria Gregorio Mara\u00f1\u00f3n, 28007 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7390-4946","authenticated-orcid":false,"given":"Luc\u00eda","family":"Garc\u00eda-Duarte S\u00e1enz","sequence":"additional","affiliation":[{"name":"Departamento de Bioingenier\u00eda e Ingenier\u00eda Aeroespacial, Universidad Carlos III de Madrid, 28911 Legan\u00e9s, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5390-5768","authenticated-orcid":false,"given":"David","family":"Garc\u00eda-Mato","sequence":"additional","affiliation":[{"name":"Departamento de Bioingenier\u00eda e Ingenier\u00eda Aeroespacial, Universidad Carlos III de Madrid, 28911 Legan\u00e9s, Spain"},{"name":"Instituto de Investigaci\u00f3n Sanitaria Gregorio Mara\u00f1\u00f3n, 28007 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2637-8901","authenticated-orcid":false,"given":"Luis","family":"Hern\u00e1ndez-\u00c1lvarez","sequence":"additional","affiliation":[{"name":"Departamento de Tecnolog\u00edas de la Informaci\u00f3n y las Comunicaciones (TIC), Instituto de Tecnolog\u00edas F\u00edsicas y de la Informaci\u00f3n (ITEFI), Consejo Superior de Investigaciones Cient\u00edficas (CSIC), 28006 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3576-6498","authenticated-orcid":false,"given":"Santiago","family":"Ochandiano","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n Sanitaria Gregorio Mara\u00f1\u00f3n, 28007 Madrid, Spain"},{"name":"Servicio de Cirug\u00eda Oral y Maxilofacial, Hospital General Universitario Gregorio Mara\u00f1\u00f3n, 28007 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1484-731X","authenticated-orcid":false,"given":"Javier","family":"Pascau","sequence":"additional","affiliation":[{"name":"Departamento de Bioingenier\u00eda e Ingenier\u00eda Aeroespacial, Universidad Carlos III de Madrid, 28911 Legan\u00e9s, Spain"},{"name":"Instituto de Investigaci\u00f3n Sanitaria Gregorio Mara\u00f1\u00f3n, 28007 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat. 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