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Ghent University Academic Bibliography

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01GTH6QRQWYCCY9HF4E3CD4A8N classification A1.
01GTH6QRQWYCCY9HF4E3CD4A8N date "2022".
01GTH6QRQWYCCY9HF4E3CD4A8N language "eng".
01GTH6QRQWYCCY9HF4E3CD4A8N type journalArticle.
01GTH6QRQWYCCY9HF4E3CD4A8N hasPart 01GTH75QX3C5RW4RPYHYMSZFA2.pdf.
01GTH6QRQWYCCY9HF4E3CD4A8N subject "Medicine and Health Sciences".
01GTH6QRQWYCCY9HF4E3CD4A8N subject "Technology and Engineering".
01GTH6QRQWYCCY9HF4E3CD4A8N doi "10.1007/s00464-022-09487-1".
01GTH6QRQWYCCY9HF4E3CD4A8N issn "0930-2794".
01GTH6QRQWYCCY9HF4E3CD4A8N issn "1432-2218".
01GTH6QRQWYCCY9HF4E3CD4A8N issue "11".
01GTH6QRQWYCCY9HF4E3CD4A8N presentedAt urn:uuid:37e7257f-99a2-4323-bef6-cfbd5ac73a67.
01GTH6QRQWYCCY9HF4E3CD4A8N volume "36".
01GTH6QRQWYCCY9HF4E3CD4A8N abstract "Background Artificial intelligence (AI) holds tremendous potential to reduce surgical risks and improve surgical assessment. Machine learning, a subfield of AI, can be used to analyze surgical video and imaging data. Manual annotations provide veracity about the desired target features. Yet, methodological annotation explorations are limited to date. Here, we provide an exploratory analysis of the requirements and methods of instrument annotation in a multi-institutional team from two specialized AI centers and compile our lessons learned. Methods We developed a bottom-up approach for team annotation of robotic instruments in robot-assisted partial nephrectomy (RAPN), which was subsequently validated in robot-assisted minimally invasive esophagectomy (RAMIE). Furthermore, instrument annotation methods were evaluated for their use in Machine Learning algorithms. Overall, we evaluated the efficiency and transferability of the proposed team approach and quantified performance metrics (e.g., time per frame required for each annotation modality) between RAPN and RAMIE. Results We found a 0.05 Hz image sampling frequency to be adequate for instrument annotation. The bottom-up approach in annotation training and management resulted in accurate annotations and demonstrated efficiency in annotating large datasets. The proposed annotation methodology was transferrable between both RAPN and RAMIE. The average annotation time for RAPN pixel annotation ranged from 4.49 to 12.6 min per image; for vector annotation, we denote 2.92 min per image. Similar annotation times were found for RAMIE. Lastly, we elaborate on common pitfalls encountered throughout the annotation process. Conclusions We propose a successful bottom-up approach for annotator team composition, applicable to any surgical annotation project. Our results set the foundation to start AI projects for instrument detection, segmentation, and pose estimation. Due to the immense annotation burden resulting from spatial instrumental annotation, further analysis into sampling frequency and annotation detail needs to be conducted.".
01GTH6QRQWYCCY9HF4E3CD4A8N author 00A4AE12-F0EE-11E1-A9DE-61C894A0A6B4.
01GTH6QRQWYCCY9HF4E3CD4A8N author 0BE4CA0A-F0EE-11E1-A9DE-61C894A0A6B4.
01GTH6QRQWYCCY9HF4E3CD4A8N author 0E596C64-F0EE-11E1-A9DE-61C894A0A6B4.
01GTH6QRQWYCCY9HF4E3CD4A8N author 14E97812-F0EE-11E1-A9DE-61C894A0A6B4.
01GTH6QRQWYCCY9HF4E3CD4A8N author 39B36AC2-F0EE-11E1-A9DE-61C894A0A6B4.
01GTH6QRQWYCCY9HF4E3CD4A8N author 5EE1A87C-9E4F-11E7-AA8C-06D5AD28A064.
01GTH6QRQWYCCY9HF4E3CD4A8N author 708B8C06-786B-11E6-815E-A870B5D1D7B1.
01GTH6QRQWYCCY9HF4E3CD4A8N author 7ACA306E-0C13-11E2-AA3C-3DCD10BDE39D.
01GTH6QRQWYCCY9HF4E3CD4A8N author 86BF5F12-9998-11E7-9FC9-587FAD28A064.
01GTH6QRQWYCCY9HF4E3CD4A8N author 9FC30E5C-8785-11E7-9FA5-1B3AAE28A064.
01GTH6QRQWYCCY9HF4E3CD4A8N author urn:uuid:10ef8808-cefc-45d8-ae8f-76ef74869296.
01GTH6QRQWYCCY9HF4E3CD4A8N author urn:uuid:37e182e0-c1f3-400c-9522-dab8974841be.
01GTH6QRQWYCCY9HF4E3CD4A8N author urn:uuid:a19d5668-36ba-46fa-a211-983ea5e76628.
01GTH6QRQWYCCY9HF4E3CD4A8N author urn:uuid:b859d94e-052d-467e-9638-f655d58c11ac.
01GTH6QRQWYCCY9HF4E3CD4A8N author urn:uuid:d6d83c39-ddbb-4ac0-b169-86fb7bf0e84b.
01GTH6QRQWYCCY9HF4E3CD4A8N dateCreated "2023-03-02T13:03:44Z".
01GTH6QRQWYCCY9HF4E3CD4A8N dateModified "2025-01-17T09:29:11Z".
01GTH6QRQWYCCY9HF4E3CD4A8N name "Multicentric exploration of tool annotation in robotic surgery : lessons learned when starting a surgical artificial intelligence project".
01GTH6QRQWYCCY9HF4E3CD4A8N pagination urn:uuid:52909664-c7c7-4015-b50d-876924e2b9f5.
01GTH6QRQWYCCY9HF4E3CD4A8N publisher urn:uuid:0f8a6320-eeda-42d0-9cd2-e0c6726105bb.
01GTH6QRQWYCCY9HF4E3CD4A8N sameAs LU-01GTH6QRQWYCCY9HF4E3CD4A8N.
01GTH6QRQWYCCY9HF4E3CD4A8N sourceOrganization urn:uuid:5657bda6-7e64-4fc4-b947-aca8a4b63277.
01GTH6QRQWYCCY9HF4E3CD4A8N sourceOrganization urn:uuid:5ba4390a-8c77-4cc3-a55a-ac7844e14872.
01GTH6QRQWYCCY9HF4E3CD4A8N sourceOrganization urn:uuid:e2d81cc5-e2fe-498d-a68c-87a822d41016.
01GTH6QRQWYCCY9HF4E3CD4A8N type A1.