Deep learning for surgical instrument recognition and segmentation in robotic-assisted surgeries: a systematic review
<p dir="ltr">Applying deep learning (DL) for annotating surgical instruments in robot-assisted minimally invasive surgeries (MIS) represents a significant advancement in surgical technology. This systematic review examines 48 studies that utilize advanced DL methods and architectures...
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| مؤلفون آخرون: | , , , , , , , , |
| منشور في: |
2024
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| _version_ | 1864513540075814912 |
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| author | Fatimaelzahraa Ali Ahmed (22224640) |
| author2 | Mahmoud Yousef (9900837) Mariam Ali Ahmed (22224643) Hasan Omar Ali (22224646) Anns Mahboob (17337886) Hazrat Ali (421019) Zubair Shah (231886) Omar Aboumarzouk (18427923) Abdulla Al Ansari (14058060) Shidin Balakrishnan (14150580) |
| author2_role | author author author author author author author author author |
| author_facet | Fatimaelzahraa Ali Ahmed (22224640) Mahmoud Yousef (9900837) Mariam Ali Ahmed (22224643) Hasan Omar Ali (22224646) Anns Mahboob (17337886) Hazrat Ali (421019) Zubair Shah (231886) Omar Aboumarzouk (18427923) Abdulla Al Ansari (14058060) Shidin Balakrishnan (14150580) |
| author_role | author |
| dc.creator.none.fl_str_mv | Fatimaelzahraa Ali Ahmed (22224640) Mahmoud Yousef (9900837) Mariam Ali Ahmed (22224643) Hasan Omar Ali (22224646) Anns Mahboob (17337886) Hazrat Ali (421019) Zubair Shah (231886) Omar Aboumarzouk (18427923) Abdulla Al Ansari (14058060) Shidin Balakrishnan (14150580) |
| dc.date.none.fl_str_mv | 2024-11-04T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10462-024-10979-w |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Deep_learning_for_surgical_instrument_recognition_and_segmentation_in_robotic-assisted_surgeries_a_systematic_review/30094486 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Deep learning Surgical tool annotation Robotic surgery Minimally invasive surgery Convolutional neural networks U-Net ResNet |
| dc.title.none.fl_str_mv | Deep learning for surgical instrument recognition and segmentation in robotic-assisted surgeries: a systematic review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Applying deep learning (DL) for annotating surgical instruments in robot-assisted minimally invasive surgeries (MIS) represents a significant advancement in surgical technology. This systematic review examines 48 studies that utilize advanced DL methods and architectures. These sophisticated DL models have shown notable improvements in the precision and efficiency of detecting and segmenting surgical tools. The enhanced capabilities of these models support various clinical applications, including real-time intraoperative guidance, comprehensive postoperative evaluations, and objective assessments of surgical skills. By accurately identifying and segmenting surgical instruments in video data, DL models provide detailed feedback to surgeons, thereby improving surgical outcomes and reducing complication risks. Furthermore, the application of DL in surgical education is transformative. The review underscores the significant impact of DL on improving the accuracy of skill assessments and the overall quality of surgical training programs. However, implementing DL in surgical tool detection and segmentation faces challenges, such as the need for large, accurately annotated datasets to train these models effectively. The manual annotation process is labor-intensive and time-consuming, posing a significant bottleneck. Future research should focus on automating the detection and segmentation process and enhancing the robustness of DL models against environmental variations. Expanding the application of DL models across various surgical specialties will be essential to fully realize this technology’s potential. Integrating DL with other emerging technologies, such as augmented reality (AR), also offers promising opportunities to further enhance the precision and efficacy of surgical procedures.</p><h2>Other Information</h2><p dir="ltr">Published in: Artificial Intelligence Review<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10462-024-10979-w" target="_blank">https://dx.doi.org/10.1007/s10462-024-10979-w</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_14f08c01b3a9fdf1338377cc1c51bd99 |
| identifier_str_mv | 10.1007/s10462-024-10979-w |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30094486 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Deep learning for surgical instrument recognition and segmentation in robotic-assisted surgeries: a systematic reviewFatimaelzahraa Ali Ahmed (22224640)Mahmoud Yousef (9900837)Mariam Ali Ahmed (22224643)Hasan Omar Ali (22224646)Anns Mahboob (17337886)Hazrat Ali (421019)Zubair Shah (231886)Omar Aboumarzouk (18427923)Abdulla Al Ansari (14058060)Shidin Balakrishnan (14150580)Biomedical and clinical sciencesClinical sciencesEngineeringBiomedical engineeringHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceDeep learningSurgical tool annotationRobotic surgeryMinimally invasive surgeryConvolutional neural networksU-NetResNet<p dir="ltr">Applying deep learning (DL) for annotating surgical instruments in robot-assisted minimally invasive surgeries (MIS) represents a significant advancement in surgical technology. This systematic review examines 48 studies that utilize advanced DL methods and architectures. These sophisticated DL models have shown notable improvements in the precision and efficiency of detecting and segmenting surgical tools. The enhanced capabilities of these models support various clinical applications, including real-time intraoperative guidance, comprehensive postoperative evaluations, and objective assessments of surgical skills. By accurately identifying and segmenting surgical instruments in video data, DL models provide detailed feedback to surgeons, thereby improving surgical outcomes and reducing complication risks. Furthermore, the application of DL in surgical education is transformative. The review underscores the significant impact of DL on improving the accuracy of skill assessments and the overall quality of surgical training programs. However, implementing DL in surgical tool detection and segmentation faces challenges, such as the need for large, accurately annotated datasets to train these models effectively. The manual annotation process is labor-intensive and time-consuming, posing a significant bottleneck. Future research should focus on automating the detection and segmentation process and enhancing the robustness of DL models against environmental variations. Expanding the application of DL models across various surgical specialties will be essential to fully realize this technology’s potential. Integrating DL with other emerging technologies, such as augmented reality (AR), also offers promising opportunities to further enhance the precision and efficacy of surgical procedures.</p><h2>Other Information</h2><p dir="ltr">Published in: Artificial Intelligence Review<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10462-024-10979-w" target="_blank">https://dx.doi.org/10.1007/s10462-024-10979-w</a></p>2024-11-04T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10462-024-10979-whttps://figshare.com/articles/journal_contribution/Deep_learning_for_surgical_instrument_recognition_and_segmentation_in_robotic-assisted_surgeries_a_systematic_review/30094486CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300944862024-11-04T03:00:00Z |
| spellingShingle | Deep learning for surgical instrument recognition and segmentation in robotic-assisted surgeries: a systematic review Fatimaelzahraa Ali Ahmed (22224640) Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Deep learning Surgical tool annotation Robotic surgery Minimally invasive surgery Convolutional neural networks U-Net ResNet |
| status_str | publishedVersion |
| title | Deep learning for surgical instrument recognition and segmentation in robotic-assisted surgeries: a systematic review |
| title_full | Deep learning for surgical instrument recognition and segmentation in robotic-assisted surgeries: a systematic review |
| title_fullStr | Deep learning for surgical instrument recognition and segmentation in robotic-assisted surgeries: a systematic review |
| title_full_unstemmed | Deep learning for surgical instrument recognition and segmentation in robotic-assisted surgeries: a systematic review |
| title_short | Deep learning for surgical instrument recognition and segmentation in robotic-assisted surgeries: a systematic review |
| title_sort | Deep learning for surgical instrument recognition and segmentation in robotic-assisted surgeries: a systematic review |
| topic | Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Deep learning Surgical tool annotation Robotic surgery Minimally invasive surgery Convolutional neural networks U-Net ResNet |