Systematic review of machine learning applications using nonoptical motion tracking in surgery
Abstract This systematic review explores machine learning (ML) applications in surgical motion analysis using non-optical motion tracking systems (NOMTS), alone or with optical methods. It investigates objectives, experimental designs, model effectiveness, and future research directions. From 3632 r...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-024-01412-1 |
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| _version_ | 1832594400033636352 |
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| author | Teona Z. Carciumaru Cadey M. Tang Mohsen Farsi Wichor M. Bramer Jenny Dankelman Chirag Raman Clemens M. F. Dirven Maryam Gholinejad Dalibor Vasilic |
| author_facet | Teona Z. Carciumaru Cadey M. Tang Mohsen Farsi Wichor M. Bramer Jenny Dankelman Chirag Raman Clemens M. F. Dirven Maryam Gholinejad Dalibor Vasilic |
| author_sort | Teona Z. Carciumaru |
| collection | DOAJ |
| description | Abstract This systematic review explores machine learning (ML) applications in surgical motion analysis using non-optical motion tracking systems (NOMTS), alone or with optical methods. It investigates objectives, experimental designs, model effectiveness, and future research directions. From 3632 records, 84 studies were included, with Artificial Neural Networks (38%) and Support Vector Machines (11%) being the most common ML models. Skill assessment was the primary objective (38%). NOMTS used included internal device kinematics (56%), electromagnetic (17%), inertial (15%), mechanical (11%), and electromyography (1%) sensors. Surgical settings were robotic (60%), laparoscopic (18%), open (16%), and others (6%). Procedures focused on bench-top tasks (67%), clinical models (17%), clinical simulations (9%), and non-clinical simulations (7%). Over 90% accuracy was achieved in 36% of studies. Literature shows NOMTS and ML can enhance surgical precision, assessment, and training. Future research should advance ML in surgical environments, ensure model interpretability and reproducibility, and use larger datasets for accurate evaluation. |
| format | Article |
| id | doaj-art-428d6e8a2e6947fbb8a9ec617f043b6a |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-428d6e8a2e6947fbb8a9ec617f043b6a2025-01-19T12:39:51ZengNature Portfolionpj Digital Medicine2398-63522025-01-018112010.1038/s41746-024-01412-1Systematic review of machine learning applications using nonoptical motion tracking in surgeryTeona Z. Carciumaru0Cadey M. Tang1Mohsen Farsi2Wichor M. Bramer3Jenny Dankelman4Chirag Raman5Clemens M. F. Dirven6Maryam Gholinejad7Dalibor Vasilic8Department of Plastic and Reconstructive Surgery, Erasmus MC University Medical CenterDepartment of Plastic and Reconstructive Surgery, Erasmus MC University Medical CenterDepartment of Plastic and Reconstructive Surgery, Erasmus MC University Medical CenterMedical Library, Erasmus MC University Medical CenterDepartment of Biomechanical Engineering, Delft University of TechnologyDepartment of Pattern Recognition and Bioinformatics, Delft University of TechnologyDepartment of Neurosurgery, Erasmus MC University Medical CenterDepartment of Plastic and Reconstructive Surgery, Erasmus MC University Medical CenterDepartment of Plastic and Reconstructive Surgery, Erasmus MC University Medical CenterAbstract This systematic review explores machine learning (ML) applications in surgical motion analysis using non-optical motion tracking systems (NOMTS), alone or with optical methods. It investigates objectives, experimental designs, model effectiveness, and future research directions. From 3632 records, 84 studies were included, with Artificial Neural Networks (38%) and Support Vector Machines (11%) being the most common ML models. Skill assessment was the primary objective (38%). NOMTS used included internal device kinematics (56%), electromagnetic (17%), inertial (15%), mechanical (11%), and electromyography (1%) sensors. Surgical settings were robotic (60%), laparoscopic (18%), open (16%), and others (6%). Procedures focused on bench-top tasks (67%), clinical models (17%), clinical simulations (9%), and non-clinical simulations (7%). Over 90% accuracy was achieved in 36% of studies. Literature shows NOMTS and ML can enhance surgical precision, assessment, and training. Future research should advance ML in surgical environments, ensure model interpretability and reproducibility, and use larger datasets for accurate evaluation.https://doi.org/10.1038/s41746-024-01412-1 |
| spellingShingle | Teona Z. Carciumaru Cadey M. Tang Mohsen Farsi Wichor M. Bramer Jenny Dankelman Chirag Raman Clemens M. F. Dirven Maryam Gholinejad Dalibor Vasilic Systematic review of machine learning applications using nonoptical motion tracking in surgery npj Digital Medicine |
| title | Systematic review of machine learning applications using nonoptical motion tracking in surgery |
| title_full | Systematic review of machine learning applications using nonoptical motion tracking in surgery |
| title_fullStr | Systematic review of machine learning applications using nonoptical motion tracking in surgery |
| title_full_unstemmed | Systematic review of machine learning applications using nonoptical motion tracking in surgery |
| title_short | Systematic review of machine learning applications using nonoptical motion tracking in surgery |
| title_sort | systematic review of machine learning applications using nonoptical motion tracking in surgery |
| url | https://doi.org/10.1038/s41746-024-01412-1 |
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