Computer vision in surgery: from potential to clinical value
Open accessAbstract
Hundreds of millions of operations are performed worldwide each year, and the rising uptake in minimally invasive surgery has enabled fiber optic cameras and robots to become both important tools to conduct surgery and sensors from which to capture information about surgery. Computer vision (CV), the application of algorithms to analyze and interpret visual data, has become a critical technology through which to study the intraoperative phase of care with the goals of augmenting surgeons' decision-making processes, supporting safer surgery, and expanding access to surgical care. While much work has been performed on potential use cases, there are currently no CV tools widely used for diagnostic or therapeutic applications in surgery. Using laparoscopic cholecystectomy as an example, we reviewed current CV techniques that have been applied to minimally invasive surgery and their clinical applications. Finally, we discuss the challenges and obstacles that remain to be overcome for broader implementation and adoption of CV in surgery.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI applications are advancing in image-guided radiotherapy, adaptive radiotherapy, stereotactic radiosurgery, and surgery.
“AI applications are also now advancing in the fields of image-guided radiotherapy, adaptive radiotherapy, stereotactic radiosurgery (119), and surgery in general, where autocontouring is similarly helpful (120).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [120]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 76 · Cited by 228
Research works printed but not linked: 4
- [26]Yengera, G., Mutter, D., Marescaux, J. & Padoy, N. Less is more: Surgical phase recognition with less annotations through self-supervised pre-training of CNN-LSTM networks. arXiv [cs.CV] (2018).
- [60]Maier-Hein L Surgical data science - from concepts toward clinical translation Med. Image Anal. 2022 76 102306 34879287 PMC9135051
- [64]Taleb, A. et al. 3D self-supervised methods for medical imaging. In Proceedings of the 34th International Conference on Neural Information Processing Systems (pp. 18158–18172) (2020).
- [65]Yu, T., Mutter, D., Marescaux, J. & Padoy, N. Learning from a tiny dataset of manual annotations: a teacher/student approach for surgical phase recognition. Preprint at: https://arxiv.org/abs/1812.00033 (2018). · arXiv
- Authors
- Mascagni, P., Alapatt, D., Sestini, L., Altieri, M. S., Madani, A., Watanabe, Y., Alseidi, A., Redan, J. A., Alfieri, S., Costamagna, G., Boškoski, I., Padoy, N., & Hashimoto, D. A.
- Affiliations
- Università Cattolica del Sacro Cuore (IT)
- Centre National de la Recherche Scientifique (FR)
- University of Pennsylvania (US)
- University Health Network (CA)
- Hokkaido University (JP)
- University of California, San Francisco (US)
- AdventHealth Celebration (US)
- Agostino Gemelli University Polyclinic (IT)
- Corresponding author
- Mascagni, Pietro
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2022-10-28
- Volume
- 5
- Issue
- 1
- Pages
- 163-163
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC9616906
- OpenAlex
- W4307511335
- Cited by
- 228
- References
- 76
- Citations by year
- 2026: 69 · 2025: 76 · 2024: 56 · 2023: 25 · 2022: 2
- FWCI
- 9.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- PubliCatt (Università Cattolica del Sacro Cuore) (submittedVersion, other-oa)
- Funders
- EC | Horizon 2020 Framework Programme (813782)
- Agence Nationale de la Recherche (ANR-20-CHIA-0029-01, ANR-10-IAHU-02)
- Topics
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- minimally invasive surgery, laparoscopic cholecystectomy, intraoperative imaging, surgical decision support, surgical safety, surgical robotics, clinical implementation barriers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
