Bibliometric analysis of deep learning for surgical instrument segmentation, detection and tracking in minimally invasive surgery.

Yousef M, Aly KE, Ahmed M, Ahmed FA, Al Jalham K, Balakrishnan S.

Open source

DOI: 10.3389/fdgth.2026.1633888
Published: 2026-02-27
Container: Front Digit Health
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3389/fdgth.2026.1633888,
  title = {Bibliometric analysis of deep learning for surgical instrument segmentation, detection and tracking in minimally invasive surgery.},
  author = {Yousef M and  Aly KE and  Ahmed M and  Ahmed FA and  Al Jalham K and  Balakrishnan S.},
  year = {2026},
  journal = {Front Digit Health},
  doi = {10.3389/fdgth.2026.1633888},
  url = {https://doi.org/10.3389/fdgth.2026.1633888}
}

RIS

TY  - JOUR
TI  - Bibliometric analysis of deep learning for surgical instrument segmentation, detection and tracking in minimally invasive surgery.
AU  - Yousef M
AU  -  Aly KE
AU  -  Ahmed M
AU  -  Ahmed FA
AU  -  Al Jalham K
AU  -  Balakrishnan S.
PY  - 2026
JO  - Front Digit Health
DO  - 10.3389/fdgth.2026.1633888
UR  - https://doi.org/10.3389/fdgth.2026.1633888
ER  -

APA

M, Y., KE, A., M, A., FA, A., K, A. J., & S., B. (2026). Bibliometric analysis of deep learning for surgical instrument segmentation, detection and tracking in minimally invasive surgery.. Front Digit Health. https://doi.org/10.3389/fdgth.2026.1633888

Source records

europe-pmc · retrieved 2026-09-25T13:12:24.266Z
doaj · retrieved 2026-09-25T13:12:24.273Z