NDLI: Automated detection of steps in videos of strabismus surgery using deep learning

Content Provider	Springer Nature : BioMed Central
Author	Zheng, Ce Li, Wen Wang, Siying Ye, Haiyun Xu, Kai Fang, Wangyi Dong, Yanli Wang, Zilei Qiao, Tong
Abstract	Background Learning to perform strabismus surgery is an essential aspect of ophthalmologists’ surgical training. Automated classification strategy for surgical steps can improve the effectiveness of training curricula and the efficient evaluation of residents’ performance. To this end, we aimed to develop and validate a deep learning (DL) model for automated detecting strabismus surgery steps in the videos. Methods In this study, we gathered 479 strabismus surgery videos from Shanghai Children’s Hospital, affiliated to Shanghai Jiao Tong University School of Medicine, spanning July 2017 to October 2021. The videos were manually cut into 3345 clips of the eight strabismus surgical steps based on the International Council of Ophthalmology’s Ophthalmology Surgical Competency Assessment Rubrics (ICO-OSCAR: strabismus). The videos dataset was randomly split by eye-level into a training (60%), validation (20%) and testing dataset (20%). We evaluated two hybrid DL algorithms: a Recurrent Neural Network (RNN) based and a Transformer-based model. The evaluation metrics included: accuracy, area under the receiver operating characteristic curve, precision, recall and F1-score. Results DL models identified the steps in video clips of strabismus surgery achieved macro-average AUC of 1.00 (95% CI 1.00–1.00) with Transformer-based model and 0.98 (95% CI 0.97-1.00) with RNN-based model, respectively. The Transformer-based model yielded a higher accuracy compared with RNN-based models (0.96 vs. 0.83, p < 0.001). In detecting different steps of strabismus surgery, the predictive ability of the Transformer-based model was better than that of the RNN. Precision ranged between 0.90 and 1 for the Transformer-based model and 0.75 to 0.94 for the RNN-based model. The f1-score ranged between 0.93 and 1 for the Transformer-based model and 0.78 to 0.92 for the RNN-based model. Conclusion The DL models can automate identify video steps of strabismus surgery with high accuracy and Transformer-based algorithms show excellent performance when modeling spatiotemporal features of video frames.
Related Links	https://bmcophthalmol.biomedcentral.com/counter/pdf/10.1186/s12886-024-03504-8.pdf
Ending Page	8
Page Count	8
Starting Page	1
File Format	HTM / HTML
ISSN	14712415
DOI	10.1186/s12886-024-03504-8
Journal	BMC Ophthalmology
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-06-10
Access Restriction	Open
Subject Keyword	Ophthalmology Deep learning Strabismus surgery Automated detection Surgical videos
Content Type	Text
Resource Type	Article
Subject	Ophthalmology
Journal Impact Factor	1.7/2023
5-Year Journal Impact Factor	2/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Detection of Referable Horizontal Strabismus in Children's Primary Gaze Photographs Using Deep Learning.

PhacoTrainer: Deep Learning for Cataract Surgical Videos to Track Surgical Tools.

PhacoTrainer: A Multicenter Study of Deep Learning for Activity Recognition in Cataract Surgical Videos.

Automated Detection of Surgical Implants on Plain Knee Radiographs Using a Deep Learning Algorithm.

Evaluation of quality and utility of YouTube vitreoretinal surgical videos

Assessment of Automated Identification of Phases in Videos of Cataract Surgery Using Machine Learning and Deep Learning Techniques

Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning.

Errors in Strabismus Surgery

Automated Detection of Keratorefractive Laser Surgeries on Optical Coherence Tomography using Deep Learning

Automated detection of steps in videos of strabismus surgery using deep learning

Similar Documents

Detection of Referable Horizontal Strabismus in Children's Primary Gaze Photographs Using Deep Learning.

PhacoTrainer: Deep Learning for Cataract Surgical Videos to Track Surgical Tools.

PhacoTrainer: A Multicenter Study of Deep Learning for Activity Recognition in Cataract Surgical Videos.

Automated Detection of Surgical Implants on Plain Knee Radiographs Using a Deep Learning Algorithm.

Evaluation of quality and utility of YouTube vitreoretinal surgical videos

Assessment of Automated Identification of Phases in Videos of Cataract Surgery Using Machine Learning and Deep Learning Techniques

Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning.

Errors in Strabismus Surgery

Automated Detection of Keratorefractive Laser Surgeries on Optical Coherence Tomography using Deep Learning

Automated detection of steps in videos of strabismus surgery using deep learning