ArticleScientific reports2022
Development of a code-free machine learning model for the classification of cataract surgery phases.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Cataract Surgery: A Systematic Review.Translational vision science & technology · 2024Pooled it
- Comparative Analysis of Automated vs. Expert-Designed Machine Learning Models in Age-Related Macular Degeneration Detection and Classification.Turkish journal of ophthalmology · 2025Article
- Phase recognition in manual Small-Incision cataract surgery with MS-TCN + + on the novel SICS-105 dataset.Scientific reports · 2025Article
- Comparing Auto-Machine Learning and Expert-Designed Models in Diagnosing Vitreomacular Interface Disorders.Journal of clinical medicine · 2025Article
- Kinematic Adaptive Frame Recognition (KAFR): A Novel Framework for Video Segmentation via Frame Similarity and Surgical Tool Tracking.IEEE access : practical innovations, open solutions · 2025Article
- FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images.PloS one · 2025Article
- Performance of automated machine learning in detecting fundus diseases based on ophthalmologic B-scan ultrasound images.BMJ open ophthalmology · 2024Article
- Comparing code-free and bespoke deep learning approaches in ophthalmology.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Review
- Comparing code-free deep learning models to expert-designed models for detecting retinal diseases from optical coherence tomography.International journal of retina and vitreous · 2024Article
- Use Test of Automated Machine Learning in Cancer Diagnostics.Diagnostics (Basel, Switzerland) · 2023Article
- Non-coding deep learning models for tomato biotic and abiotic stress classification using microscopic images.Frontiers in plant science · 2023Article
- Deep learning for ultra-widefield imaging: a scoping review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022Article
- Testing the applicability and performance of Auto ML for potential applications in diagnostic neuroradiology.Scientific reports · 2022Article
- Performance of Automated Machine Learning in Predicting Outcomes of Pneumatic Retinopexy.Ophthalmology scienceArticle
- Development and deployment of a smartphone application for diagnosing trachoma: Leveraging code-free deep learning and edge artificial intelligence.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
- A User-friendly Approach for the Diagnosis of Diabetic Retinopathy Using ChatGPT and Automated Machine Learning.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study assessed the performance of automated machine learning (AutoML) in classifying cataract surgery phases from surgical videos. Two ophthalmology trainees without coding experience designed a deep learning model in Google Cloud AutoML Video Classification for the classification of 10 different cataract surgery phases. We used two open-access publicly available datasets (total of 122 surgeries) for model training, validation and testing. External validation was performed on 10 surgeries issued from another dataset. The AutoML model demonstrated excellent discriminating performance, even outperforming bespoke deep learning models handcrafter by experts. The area under the precision-recall curve was 0.855. At the 0.5 confidence threshold cut-off, the overall performance metrics were as follows: sensitivity (81.0%), recall (77.1%), accuracy (96.0%) and F1 score (0.79). The per-segment metrics varied across the surgical phases: precision 66.7-100%, recall 46.2-100% and specificity 94.1-100%. Hydrodissection and phacoemulsification were the most accurately predicted phases (100 and 92.31% correct predictions, respectively). During external validation, the average precision was 54.2% (0.00-90.0%), the recall was 61.1% (0.00-100%) and specificity was 96.2% (91.0-99.0%). In conclusion, a code-free AutoML model can accurately classify cataract surgery phases from videos with an accuracy comparable or better than models developed by experts.
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