| Title: |
SICS-105: Phase recognition in Manual Small-Incision Cataract Surgery |
| Authors: |
Mueller, Simon; Wintergerst, Maximilian; Schultz, Thomas |
| Contributors: |
Sachdeva, Bhuvan; Mohit, Jain; Prasad, Singri Niharika; Murali, Kaushik; Holz, Frank; Finger, Robert; Lechtenboehmer, Raphael |
| Publisher Information: |
Zenodo |
| Publication Year: |
2024 |
| Collection: |
Zenodo |
| Subject Terms: |
Small-Incision Cataract Surgery; Phacoemulsification; Artificial Intelligence; Temporal Action Segmentation; Phase Recognition; Video Dataset |
| Description: |
This prospective cross-sectional study introduces the first Manual Small-Incision Cataract Surgery (SICS) video dataset, which is prevalent but understudied in low- and middle-income countries (LMICs), evaluates effectiveness of phase recognition through deep learning (DL) using the MS-TCN++ architecture and compares its results with the well-studied phacoemulsification procedure using the Cataract-101 public dataset. Our novel SICS-105 dataset involved 105 patients recruited at Sankara Eye Hospital in India. Performance is evaluated with frame-wise accuracy, edit distance, F1-score, Precision-Recall AUC, sensitivity, and specificity. The MS-TCN++ architecture performs better on the Cataract-101 dataset across reported metrics, with an accuracy of 89.97% [CI 86.69-93.46%] compared to 84.94% [CI 79.45-92.03%] on the SICS-105 dataset (ROC AUC 99.10% [98.34-99.51%] vs. 98.22% [97.17-99.39%]). Reducing the 20 phases to 13 phase in SICS improved performance without completely bridging the gap. The per-video accuracy distribution and confidence-intervals overlap between the samples. PR-AUC curves for each phase in the SICS dataset range from 46.20 to 94.18%. In conclusion, we replicated phase recognition results from phacoemulsification in a new open-access SICS dataset using DL with slightly lower prediction performance. This research marks a crucial step towards improving postoperative analysis and training for SICS. |
| Document Type: |
moving image (video) |
| Language: |
unknown |
| Relation: |
https://zenodo.org/records/13847781; oai:zenodo.org:13847781; https://doi.org/10.5281/zenodo.13847781 |
| DOI: |
10.5281/zenodo.13847781 |
| Availability: |
https://doi.org/10.5281/zenodo.13847781; https://zenodo.org/records/13847781 |
| Rights: |
Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode |
| Accession Number: |
edsbas.5AA125C6 |
| Database: |
BASE |