Phase recognition in manual Small-Incision cataract surgery with MS-TCN + + on the novel SICS-105 dataset.

Manual Small-Incision Cataract Surgery (SICS) is a prevalent technique in low- and middle-income countries (LMICs) but understudied with respect to computer assisted surgery. This prospective cross-sectional study introduces the first SICS video dataset, 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, with an accuracy of 89.97% [CI 86.69-93.46%] compared to 85.56% [80.63-92.09%] on the SICS-105 dataset (ROC AUC 99.10% [98.34-99.51%] vs. 98.22% [97.16-99.26%]). The accuracy distribution and confidence-intervals overlap and the ROC AUC values range 46.20 to 94.18%. Even though DL is found to be effective for phase recognition in SICS, the larger number of phases and longer duration makes it more challenging compared to phacoemulsification. To support further developments, we make our dataset open access. This research marks a crucial step towards improving postoperative analysis and training for SICS.