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}.