SuPRA: Surgical Phase Recognition and Anticipation for Intra-Operative
  Planning
Maxence Boels Yang Liu Prokar Dasgupta Alejandro Granados Sebastien Ourselin
Intra-operative recognition of surgical phases holds significant potential
for enhancing real-time contextual awareness in the operating room. However, we
argue that online recognition, while beneficial, primarily lends itself to
post-operative video analysis due to its limited direct impact on the actual
surgical decisions and actions during ongoing procedures. In contrast, we
contend that the prediction and anticipation of surgical phases are inherently
more valuable for intra-operative assistance, as they can meaningfully
influence a surgeon's immediate and long-term planning by providing foresight
into future steps. To address this gap, we propose a dual approach that
simultaneously recognises the current surgical phase and predicts upcoming
ones, thus offering comprehensive intra-operative assistance and guidance on
the expected remaining workflow. Our novel method, Surgical Phase Recognition
and Anticipation (SuPRA), leverages past and current information for accurate
intra-operative phase recognition while using future segments for phase
prediction. This unified approach challenges conventional frameworks that treat
these objectives separately. We have validated SuPRA on two reputed datasets,
Cholec80 and AutoLaparo21, where it demonstrated state-of-the-art performance
with recognition accuracies of 91.8% and 79.3%, respectively. Additionally, we
introduce and evaluate our model using new segment-level evaluation metrics,
namely Edit and F1 Overlap scores, for a more temporal assessment of segment
classification. In conclusion, SuPRA presents a new multi-task approach that
paves the way for improved intra-operative assistance through surgical phase
recognition and prediction of future events.