Publications by authors named "Nithya Bhasker"
Background: With Surgomics, we aim for personalized prediction of the patient's surgical outcome using machine-learning (ML) on multimodal intraoperative data to extract surgomic features as surgical process characteristics. As high-quality annotations by medical experts are crucial, but still a bottleneck, we prospectively investigate active learning (AL) to reduce annotation effort and present automatic recognition of surgomic features.
Methods: To establish a process for development of surgomic features, ten video-based features related to bleeding, as highly relevant intraoperative complication, were chosen.
Clinically relevant postoperative pancreatic fistula (CR-POPF) can significantly affect the treatment course and outcome in pancreatic cancer patients. Preoperative prediction of CR-POPF can aid the surgical decision-making process and lead to better perioperative management of patients. In this retrospective study of 108 pancreatic head resection patients, we present risk models for the prediction of CR-POPF that use combinations of preoperative computed tomography (CT)-based radiomic features, mesh-based volumes of annotated intra- and peripancreatic structures and preoperative clinical data.
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- Context-aware decision support in operating rooms enhances surgical safety and efficiency by utilizing real-time feedback from workflow analysis, but current methods often miss detailed interactions needed for effective AI assistance.
- The paper introduces CholecTriplet2021, a challenge aimed at recognizing surgical action triplets (instrument, verb, target) in laparoscopic videos, using the CholecT50 dataset annotated with such triplet information.
- It presents the challenge's setup, results from various deep learning methods (with mean average precision ranging from 4.2% to 38.1%), and proposes future research directions to improve fine-grained surgical activity recognition in the field of AI-assisted surgery.
Article Synopsis
- Surgomics is a new approach to personalized medicine that focuses on analyzing intraoperative surgical data using machine learning to improve individualized surgical care.
- A study identified 52 surgomic features from various data sources, with experts rating "surgical skill and quality of performance" as the most clinically relevant and "Instrument" as the most feasible to extract automatically.
- The findings suggest that integrating Surgomics with other preoperative data can enhance patient care by understanding the processes of surgery better and predicting outcomes more accurately.