TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks

Automatic surgical phase recognition is a challenging and crucial task with the potential to improve patient safety and become an integral part of intra-operative decision-support systems. In this paper, we propose, for the first time in workflow analysis, a Multi-Stage Temporal Convolutional Network (MS-TCN) that performs hierarchical prediction refinement for surgical phase recognition. Causal, dilated convolutions allow for a large receptive field and online inference with smooth predictions even during ambiguous transitions. Our method is thoroughly evaluated on two datasets of laparoscopic cholecystectomy videos with and without the use of additional surgical tool information. Outperforming various state-of-the-art LSTM approaches, we verify the suitability of the proposed causal MS-TCN for surgical phase recognition.Comment: 10 pages, 2 figure

GLSFormer: Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos

Automated surgical step recognition is an important task that can significantly improve patient safety and decision-making during surgeries. Existing state-of-the-art methods for surgical step recognition either rely on separate, multi-stage modeling of spatial and temporal information or operate on short-range temporal resolution when learned jointly. However, the benefits of joint modeling of spatio-temporal features and long-range information are not taken in account. In this paper, we propose a vision transformer-based approach to jointly learn spatio-temporal features directly from sequence of frame-level patches. Our method incorporates a gated-temporal attention mechanism that intelligently combines short-term and long-term spatio-temporal feature representations. We extensively evaluate our approach on two cataract surgery video datasets, namely Cataract-101 and D99, and demonstrate superior performance compared to various state-of-the-art methods. These results validate the suitability of our proposed approach for automated surgical step recognition. Our code is released at: https://github.com/nisargshah1999/GLSFormerComment: Accepted to MICCAI 2023 (Early Accept

Advancing herbal medicine: enhancing product quality and safety through robust quality control practices

This manuscript provides an in-depth review of the significance of quality control in herbal medication products, focusing on its role in maintaining efficiency and safety. With a historical foundation in traditional medicine systems, herbal remedies have gained widespread popularity as natural alternatives to conventional treatments. However, the increasing demand for these products necessitates stringent quality control measures to ensure consistency and safety. This comprehensive review explores the importance of quality control methods in monitoring various aspects of herbal product development, manufacturing, and distribution. Emphasizing the need for standardized processes, the manuscript delves into the detection and prevention of contaminants, the authentication of herbal ingredients, and the adherence to regulatory standards. Additionally, it highlights the integration of traditional knowledge and modern scientific approaches in achieving optimal quality control outcomes. By emphasizing the role of quality control in herbal medicine, this manuscript contributes to promoting consumer trust, safeguarding public health, and fostering the responsible use of herbal medication products

Offline identification of surgical deviations in laparoscopic rectopexy

International audienceObjective: According to a meta-analysis of 7 studies, the median number of patients with at least one adverse event during the surgery is 14.4%, and a third of those adverse events were preventable. The occurrence of adverse events forces surgeons to implement corrective strategies and, thus, deviate from the standard surgical process. Therefore, it is clear that the automatic identification of adverse events is a major challenge for patient safety. In this paper, we have proposed a method enabling us to identify such deviations. We have focused on identifying surgeons’ deviations from standard surgical processes due to surgical events rather than anatomic specificities. This is particularly challenging, given the high variability in typical surgical procedure workflows.Methods: We have introduced a new approach designed to automatically detect and distinguish surgical process deviations based on multi-dimensional non-linear temporal scaling with a hidden semi-Markov model using manual annotation of surgical processes. The approach was then evaluated using cross-validation.Results: The best results have over 90% accuracy. Recall and precision for event deviations, i.e. related to adverse events, are respectively below 80% and 40%. To understand these results, we have provided a detailed analysis of the incorrectly-detected observations.Conclusion: Multi-dimensional non-linear temporal scaling with a hidden semi-Markov model provides promising results for detecting deviations. Our error analysis of the incorrectly-detected observations offers different leads in order to further improve our method.Significance: Our method demonstrated the feasibility of automatically detecting surgical deviations that could be implemented for both skill analysis and developing situation awareness-based computer-assisted surgical system