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Speech-Based Surgical Phase Recognition for Non-Intrusive Surgical Skills’ Assessment in Educational Contexts

机译：基于语音的外科手术阶段识别非侵入式外科技能在教育背景下的评估

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摘要

Surgeons’ procedural skills and intraoperative decision making are key elements of clinical practice. However, the objective assessment of these skills remains a challenge to this day. Surgical workflow analysis (SWA) is emerging as a powerful tool to solve this issue in surgical educational environments in real time. Typically, SWA makes use of video signals to automatically identify the surgical phase. We hypothesize that the analysis of surgeons’ speech using natural language processing (NLP) can provide deeper insight into the surgical decision-making processes. As a preliminary step, this study proposes to use audio signals registered in the educational operating room (OR) to classify the phases of a laparoscopic cholecystectomy (LC). To do this, we firstly created a database with the transcriptions of audio recorded in surgical educational environments and their corresponding phase. Secondly, we compared the performance of four feature extraction techniques and four machine learning models to find the most appropriate model for phase recognition. The best resulting model was a support vector machine (SVM) coupled to a hidden-Markov model (HMM), trained with features obtained with Word2Vec (82.95% average accuracy). The analysis of this model’s confusion matrix shows that some phrases are misplaced due to the similarity in the words used. The study of the model’s temporal component suggests that further attention should be paid to accurately detect surgeons’ normal conversation. This study proves that speech-based classification of LC phases can be effectively achieved. This lays the foundation for the use of audio signals for SWA, to create a framework of LC to be used in surgical training, especially for the training and assessment of procedural and decision-making skills (e.g., to assess residents’ procedural knowledge and their ability to react to adverse situations).
机译：外科医生的程序技能和术中决策是临床实践的关键要素。然而，对这些技能的客观评估仍然是这一天的挑战。外科工作流程分析（SWA）是一个强大的工具，实时解决手术教育环境中的这个问题。通常，SWA利用视频信号自动识别外科阶段。我们假设使用自然语言处理（NLP）的外科医生的演讲分析可以提供更深入的洞察外科决策过程。作为初步步骤，本研究建议使用在教育手术室（或）中注册的音频信号来分类腹腔镜胆囊切除术（LC）的阶段。为此，我们首先创建了一个数据库，其中包含在外科教育环境中记录的音频转录及其相应的阶段。其次，我们比较了四种特征提取技术和四台机器学习模型的性能，以找到最合适的阶段识别模型。最好的结果模型是耦合到隐藏马尔可夫模型（HMM）的支持向量机（SVM），培训具有用Word2VEC（平均精度82.95％）获得的特征训练。对该模型的混乱矩阵的分析表明，由于所用词的相似性，一些短语是错误的。模型的颞部件的研究表明，应进一步关注以准确地检测外科医生的正常谈话。本研究证明，可以有效地实现基于语音的LC阶段的分类。这为SWA提供了音频信号的基础，以创建LC的框架，以用于手术培训，特别是对于培训和评估程序和决策技能（例如，以评估居民的程序知识及其来说对不利情况做出反应的能力）。

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