Development and validation of a deep‐learning based assistance system for enhancing laparoscopic control level

Zheng, Qingyuan; Yang, Rui; Yang, Song; Ni, Xinmiao; Li, Yanze; Jiang, Zhengyu; Wang, Xinyu; Wang, Lei; Chen, Zhiyuan; Liu, Xiuheng

doi:10.1002/rcs.2449

Cited by 2 publications

(1 citation statement)

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“…This technology, in turn, contributes to accelerating the patient's recovery process [6,7]. Additionally, MIRS provides the surgeon with more precise visualisation and control, which can lead to a significant improvement in surgical outcomes [8,9].…”

Section: Introductionmentioning

confidence: 99%

Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions

Urrutia,

Espejo,

Evens

et al. 2023

Sensors

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This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signals to understand tissue behaviour better and build upon prior research. This study is divided into three key stages: feature extraction using the Cepstrum Transform (CT), Mel-Frequency Cepstral Coefficients (MFCCs), and Fast Chirplet Transform (FCT); dimensionality reduction employing techniques such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP); and, finally, classification using a nearest neighbours classifier. The results demonstrate that using feature extraction techniques, especially the combination of CT and MFCC with dimensionality reduction algorithms, yields highly efficient outcomes. The classification metrics (Accuracy, Recall, and F1-score) approach 99%, and the clustering metric is 0.61. The performance of the CT–UMAP combination stands out in the evaluation metrics.

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