Biocybernetics and Biomedical Engineering最新文献

{"title":"A systematic review of artificial neural network techniques for analysis of foot plantar pressure","authors":"Chongguang Wang ,&nbsp;Kerrie Evans ,&nbsp;Dean Hartley ,&nbsp;Scott Morrison ,&nbsp;Martin Veidt ,&nbsp;Gui Wang","doi":"10.1016/j.bbe.2024.01.005","DOIUrl":"https://doi.org/10.1016/j.bbe.2024.01.005","url":null,"abstract":"<div><p>Plantar pressure distribution offers insights into foot function, gait mechanics, and foot-related issues. This systematic review presents an analysis of the use of artificial neural network techniques in the context of plantar pressure analysis. 60 studies were included in the review. Sample size, pathology, pressure sensor number, data collection device, utilization of other sensor devices, ground-truth methods, pre-processing dataset, neural network type, and evaluation metrics were evaluated. Utilization of customized wearable footwear devices for the acquisition of data was common amongst both healthy participants and patients. Inertial measurement units emerged as an effective compensatory measure to address the limitations associated with the distribution of plantar pressure. Ground truth methods predominantly relied on the usage of both annotations and reference devices. Multilayer perceptron, convolutional neural networks, and recurrent neural networks were identified as the most frequently employed artificial neural network algorithms across the reviewed studies. Finally, the evaluation of performance largely drew upon statistical descriptions and other machine learning methods. This review provides a comprehensive understanding of the use of artificial neural network techniques in plantar pressure analysis, highlighting opportunities for future research.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 197-208"},"PeriodicalIF":6.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521624000056/pdfft?md5=9b7f95a54d7af9620bb2a34de80b906f&pid=1-s2.0-S0208521624000056-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139674820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disruptions in brain functional connectivity: The hidden risk for oxygen-intolerant professional divers in simulated deep water","authors":"Emanuela Formaggio ,&nbsp;Lucio Pastena ,&nbsp;Massimo Melucci ,&nbsp;Lucio Ricciardi ,&nbsp;Silvia Francesca Storti","doi":"10.1016/j.bbe.2024.01.004","DOIUrl":"https://doi.org/10.1016/j.bbe.2024.01.004","url":null,"abstract":"<div><p>In this study, we investigated the effects of oxygen toxicity on brain activity and functional connectivity (FC) in divers using a closed-circuit oxygen breathing apparatus. We acquired and analyzed electroencephalographic (EEG) signals from a group of normal professional divers (PD) and a group that developed oxygen intolerance, i.e., oxygen-intolerant professional divers (OPD), to evaluate the potential risk of a dive and understand the physiological mechanisms involved. The results highlighted a significant difference in the baseline levels of <span><math><mi>α</mi></math></span> rhythm between PD and OPD, with PD exhibiting a lower level to counteract the effects of increased <span><math><msub><mrow><mi>O</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> inhalation, while OPD showed a higher level that resulted in a pathological state. Connectivity analysis revealed a strong correlation between cognitive and motor regions, and high levels of <span><math><mi>α</mi></math></span> synchronization at rest in OPDs. Our findings suggest that a pathological condition may underlie the higher <span><math><mi>α</mi></math></span> levels observed in these individuals when facing the stress of high <span><math><msub><mrow><mi>O</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> inhalation. These findings support the hypothesis that oxygen modulates brain networks, and have important implications for understanding the neural mechanisms involved in oxygen toxicity. The study also provides a unique opportunity to investigate the impact of neurophysiological activity in simulated critical scenarios, and opens up new perspectives in the screening and monitoring of divers.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 209-217"},"PeriodicalIF":6.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521624000044/pdfft?md5=5edb8ca083818f99257fa9753df94806&pid=1-s2.0-S0208521624000044-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139709621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Video-based HR measurement using adaptive facial regions with multiple color spaces","authors":"Arpita Panigrahi ,&nbsp;Hemant Sharma ,&nbsp;Atin Mukherjee","doi":"10.1016/j.bbe.2023.12.001","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.12.001","url":null,"abstract":"<div><p><span><span><span>Driven by the desire for feasible and convenient healthcare, non-contact heart rate (HR) monitoring based on consumer-grade cameras has gained significant recognition among researchers. However, this technology suffers from performance reliability and consistency in realistic situations of motion artifacts, illumination variations, and skin tones, limiting it to emerge as an alternative to conventional methods. Considering these challenges, this paper suggests an effective technique for HR measurement from facial </span>RGB<span> videos. The face being the region of interest (ROI) is divided into several small sub-ROIs of even size. A group of quality sub-ROIs is formed and weighted based on the fundamental periodicity coefficient to handle spatial non-uniform illumination and facial motions. Five different color spaces are considered, and the most suitable color component from each space is chosen to alleviate the influence of temporal illumination variation and other factors. The resultant color signals are denoised using the ensemble empirical mode decomposition and integrated using the </span></span>principal component analysis to derive a pulsating component representing the blood </span>volumetric<span> changes for HR computation. Experiments are conducted over three standard datasets, namely PURE, UBFC, and COHFACE. The obtained mean absolute error values are 1.16 beats per minute (bpm), 1.56 bpm, and 2.10 bpm for PURE, UBFC, and COHFACE datasets, respectively, indicating the performance of the technique well above the clinically acceptable threshold. In comparison, the technique showed performance superiority over the state-of-art methods. These outcomes substantiate the potential of alternative color spaces for accurate and reliable HR monitoring from facial videos in challenging scenarios.</span></p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 68-82"},"PeriodicalIF":6.4,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139050378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Insulin resistance: Risk factors, diagnostic approaches and mathematical models for clinical practice, epidemiological studies, and beyond","authors":"Janusz Krzymien ,&nbsp;Piotr Ladyzynski","doi":"10.1016/j.bbe.2023.12.004","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.12.004","url":null,"abstract":"<div><p><span>Insulin resistance (IR) is a multifactorial metabolic disorder associated with the development of cardiometabolic syndrome, cardiovascular diseases and obesity. Factors such as inflammation, </span>hyperinsulinemia<span>, hyperglucagonemia, mitochondrial dysfunction, glucotoxicity and lipotoxicity<span><span> contribute to the development of IR. Despite being extensively studied for over 60 years, assessing the incidence of IR, developing effective prevention strategies, and implementing appropriate therapeutic approaches remain challenging. This review explores the multifaceted nature of IR, including its association with various conditions such as obesity, primary hypertension, dyslipidemia, obstructive sleep apnea, </span>Alzheimer's disease<span>, non-alcoholic fatty liver disease, polycystic ovary syndrome, chronic kidney disease and cancer. Additionally, we discuss the complexity of diagnosing and quantifying IR, emphasizing the lack of absolute, common criteria for classification. We delve into the use of mathematical models<span> in clinical and epidemiological studies<span>, focusing on the choice between insulin, triglycerides, or waist-to-hip ratio as IR determinants. Furthermore, we highlight the importance of reliable input data and caution in interpreting results when utilizing mathematical models for IR assessment. This narrative review aims to provide insights into the challenges and considerations involved in conducting IR diagnostics, with implications for clinical practice, epidemiological research, and future advancements in this field.</span></span></span></span></span></p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 55-67"},"PeriodicalIF":6.4,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139050400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid lightweight breast cancer classification framework using the histopathological images","authors":"Daniel Addo ,&nbsp;Shijie Zhou ,&nbsp;Kwabena Sarpong ,&nbsp;Obed T. Nartey ,&nbsp;Muhammed A. Abdullah ,&nbsp;Chiagoziem C. Ukwuoma ,&nbsp;Mugahed A. Al-antari","doi":"10.1016/j.bbe.2023.12.003","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.12.003","url":null,"abstract":"<div><p>A crucial element in the diagnosis of breast cancer is the utilization of a classification method that is efficient, lightweight, and precise. Convolutional neural networks (CNNs) have garnered attention as a viable approach for classifying histopathological images. However, deeper and wider models tend to rely on first-order statistics, demanding substantial computational resources and struggling with fixed kernel dimensions that limit encompassing diverse resolution data, thereby degrading the model’s performance during testing. This study introduces BCHI-CovNet, a novel lightweight artificial intelligence (AI) model for histopathological breast image classification. Firstly, a novel multiscale depth-wise separable convolution is proposed. It is introduced to split input tensors into distinct tensor fragments, each subject to unique kernel sizes integrating various kernel sizes within one depth-wise convolution to capture both low- and high-resolution patterns. Secondly, an additional pooling module is introduced to capture extensive second-order statistical information across the channels and spatial dimensions. This module works in tandem with an innovative multi-head self-attention mechanism to capture the long-range pixels contributing significantly to the learning process, yielding distinctive and discriminative features that further enrich representation and introduce pixel diversity during training. These novel designs substantially reduce computational complexities regarding model parameters and FLOPs, which is crucial for resource-constrained medical devices. The outcomes achieved by employing the suggested model on two openly accessible datasets for breast cancer histopathological images reveal noteworthy performance. Specifically, the proposed approach attains high levels of accuracy: 99.15 % at 40× magnification, 99.08 % at 100× magnification, 99.22 % at 200× magnification, and 98.87 % at 400× magnification on the BreaKHis dataset. Additionally, it achieves an accuracy of 99.38 % on the BACH dataset. These results highlight the exceptional effectiveness and practical promise of BCHI-CovNet for the classification of breast cancer histopathological images.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 31-54"},"PeriodicalIF":6.4,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139033249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differentiating age and sex in vertebral body CT scans – Texture analysis versus deep learning approach","authors":"Karolina Nurzynska ,&nbsp;Adam Piórkowski ,&nbsp;Michał Strzelecki ,&nbsp;Marcin Kociołek ,&nbsp;Robert Paweł Banyś ,&nbsp;Rafał Obuchowicz","doi":"10.1016/j.bbe.2023.11.002","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.11.002","url":null,"abstract":"<div><p><span><span>The automated analysis of computed tomography<span> (CT) scans of vertebrae, for the purpose of determining an individual's age and sex constitutes a vital area of research. Accurate assessment of bone age in children facilitates the monitoring of their growth and development. Moreover, the determination of both age and sex has significant relevance in various legal contexts involving human remains. We have built a dataset comprising CT scans of vertebral bodies from 166 patients of diverse genders, acquired during routine cardiac examinations. These images were rescaled to 8-bit data, and textural features were computed using the qMaZda software. The results were analysed employing conventional </span></span>machine learning techniques and deep convolutional networks. The regression model, developed for the automatic estimation of bone age, accurately determined patients' ages, with a </span>mean absolute error of 3.14 years and R2 = 0.79. In the context of classifying patient gender through textural analysis supported by machine learning, we achieved an accuracy of 69 %. However, the application of deep convolutional networks for this task yielded a slightly lower accuracy of 59 %.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 20-30"},"PeriodicalIF":6.4,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138558885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hollow fiber bioreactor with genetically modified hepatic cells as a model of biologically active function block of the bioartificial liver","authors":"Malgorzata Jakubowska ,&nbsp;Monika Joanna Wisniewska ,&nbsp;Agnieszka Wencel ,&nbsp;Cezary Wojciechowski ,&nbsp;Monika Gora ,&nbsp;Krzysztof Dudek ,&nbsp;Andrzej Chwojnowski ,&nbsp;Beata Burzynska ,&nbsp;Dorota Genowefa Pijanowska ,&nbsp;Krzysztof Dariusz Pluta","doi":"10.1016/j.bbe.2023.11.003","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.11.003","url":null,"abstract":"<div><p>Chronic liver disease and cirrhosis, that can lead to liver failure, are major public health issues, with liver transplantation as the only effective treatment. However, the limited availability of transplantable organs has spurred research into alternative therapies, including bioartificial livers. To date, liver hybrid support devices, using porcine hepatocytes or hepatoma-derived cell lines, have failed to demonstrate efficacy in clinical trials.</p><p>Here, for the first time, we report the construction of a model of biologically active function block of bioartificial liver based on a hollow fiber bioreactor populated with genetically modified hepatic cells. For comprehensive comparison the culturing of hepatic cells was carried out in both static and dynamic conditions in a medium that flowed through porous polysulfone capillaries. The most crucial parameters, such as cell viability, glucose consumption, albumin secretion and urea production, were analyzed in static conditions while glucose usage and albumin production were compared in dynamic cell cultures. This model has the potential to improve the development of bioartificial liver devices and contribute to the treatment of patients with impaired liver function.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 9-19"},"PeriodicalIF":6.4,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521623000621/pdfft?md5=03a6bb7b322d5ddb29c988d0991b1104&pid=1-s2.0-S0208521623000621-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138549545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"System and approach to detecting of gastric slow wave and environmental noise suppression based on optically pumped magnetometer","authors":"Shuang Liang ,&nbsp;Kexin Gao ,&nbsp;Junhuai He ,&nbsp;Yikang Jia ,&nbsp;Hongchen Jiao ,&nbsp;Lishuang Feng","doi":"10.1016/j.bbe.2023.11.004","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.11.004","url":null,"abstract":"<div><p><span>Gastric slow waves (SWs) are commonly used for the quantitative assessment of gastric functional disorders. Compared with surface electrogastrography, using of magnetic signals to record SWs can achieve higher-quality signal recording. In this study, we discovered that optically pumped </span>magnetometers<span><span> (OPM) based on the spin exchange relaxation-free method have comparable weak magnetic detection capabilities to superconducting quantum interference devices but without liquid helium cooling. However, owing to the inevitable interference of low-frequency environmental drift, the characteristic features of SW are obscured, greatly increasing the difficulty in detecting gastric magnetic signals. Therefore, in this study, we constructed an OPM Magnetogastrography (OPM-MGG). We proposed an </span>adaptive filtering<span><span> architecture combined with environmental drift suppression and a non-stationary signal decomposition method<span> for extracting SW signals. Through controlled human experiments, the results demonstrated that our testing system successfully extracted SW signals in the frequency range of 2–4 cycles per minute. The extracted SW signals exhibited consistent power and time–frequency characteristics with the reported results. This study validates the feasibility of (1) using the OPM-MGG system for capturing SW signals and (2) the proposed processing strategies for identifying ultralow-frequency SW signals. In conclusion, the OPM-MGG system and the signal extraction strategies developed in this study have the potential to provide a wearable technology for bioweak </span></span>magnetic field measurements, offering new opportunities for both research and clinical applications.</span></span></p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 1-8"},"PeriodicalIF":6.4,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138490667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel deep learning-based approach for prediction of neonatal respiratory disorders from chest X-ray images","authors":"Ayse Erdogan Yildirim ,&nbsp;Murat Canayaz","doi":"10.1016/j.bbe.2023.08.004","DOIUrl":"10.1016/j.bbe.2023.08.004","url":null,"abstract":"<div><p><span>In recent years, many diseases can be diagnosed in a short time with the use of deep learning models in the field of medicine. Most of the studies in this area focus on adult or </span>pediatric patients<span><span><span>. However, deep learning studies for the diagnosis of diseases in neonatal are not sufficient. Also, since it is known that respiratory disorders such as pneumonia have a large place among the causes of neonatal death, early and accurate diagnosis of respiratory diseases in neonates is crucial. For this reason, our study aims to detect the presence of respiratory disorders through the developed deep-learning approach using chest X-ray images of patients hospitalized in the Neonatal </span>Intensive Care Unit. Accordingly, the enhanced version of C+EffxNet, the new hybrid deep learning model, is designed to predict respiratory disorders in neonates. In this version, the features selected by </span>PCA<span> are combined as 100, 200, and 300, then the binary classification process was carried out. In the study, the accuracy and kappa value were obtained as 0.965, and 0.904, respectively before feature merging, while these values were obtained as 0.977, and 0.935 after feature merging. This method, which was developed for the diagnosis of respiratory disorders in neonates, was also subsequently applied to a chest X-ray dataset that is frequently used in the literature for the diagnosis of pediatric pneumonia. For this data set, while the accuracy was 0.992, the kappa value was 0.982. The results obtained confirm the success of the proposed method for both datasets.</span></span></p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"43 4","pages":"Pages 635-655"},"PeriodicalIF":6.4,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49483878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surgical phase classification and operative skill assessment through spatial context aware CNNs and time-invariant feature extracting autoencoders","authors":"Chakka Sai Pradeep,&nbsp;Neelam Sinha","doi":"10.1016/j.bbe.2023.10.001","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.10.001","url":null,"abstract":"<div><p>Automated surgical video analysis promises improved healthcare. We propose novel spatial context aware combined loss function for end-to-end Encoder-Decoder training for Surgical Phase Classification (SPC) on laparoscopic cholecystectomy (LC) videos. Proposed loss function leverages on fine-grained class activation maps obtained from fused multi-layer Layer-CAM for supervised learning of SPC, obtaining improved Layer-CAM explanations. Post classification, we introduce graph theory to incorporate known hierarchies of surgical phases. We report peak SPC accuracy of 96.16%, precision of 94.08% and recall of 90.02% on public dataset Cholec80, with 7 phases. Our proposed method utilizes just 73.5% of parameters as against existing state-of-the-art methodology, achieving improvement of 0.5% in accuracy, 1.76% in precision with comparable recall, with an order less standard deviation. We also propose DNN based surgical skill assessment methodology. This approach utilizes surgical phase prediction scores from the final fully-connected layer of spatial-context aware classifier to form multi-channel temporal signal of surgical phases. Time-invariant representation is obtained from this temporal signal through time- and frequency-domain analyses. Autoencoder based time-invariant features are utilized for reconstruction and identification of prominent peaks in dissimilarity curves. We devise a surgical skill measure (SSM) based on spatial-context aware temporal-prominence-of-peaks curve. SSM values are expected to be high when executed skillfully, aligning with expert assessed GOALS metric. We illustrate this trend on Cholec80 and m2cai16-tool datasets, in comparison with GOALS metric. Concurrence in the trend of SSM with respect to GOALS metric is obtained on these test videos, making it a promising step towards automated surgical skill assessment.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"43 4","pages":"Pages 700-724"},"PeriodicalIF":6.4,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49761151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}