Medical & Biological Engineering & Computing最新文献_第2页

An arbitrary waveform neurostimulator for preclinical studies: design and verification.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-12 DOI: 10.1007/s11517-024-03241-6

Hipolito Guzman-Miranda, Alejandro Barriga-Rivera

{"title":"An arbitrary waveform neurostimulator for preclinical studies: design and verification.","authors":"Hipolito Guzman-Miranda, Alejandro Barriga-Rivera","doi":"10.1007/s11517-024-03241-6","DOIUrl":"https://doi.org/10.1007/s11517-024-03241-6","url":null,"abstract":"Neural electrostimulation has enabled different therapies to treat a number of health problems. For example, the cochlear implant allows for recovering the hearing function and deep brain electrostimulation has been proved to reduce tremor in Parkinson's disease. Other approaches such as retinal prostheses are progressing rapidly, as researchers continue to investigate new strategies to activate targeted neurons more precisely. The use of arbitrary current waveform electrosimulation is a promising technique that allows exploiting the differences that exist among different neural types to enable preferential activation. This work presents a two-channel arbitrary waveform neurostimulator designed for visual prosthetics research. A field programmable gate array (FPGA) was employed to control and generate voltage waveforms via digital-to-analog converters. Voltage waveforms were then electrically isolated and converted to current waveforms using a modified Howland amplifier. Shorting of the electrodes was provided using multiplexers. The FPGA gateware was verified to a high level of confidence using a transaction-level modeled testbench, achieving a line coverage of 91.4%. The complete system was tested in saline using silver electrodes with diameters from 200 to 1000 µm. The bandwidth obtained was 30 kHz with voltage compliance ± 15 V. The neurostimulator can be easily scaled up using the provided in/out trigger ports and adapted to other applications with minor modifications.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting 30-day reoperation following primary total knee arthroplasty: machine learning model outperforms the ACS risk calculator.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-09 DOI: 10.1007/s11517-024-03258-x

Tony Lin-Wei Chen, Anirudh Buddhiraju, Blake M Bacevich, Henry Hojoon Seo, Michelle Riyo Shimizu, Young-Min Kwon

{"title":"Predicting 30-day reoperation following primary total knee arthroplasty: machine learning model outperforms the ACS risk calculator.","authors":"Tony Lin-Wei Chen, Anirudh Buddhiraju, Blake M Bacevich, Henry Hojoon Seo, Michelle Riyo Shimizu, Young-Min Kwon","doi":"10.1007/s11517-024-03258-x","DOIUrl":"https://doi.org/10.1007/s11517-024-03258-x","url":null,"abstract":"The ACS risk calculator (ARC) has proven less effective in predicting patient-specific risk of early reoperation after primary total knee arthroplasty (TKA), compromising care quality and cost efficiency. This study compared the performance of a machine learning (ML) model and ARC in predicting 30-day reoperation after primary TKA using a national-scale dataset. Data of 366,151 TKAs were acquired from the ACS-NSQIP database. A random forest model was derived using ARC build-in parameters from the training dataset via techniques of hyperparameter optimization and cross-validation. The predictive performance of random forest and ARC was evaluated by metrics of discrimination, calibration, and clinical utility using the testing dataset. The ML model demonstrated good discrimination and calibration (AUC: 0.72, slope: 1.18, intercept: - 0.14, Brier score: 0.012), outperforming ARC in all metrics (AUC: 0.51, slope: - 0.01, intercept: 0.01, Brier score: 0.135) including clinical utility measured by decision curve analyses. Age (> 67 years) and BMI (> 34 kg/m2) were the important predictors of reoperation. This study suggests the superiority of ML models in identifying individualized 30-day reoperation risk following TKA. ML models may be an adjunct prediction tool in enhancing patient-specific risk stratification and postoperative care management.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142802873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mesothelin expression prediction in pancreatic cancer based on multimodal stochastic configuration networks.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-06 DOI: 10.1007/s11517-024-03253-2

Junjie Li, Xuanle Li, Yingge Chen, Yunling Wang, Binjie Wang, Xuefeng Zhang, Na Zhang

{"title":"Mesothelin expression prediction in pancreatic cancer based on multimodal stochastic configuration networks.","authors":"Junjie Li, Xuanle Li, Yingge Chen, Yunling Wang, Binjie Wang, Xuefeng Zhang, Na Zhang","doi":"10.1007/s11517-024-03253-2","DOIUrl":"https://doi.org/10.1007/s11517-024-03253-2","url":null,"abstract":"Predicting tumor biomarkers with high precision is essential for improving the diagnostic accuracy and developing more effective treatment strategies. This paper proposes a machine learning model that utilizes CT images and biopsy whole slide images (WSI) to classify mesothelin expression levels in pancreatic cancer. By combining multimodal learning and stochastic configuration networks, a radiopathomics mesothelin-prediction system named RPMSNet is developed. The system extracts radiomic and pathomic features from CT images and WSI, respectively, and sends them into stochastic configuration networks for the final prediction. Compared to traditional radiomics or pathomics, this system has the capability to capture more comprehensive image features, providing a multidimensional insight into tissue characteristics. The experiments and analyses demonstrate the accuracy and effectiveness of the system, with an area under the curve of 81.03%, an accuracy of 73.67%, a sensitivity of 71.54%, a precision of 76.78%, and a F1-score of 72.61%, surpassing both single-modality and dual-modality models. RPMSNet highlights its potential for early diagnosis and personalized treatment in precision medicine.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and feasibility study of a piezoresistive pressure sensor-based automated system for monitoring and controlling gastric pressure in endoscopy.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-03 DOI: 10.1007/s11517-024-03254-1

Sukgyu Koh, Sungwan Kim

{"title":"Development and feasibility study of a piezoresistive pressure sensor-based automated system for monitoring and controlling gastric pressure in endoscopy.","authors":"Sukgyu Koh, Sungwan Kim","doi":"10.1007/s11517-024-03254-1","DOIUrl":"https://doi.org/10.1007/s11517-024-03254-1","url":null,"abstract":"Maintaining precise intragastric pressure during gastrointestinal endoscopy is critical for patient safety and diagnostic accuracy, yet current methods relying on manual adjustments pose risks of improper insufflation. This study aimed to develop an automated gastric pressure control system for flexible endoscopy, addressing these challenges with a piezoresistive pressure sensor that can be integrated into a 7.3 mm diameter flexible endoscope. The system, incorporating air and suction pumps controlled by a microcontroller, was calibrated in an acrylic chamber and validated through comprehensive testing in both an endoscopy simulator and a porcine specimen. Testing scenarios included normal breathing, coughing, belching, and combined events, assessing accuracy, stability, and real-time pressure regulation under conditions mimicking physiological responses. Results demonstrated high accuracy (R2 = 0.9999), minimal bias (0.23 mmHg), and strong agreement with reference standards, confirming effective pressure management. Simulated clinical scenarios in simulator and porcine specimen further showed the system's ability to maintain target pressure with minimal errors, indicating robustness under dynamic conditions. These findings suggest that the automated pressure control system significantly improves safety and procedural efficiency in endoscopy, with potential applicability to other minimally invasive procedures. Further animal model testing is recommended to validate the clinical performance under realistic physiological conditions.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deployment and validation of a smart bed architecture for untethered patients with wireless biomonitoring stickers. 利用无线生物监测贴纸，部署和验证用于无牵挂病人的智能床结构。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-22 DOI: 10.1007/s11517-024-03155-3

Tânia Nunes, Luís Gaspar, José N Faria, David Portugal, Telmo Lopes, Pedro Fernandes, Mahmoud Tavakoli

{"title":"Deployment and validation of a smart bed architecture for untethered patients with wireless biomonitoring stickers.","authors":"Tânia Nunes, Luís Gaspar, José N Faria, David Portugal, Telmo Lopes, Pedro Fernandes, Mahmoud Tavakoli","doi":"10.1007/s11517-024-03155-3","DOIUrl":"10.1007/s11517-024-03155-3","url":null,"abstract":"Conventional patient monitoring in healthcare has limitations such as delayed identification of deteriorating conditions, disruptions to patient routines, and discomfort due to extensive wiring for bed-bound patients. To address these, we have recently developed an innovative IoT-based healthcare system for real-time wireless patient monitoring. This system includes a flexible epidermal patch that collects vital signs using low power electronics and transmits the data to IoT nodes in hospital beds. The nodes connect to a smart gateway that aggregates the information and interfaces with the hospital information system (HIS), facilitating the exchange of electronic health records (EHR) and enhancing access to patient vital signs for healthcare professionals. Our study validates the proposed smart bed architecture in a clinical setting, assessing its ability to meet healthcare personnel needs, patient comfort, and data transmission reliability. Technical performance assessment involves analyzing key performance indicators for communication across various interfaces, including the wearable device and the smart box, and the link between the gateway and the HIS. Also, a comparative analysis is conducted on data from our architecture and traditional hospital equipment. Usability evaluation involves questionnaires completed by patients and healthcare professionals. Results demonstrate the robustness of the architecture proposed, exhibiting reliable and efficient information flow, while offering significant improvements in patient monitoring over conventional wired methods, including unrestricted mobility and improved comfort to enhance healthcare delivery.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3815-3840"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141735514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques. 电容式心电图监测系统中的运动伪影：现有模型和减少技术综述。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-20 DOI: 10.1007/s11517-024-03165-1

Matin Khalili, Hamid GholamHosseini, Andrew Lowe, Matthew M Y Kuo

{"title":"Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques.","authors":"Matin Khalili, Hamid GholamHosseini, Andrew Lowe, Matthew M Y Kuo","doi":"10.1007/s11517-024-03165-1","DOIUrl":"10.1007/s11517-024-03165-1","url":null,"abstract":"Current research focuses on improving electrocardiogram (ECG) monitoring systems to enable real-time and long-term usage, with a specific focus on facilitating remote monitoring of ECG data. This advancement is crucial for improving cardiovascular health by facilitating early detection and management of cardiovascular disease (CVD). To efficiently meet these demands, user-friendly and comfortable ECG sensors that surpass wet electrodes are essential. This has led to increased interest in ECG capacitive electrodes, which facilitate signal detection without requiring gel preparation or direct conductive contact with the body. This feature makes them suitable for wearables or integrated measurement devices. However, ongoing research is essential as the signals they measure often lack sufficient clinical accuracy due to susceptibility to interferences, particularly Motion Artifacts (MAs). While our primary focus is on studying MAs, we also address other limitations crucial for designing a high Signal-to-Noise Ratio (SNR) circuit and effectively mitigating MAs. The literature on the origins and models of MAs in capacitive electrodes is insufficient, which we aim to address alongside discussing mitigation methods. We bring attention to digital signal processing approaches, especially those using reference signals like Electrode-Tissue Impedance (ETI), as highly promising. Finally, we discuss its challenges, proposed solutions, and offer insights into future research directions.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3599-3622"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11568998/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smartphone videos-driven musculoskeletal multibody dynamics modelling workflow to estimate the lower limb joint contact forces and ground reaction forces. 智能手机视频驱动的肌肉骨骼多体动力学建模工作流程，用于估算下肢关节接触力和地面反作用力。

IF 4.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-24 DOI: 10.1007/s11517-024-03171-3

Yinghu Peng, Wei Wang, Lin Wang, Hao Zhou, Zhenxian Chen, Qida Zhang, Guanglin Li

{"title":"Smartphone videos-driven musculoskeletal multibody dynamics modelling workflow to estimate the lower limb joint contact forces and ground reaction forces.","authors":"Yinghu Peng, Wei Wang, Lin Wang, Hao Zhou, Zhenxian Chen, Qida Zhang, Guanglin Li","doi":"10.1007/s11517-024-03171-3","DOIUrl":"10.1007/s11517-024-03171-3","url":null,"abstract":"The estimation of joint contact forces in musculoskeletal multibody dynamics models typically requires the use of expensive and time-consuming technologies, such as reflective marker-based motion capture (Mocap) system. In this study, we aim to propose a more accessible and cost-effective solution that utilizes the dual smartphone videos (SPV)-driven musculoskeletal multibody dynamics modeling workflow to estimate the lower limb mechanics. Twelve participants were recruited to collect marker trajectory data, force plate data, and motion videos during walking and running. The smartphone videos were initially analyzed using the OpenCap platform to identify key joint points and anatomical markers. The markers were used as inputs for the musculoskeletal multibody dynamics model to calculate the lower limb joint kinematics, joint contact forces, and ground reaction forces, which were then evaluated by the Mocap-based workflow. The root mean square error (RMSE), mean absolute deviation (MAD), and Pearson correlation coefficient (ρ) were adopted to evaluate the results. Excellent or strong Pearson correlations were observed in most lower limb joint angles (ρ = 0.74 ~ 0.94). The averaged MADs and RMSEs for the joint angles were 1.93 ~ 6.56° and 2.14 ~ 7.08°, respectively. Excellent or strong Pearson correlations were observed in most lower limb joint contact forces and ground reaction forces (ρ = 0.78 ~ 0.92). The averaged MADs and RMSEs for the joint lower limb joint contact forces were 0.18 ~ 1.07 bodyweight (BW) and 0.28 ~ 1.32 BW, respectively. Overall, the proposed smartphone video-driven musculoskeletal multibody dynamics simulation workflow demonstrated reliable accuracy in predicting lower limb mechanics and ground reaction forces, which has the potential to expedite gait dynamics analysis in a clinical setting.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3841-3853"},"PeriodicalIF":4.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration. ConKeD：基于关键点的视网膜图像配准的多视角对比描述学习。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-06 DOI: 10.1007/s11517-024-03160-6

David Rivas-Villar, Álvaro S Hervella, José Rouco, Jorge Novo

{"title":"ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration.","authors":"David Rivas-Villar, Álvaro S Hervella, José Rouco, Jorge Novo","doi":"10.1007/s11517-024-03160-6","DOIUrl":"10.1007/s11517-024-03160-6","url":null,"abstract":"Retinal image registration is of utmost importance due to its wide applications in medical practice. In this context, we propose ConKeD, a novel deep learning approach to learn descriptors for retinal image registration. In contrast to current registration methods, our approach employs a novel multi-positive multi-negative contrastive learning strategy that enables the utilization of additional information from the available training samples. This makes it possible to learn high-quality descriptors from limited training data. To train and evaluate ConKeD, we combine these descriptors with domain-specific keypoints, particularly blood vessel bifurcations and crossovers, that are detected using a deep neural network. Our experimental results demonstrate the benefits of the novel multi-positive multi-negative strategy, as it outperforms the widely used triplet loss technique (single-positive and single-negative) as well as the single-positive multi-negative alternative. Additionally, the combination of ConKeD with the domain-specific keypoints produces comparable results to the state-of-the-art methods for retinal image registration, while offering important advantages such as avoiding pre-processing, utilizing fewer training samples, and requiring fewer detected keypoints, among others. Therefore, ConKeD shows a promising potential towards facilitating the development and application of deep learning-based methods for retinal image registration.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3721-3736"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11568994/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141538845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

G-T correcting: an improved training of image segmentation under noisy labels. G-T 校正：噪声标签下图像分割的改进训练。

IF 4.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-20 DOI: 10.1007/s11517-024-03170-4

Yun Gao, Junhu Fu, Yi Guo, Yuanyuan Wang

{"title":"G-T correcting: an improved training of image segmentation under noisy labels.","authors":"Yun Gao, Junhu Fu, Yi Guo, Yuanyuan Wang","doi":"10.1007/s11517-024-03170-4","DOIUrl":"10.1007/s11517-024-03170-4","url":null,"abstract":"Data-driven medical image segmentation networks require expert annotations, which are hard to obtain. Non-expert annotations are often used instead, but these can be inaccurate (referred to as \"noisy labels\"), misleading the network's training and causing a decline in segmentation performance. In this study, we focus on improving the segmentation performance of neural networks when trained with noisy annotations. Specifically, we propose a two-stage framework named \"G-T correcting,\" consisting of \"G\" stage for recognizing noisy labels and \"T\" stage for correcting noisy labels. In the \"G\" stage, a positive feedback method is proposed to automatically recognize noisy samples, using a Gaussian mixed model to classify clean and noisy labels through the per-sample loss histogram. In the \"T\" stage, a confident correcting strategy and early learning strategy are adopted to allow the segmentation network to receive productive guidance from noisy labels. Experiments on simulated and real-world noisy labels show that this method can achieve over 90% accuracy in recognizing noisy labels, and improve the network's DICE coefficient to 91%. The results demonstrate that the proposed method can enhance the segmentation performance of the network when trained with noisy labels, indicating good clinical application prospects.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3781-3799"},"PeriodicalIF":4.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Radiomics of pituitary adenoma using computer vision: a review. 利用计算机视觉对垂体腺瘤进行放射组学研究：综述。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-16 DOI: 10.1007/s11517-024-03163-3

Tomas Zilka, Wanda Benesova

{"title":"Radiomics of pituitary adenoma using computer vision: a review.","authors":"Tomas Zilka, Wanda Benesova","doi":"10.1007/s11517-024-03163-3","DOIUrl":"10.1007/s11517-024-03163-3","url":null,"abstract":"Pituitary adenomas (PA) represent the most common type of sellar neoplasm. Extracting relevant information from radiological images is essential for decision support in addressing various objectives related to PA. Given the critical need for an accurate assessment of the natural progression of PA, computer vision (CV) and artificial intelligence (AI) play a pivotal role in automatically extracting features from radiological images. The field of \"Radiomics\" involves the extraction of high-dimensional features, often referred to as \"Radiomic features,\" from digital radiological images. This survey offers an analysis of the current state of research in PA radiomics. Our work comprises a systematic review of 34 publications focused on PA radiomics and other automated information mining pertaining to PA through the analysis of radiological data using computer vision methods. We begin with a theoretical exploration essential for understanding the theoretical background of radionmics, encompassing traditional approaches from computer vision and machine learning, as well as the latest methodologies in deep radiomics utilizing deep learning (DL). Thirty-four research works under examination are comprehensively compared and evaluated. The overall results achieved in the analyzed papers are high, e.g., the best accuracy is up to 96% and the best achieved AUC is up to 0.99, which establishes optimism for the successful use of radiomic features. Methods based on deep learning seem to be the most promising for the future. In relation to this perspective DL methods, several challenges are remarkable: It is important to create high-quality and sufficiently extensive datasets necessary for training deep neural networks. Interpretability of deep radiomics is also a big open challenge. It is necessary to develop and verify methods that will explain to us how deep radiomic features reflect various physics-explainable aspects.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3581-3597"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11568991/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141621592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0