Journal of Medical and Biological Engineering最新文献

{"title":"Attribute and Malignancy Analysis of Lung Nodule on Chest CT with Cause-and-Effect Logic","authors":"Hui Liu, Qingshan She, Jingchao Lin, Qiang Chen, Feng Fang, Yingchun Zhang","doi":"10.1007/s40846-024-00895-3","DOIUrl":"https://doi.org/10.1007/s40846-024-00895-3","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Lung cancer is the leading cause of cancer-related death. Early detection and treatment are crucial to improve survival rates. Radiologists determine whether the nodules are benign or malignant by observing their morphological attributes. However, this can be a challenging task for well-trained doctors.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>We propose a more efficient automatic lung nodule analysis method, which establishes a clear cause-and-effect logic relationship between attribute features and malignancy features by incorporating multiple instance learning (MIL). The designed MIL classifier aggregates the learned instance weights and corresponding attribute features to form malignancy features. Compared to existing methods, it starts by mirroring the way radiologists observe nodules, then proceeds to extract the multi-scale morphological attribute characteristics of the nodules. The instance weight also serves as the attribute score of the attribute, providing a reference for consultation.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Our method was validated using the LIDC-IDRI dataset and achieved an accuracy of 93.05% on benign-malignant classification task with the added capability of accurately scoring the attributes.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The proposed method based on attribute score regression and multi-instance learning establishes the causal relationship between attribute scores and malignancy. This method improves accuracy in nodule classification and addresses the issue of poor model interpretability.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215130","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}

{"title":"Enhancing Myocardial Infarction Diagnosis: LSTM-based Deep Learning Approach Integrating Echocardiographic Wall Motion Analysis","authors":"Hsu Thiri Soe, Hiroyasu Iwata","doi":"10.1007/s40846-024-00897-1","DOIUrl":"https://doi.org/10.1007/s40846-024-00897-1","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Owing to the increased mortality of heart diseases worldwide, especially myocardial infarction (MI), early detection is essential for improved diagnosis and treatment. The main purpose of this study is to develop a myocardial infarction detection method that combines deep learning and image processing, focusing on abnormalities in left ventricular (LV) wall motion.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>The proposed method primarily uses the LV wall motion movement as a feature to train an LSTM network for MI detection. LV wall motion annotated by expert cardiologists was used as the ground truth. Accuracy, sensitivity, specificity, and area under the curve (AUC) were used to evaluate model performance. The proposed method primarily uses LV wall motion as a feature, combined with LV size and image pixels, to improve diagnostic accuracy over existing computer-aided design (CAD) systems.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The LSTM model achieved the highest diagnostic performance when trained on a combination of LV wall motion, LV size, and image pixel features with an accuracy of 95%, sensitivity of 96%, specificity of 94%, and an AUC value of 0.98. The LSTM model significantly outperformed models trained on individual feature sets or conventional machine learning algorithms. The inclusion of LV wall motion analysis improved accuracy by 10% compared to using only LV size and pixel data.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>Our MI diagnosis system uses echocardiographic image analysis and LSTM-based deep learning to accurately detect LV wall motion issues related to MI. Compared with current CAD systems, the inclusion of LV wall motion analysis significantly improves diagnosis accuracy. The proposed system can help physicians detect MI early, thereby accelerating treatment and improving patient outcomes.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215129","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}

{"title":"Investigation of Image Quality for Cuboid and Tapered Array microPET Systems","authors":"Alireza Sadremomtaz, Payvand Taherparvar, Mohaddeseh Saber","doi":"10.1007/s40846-024-00890-8","DOIUrl":"https://doi.org/10.1007/s40846-024-00890-8","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Small animals are integral to medical research as they provide insights into human diseases. Mice, particularly suitable as human models, share gene functions, making them vital for biomedical research. Positron emission tomography (PET) has emerged as a key tool for non-invasive imaging of mouse models, providing molecular-level insights with remarkable sensitivity. Achieving optimal spatial resolution is crucial for capturing detailed images of small animal organs, and enhancing sensitivity is imperative for microPET scanner efficiency.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>This study investigates the performance of microPET scanners using cuboidal and tapered arrays, simulated by the GATE Monte Carlo package. To this end, it focuses on detector materials, crystal geometry, and reconstruction methods. Finally, critical parameters such as sensitivity, NECR, and FWHM of Gaussian fit of image intensity profiles are assessed.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Simulation outputs reveal that tapered arrays outperform their cuboid counterparts by 44% in sensitivity and NECR, along with a 22% improvement in spatial resolution. The relative FWHM difference for crystals compared to LSO remains below 5%. Crystal material significantly affects sensitivity and NECR, with BGO demonstrating 25% greater values than LSO. Meanwhile, GSO and LYSO showed 32% and 60% lower values, respectively. BGO crystal demonstrated a higher profile amplitude, indicating higher counts. This difference could be attributed more to heightened noise than an increase in signal, as BGO crystals exhibit a higher scatter fraction than other crystals. Furthermore, COSEM and ACOSEM algorithms achieve the minimum FWHM of 0.7 mm, suggesting 10% better spatial resolution than the OSEM algorithm. In contrast, RAMLA and MRAMLA algorithms showed 14% and 4% worse spatial resolution than the OSEM algorithm, respectively.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>Tapered arrays, especially when paired with BGO crystals, demonstrate superior sensitivity, NECR, and lower FWHM suggesting better spatial resolution than cuboid arrays. Crystal material choice minimally affects FWHM for a low-activity point source but significantly influences sensitivity and NECR, with BGO outperforming other crystals. COSEM and ACOSEM reconstruction algorithms yielded better image quality with lower FWHM and noise, demonstrating their effectiveness in microPET applications.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215131","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}

{"title":"Exosomes Derived from Irradiated-Prostate Cancer Cells Promote Cancer Progression","authors":"Chien‑Chih Ke, Chikondi Jassi, Chih-Hung Chuang, Chiung-Yuan Ko, Shu-Pin Huang, Shih-Hsun Kuo, Chia-Yang Li, Ya‑Ju Hsieh","doi":"10.1007/s40846-024-00888-2","DOIUrl":"https://doi.org/10.1007/s40846-024-00888-2","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Radiotherapy (RT) is a commonly employed therapeutic strategy for the treatment of localized cancers, including prostate cancer (PCa). Despite significant advancements in radiotherapy technology over recent years, high recurrence and metastasis of PCa after RT remain critical challenges. Various mechanisms have been implicated in how cancer evades radiotherapy, and exosomes, a type of extracellular vesicles (EVs) has recently been identified as one of the contributing factors. This study aimed to investigate whether exosomes derived from irradiated PCa cells are involved in the cancer progression and to identify possible key factor in this process.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Exosomes were isolated from irradiated or non-irradiated PCa cell lines (designated as Rad-Exo or Exo) and characterized by specific marker expression, morphology and size. PCa cells treated with Rad-Exo or Exo were analyzed for the effects of proliferation, specific gene expression, migration and cancer stem cell property. Differential protein expression in Rad-Exo and Exo were carried out by mass spectrometry.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Results showed that, compared to Exo, Rad-Exo treatment inhibited cell proliferation but significantly promoted migration and elevated the expression of genes related to epithelial to mesenchymal transition. Additionally, cells treated with Rad-Exo showed increased expression of genes related to cancer stem cells. Mass spectrometry identified POTEE as more abundant within Rad-Exo then in Exo, and its expression was confirmed to be elevated in PCa cells following irradiation. Furthermore, POTEE expression increased in cells after Rad-Exo treatment.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>This study suggests that exosomes derived from irradiated PCa cells may function as a driver of cancer progression, including recurrent or metastatic cancer. Also, exosomal POTEE may serve as a potential target for future therapeutic or diagnostic investigations in prostate cancer.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215157","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}

{"title":"Subject-Specific Session-to-Session Transfer Learning Strategies for Increasing Brain-Computer Interface Performance during Upper Extremity Neurorehabilitation in Stroke","authors":"Ruben I. Carino-Escobar, Luis A. Franceschi-Jimenez, Paul Carrillo-Mora, Jessica Cantillo-Negrete","doi":"10.1007/s40846-024-00891-7","DOIUrl":"https://doi.org/10.1007/s40846-024-00891-7","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>To assess if transfer learning strategies can improve stroke patients’ ability to control a Brain-computer interface (BCI) based on motor intention across an upper extremity neurorehabilitation intervention.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Three subject-specific session-to-session training strategies were retrospectively assessed in the present study, using information acquired during a BCI intervention in 12 stroke patients. One strategy used data from the previous therapy session (previous session), another used data from all previous sessions (accumulative) and another initially used previous session’s data and was updated with data acquired during the current session (instantaneous).</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Classification accuracy was significantly higher with the instantaneous strategy (median = 76.4%, IQR = [68.7%, 81.5%]) compared to the obtained with the accumulative (71.67%, [65.1%, 78.5%]) and previous session (69.2%, [59%, 77.4%]) strategies. Median classification accuracies across sessions were also higher with the instantaneous strategy in each BCI intervention session.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The instantaneous strategy could allow stroke patients to achieve a competitive level of BCI performance during a motor intention BCI intervention without reducing effective therapy time or requiring data from other patients.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215158","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}

{"title":"Towards Improving the Saccade Angle Recognition Using the Sensitivity Weights of Channels","authors":"Jianning Hua, Qinghua Luo, Lintao Xu, Bowei Hu, Ziping Chen","doi":"10.1007/s40846-024-00894-4","DOIUrl":"https://doi.org/10.1007/s40846-024-00894-4","url":null,"abstract":"","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920971","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}

{"title":"Automatic Segmentation of Intracranial Hemorrhage in Computed Tomography Scans with Convolution Neural Networks","authors":"Weijing Xu, Z. Sha, Tao Tan, Wentao Liu, Yifu Chen, Zhanying Li, Xipeng Pan, Rongcai Jiang, Huihua Yang","doi":"10.1007/s40846-024-00892-6","DOIUrl":"https://doi.org/10.1007/s40846-024-00892-6","url":null,"abstract":"","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928030","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}

{"title":"Ultrasound Delta CBE Imaging: A New Approach Based on Local Energy Subtraction to Localization of the HIFU Focal Spot Using Changes in Backscattered Energy","authors":"Kun Yang, Qiang Li, Xiaowei Zhou, Chiao-Yin Wang, Po-Hsiang Tsui","doi":"10.1007/s40846-024-00887-3","DOIUrl":"https://doi.org/10.1007/s40846-024-00887-3","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>High-intensity focused ultrasound (HIFU) is a promising non-invasive technique for thermal ablation of tumors. Positioning the focal point of the HIFU accurately prior to the procedure is crucial to the success of the treatment. A change in backscattered energy (CBE) in ultrasound images has been shown to allow visualization of thermal information and can be used to locate the focal spot of HIFU prior to ablation. In CBE imaging, however, tailing artifacts may exist below the focal point of HIFU to hinder the identification of the HIFU focal spot.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>This study proposed ultrasound delta CBE (DCBE) imaging that reduces CBE artifacts by local energy subtraction between measured and the reference envelope images. Phantom experiments were performed for validation of the proposed method. A HIFU system operating at a frequency of 2.12 MHz was used to heat phantoms, which were imaged with a clinical ultrasound scanner equipped with a 3-MHz convex transducer for analysis of CBE and DCBE data.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The results showed that the DCBE value increases monotonically with temperature (correlation coefficient = 0.90). Particularly, DCBE imaging can identify the HIFU focal spot, suppress tailing artifacts, and increase the contrast between the focal and artifact zones by 8 dB in comparison with conventional CBE imaging.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>Based on this study, DCBE imaging may be an effective method of locating HIFU focal points through ultrasound backscattered energy.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935015","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}

{"title":"Enhancing Two-Year Recurrence-Free Survival Prediction in Non-Small Cell Lung Cancer (NSCLC) Patients Using Tumor-Centric Attention Network (TCA-Net)","authors":"Hye Ryun Kim, Gahee Ahn, Helen Hong, Bong-Seog Kim","doi":"10.1007/s40846-024-00884-6","DOIUrl":"https://doi.org/10.1007/s40846-024-00884-6","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Accurate predictions of postoperative recurrence are essential for determining appropriate follow-up treatments after surgery, as patients with non-small-cell lung cancer (NSCLC) at the same clinical stage have different recurrence incidences. However, simple convolutional neural network (CNN)-based methods are limited when presented with tumors of various sizes. This study aims to predict two-year recurrence-free survival precisely in patients with tumors of various sizes on preoperative CT images using what is termed a Tumor-Centric Attention Network (TCA-Net).</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>The proposed network features dual branches, each with an identical architecture but distinct weights to extract diverse features from CT images and tumor masks simultaneously. The tumor-centric attention module integrates two disparate feature maps at each level to amplify the characteristics of the tumor. All feature maps are concatenated with the finest resolution, enabling the extraction and integration of comprehensive multi-scale features for the complex tumor environment.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>TCA-Net showed an accuracy of 75%, balanced accuracy of 75.05%, specificity of 76.16% and an AUC value of 0.78. These results represent more balanced accuracies by 4.76% and 2.58% compared to ResNet-18 with CT images and dual ResNet-18s with CT images and tumor masks, respectively. Specifically, TCA-Net demonstrated a substantial improvement in the small-sized tumor group, achieving a balanced accuracy of 81.32%, sensitivity of 85.71%, and specificity of 76.92%.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>TCA-Net improved the prediction performance of two-year recurrence-free survival on average across tumors of all sizes, with significant improvements, especially for small-sized tumors.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935017","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}

{"title":"Artificial Intelligence as a Tool for Diagnosis of Cardiac Amyloidosis: A Systematic Review","authors":"Armia Ahmadi-Hadad, Egle De Rosa, Luigi Di Serafino, Giovanni Esposito","doi":"10.1007/s40846-024-00893-5","DOIUrl":"https://doi.org/10.1007/s40846-024-00893-5","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Cardiac amyloidosis (CA) is a highly underdiagnosed disease characterized by the accumulation of misfolded amyloid protein fragments in the heart, resulting in reduced heart functionality and myocardial stiffness. Artificial intelligence (AI) has garnered considerable interest as a potential tool for diagnosing cardiovascular diseases, including CA. This systematic review concentrates on the application of AI in the diagnosis of CA.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>A comprehensive systematic search was performed on the databases of PubMed, Embase, and Medline, to identify relevant studies. The screening process was conducted in two stages, using predetermined inclusion and exclusion criteria, and was carried out in a blinded manner. In cases where discrepancies arose, the reviewers discussed and resolved the issue through consensus.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Following the screening process, a total of 10 studies were deemed eligible for inclusion in this review. These investigations evaluated the potential utility of AI models that analyzed routine laboratory data, medical records, ECG, transthoracic echocardiography, CMR, and WBS in the diagnosis of CA.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>AI models have demonstrated utility as a diagnostic tool for CA, with comparable or in one case superior efficacy to that of expert cardiologists.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935016","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}