Current Medical Imaging Reviews最新文献

{"title":"Advanced Lung Disease Detection: CBAM-Augmented, Lightweight EfficientNetB2 with Visual Insights.","authors":"A Beena Godbin, S Graceline Jasmine","doi":"10.2174/0115734056344651241023070250","DOIUrl":"https://doi.org/10.2174/0115734056344651241023070250","url":null,"abstract":"<p><strong>Introduction: </strong>This paper presents a multichannel deep-learning method for detecting lung diseases using chest X-ray images. Using EfficientNetB0 through EfficientNetB7 pretrained models, the methodology offers improved performance in classifying COVID-19, viral pneumonia, and normal chest Xrays.</p><p><strong>Methods: </strong>The EfficientNetB2 model was customized by incorporating Squeeze-and-Excitation (SE) blocks and the Convolutional Block Attention Module (CBAM) to improve the model's attention mechanisms. Additional convolutional layers were added for improved feature extraction, and multiscale feature fusion was implemented to capture features at different scales.</p><p><strong>Results: </strong>In this study, 99.3% of the unseen chest X-ray images were identified using the proposed model. It demonstrated superior performance, surpassing existing techniques and highlighting its robustness and generalizability on unseen data samples.</p><p><strong>Conclusion: </strong>Moreover, visualization techniques were used to inspect the intermediate layers of the model, providing deeper insights into its processing and interpretation of medical images. The proposed method offers healthcare radiologists a valuable tool for rapid and accurate point-of-care diagnoses.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683571","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":"Multiple Pulmonary Sclerosing Haemangiomas with a Cavity: A Case Report and Review of the Literature.","authors":"Yan Li, Fangbiao Zhang, Zhijun Wu, Yan Wu","doi":"10.2174/0115734056338122241018042107","DOIUrl":"https://doi.org/10.2174/0115734056338122241018042107","url":null,"abstract":"<p><strong>Objective: </strong>Pulmonary sclerosing haemangioma (PSH) is a relatively uncommon benign neoplasm that is often asymptomatic and predominantly affects young and middle-aged females. PSH often appears as a single nodule, whereas multiple lesions with a cavity are relatively rare and easily misdiagnosed.</p><p><strong>Case presentation: </strong>In our study, we report a patient with separated nodules in the same lobe with a cavity and clinical manifestations of cough and sputum with a radiographic presentation similar to that of tuberculosis. The patient underwent percutaneous lung biopsy and thoracoscopic partial pneumonectomy and was diagnosed with multiple PSHs.</p><p><strong>Conclusion: </strong>We report a rare case of multiple PSHs that were treated with a thoracoscopic partial resection of the left upper lobe. Postoperative pathology confirmed multiple PSHs. Due to the rarity of PSH, it is easily misdiagnosed in clinical practice as lung cancer, tuberculosis, or other diseases. The final diagnosis depends on the pathology, and surgery is considered to be an appropriate treatment that leads to a good prognosis.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548919","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":"Combination of Different Sectional Elastography Techniques with Age to Optimize the Downgrading of Breast BI-RAIDS Class 4a Nodules.","authors":"Xianxian Jiang, Le-Yuan Chen, Juan Li, Fang-Yuan Chen, Nian-An He, Xian-Jun Ye","doi":"10.2174/0115734056307595240911075111","DOIUrl":"https://doi.org/10.2174/0115734056307595240911075111","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to optimize the downgrading of BI-RADS class 4a nodules by combining various sectional elastography techniques with age.</p><p><strong>Materials and methods: </strong>We performed conventional ultrasonography, strain elastography (SE), and shear wave elastography (SWE) on patients. Quantitative parameters recorded included age, cross-sectional and longitudinal area ratios (C-EI/B, L-EI/B), strain rate ratios (C-SR, L-SR), overall average elastic modulus values (C-Emean1, L-Emean1), five-point average elastic modulus values (C-Emean2, L-Emean2), and maximum elastic modulus values (C-Emax, L-Emax).</p><p><strong>Results: </strong>Histopathological evaluations showed that out of 230 lesions, 45 were malignant, and 185 were benign. The sensitivity and specificity of conventional ultrasonography were 100% and 0%, respectively. In contrast, SE and SWE exhibited higher specificity but lower sensitivity. Crosssectional parameters (C-EI/B, C-SR, C-Emean1, C-Emean2, and C-Emax) outperformed their longitudinal counterparts, with C-SR and C-Emax showing the highest specificity (72.43% and 73.51%) and satisfactory sensitivity (80.00% and 88.89%). Combining age with C-SR and C-Emax significantly improved diagnostic efficiency, achieving a sensitivity of 97.78% and a specificity of 77.30%.</p><p><strong>Conclusion: </strong>Integrating age with C-SR and C-Emax effectively reduces unnecessary biopsies for most BI-RADS 4a benign lesions while maintaining a very low misdiagnosis rate.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367424","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":"An Integrated Approach using YOLOv8 and ResNet, SeResNet & Vision Transformer (ViT) Algorithms based on ROI Fracture Prediction in X-ray Images of the Elbow.","authors":"Taukir Alam, Wei-Cheng Yeh, Fang Rong Hsu, Wei-Chung Shia, Robert Singh, Taimoor Hassan, Wenru Lin, Hong-Ye Yang, Tahir Hussain","doi":"10.2174/0115734056309890240912054616","DOIUrl":"https://doi.org/10.2174/0115734056309890240912054616","url":null,"abstract":"<p><strong>Introduction: </strong>In this study, we harnessed three cutting-edge algorithms' capabilities to refine the elbow fracture prediction process through X-ray image analysis. Employing the YOLOv8 (You only look once) algorithm, we first identified Regions of Interest (ROI) within the X-ray images, significantly augmenting fracture prediction accuracy.</p><p><strong>Methods: </strong>Subsequently, we integrated and compared the ResNet, the SeResNet (Squeeze-and-Excitation Residual Network) ViT (Vision Transformer) algorithms to refine our predictive capabilities. Furthermore, to ensure optimal precision, we implemented a series of meticulous refinements. This included recalibrating ROI regions to enable finer-grained identification of diagnostically significant areas within the X-ray images. Additionally, advanced image enhancement techniques were applied to optimize the X-ray images' visual quality and structural clarity.</p><p><strong>Results: </strong>These methodological enhancements synergistically contributed to a substantial improvement in the overall accuracy of our fracture predictions. The dataset utilized for training, testing & validation, and comprehensive evaluation exclusively comprised elbow X-ray images, where predicting the fracture with three algorithms: Resnet50; accuracy 0.97, precision 1, recall 0.95, SeResnet50; accuracy 0.97, precision 1, recall 0.95 & ViTB- 16 with high accuracy of 0.99, precision same as the other two algorithms, with a recall of 0.95.</p><p><strong>Conclusion: </strong>This approach has the potential to increase the precision of diagnoses, lessen the burden of radiologists, easily integrate into current medical imaging systems, and assist clinical decision-making, all of which could lead to better patient care and health outcomes overall.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367423","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":"Prenatal Three-Dimensional Ultrasound Diagnosis of Dural Sinus Arteriovenous Malformation: An Unusual Case Report.","authors":"Li Qiu, Huizhu Chen, Ni Chen, Hong Luo","doi":"10.2174/0115734056310875240918043257","DOIUrl":"https://doi.org/10.2174/0115734056310875240918043257","url":null,"abstract":"<p><strong>Background: </strong>Dural sinus arteriovenous malformation is an uncommon intracranial vascular malformation. The affected cases may suffer from severe neurological injury. Prenatal ultrasound has been used to diagnose fetal intracranial vascular abnormality, but prenatal three-dimensional (3D) ultrasound presents a very rare anomaly; an arteriovenous malformation of the dural sinus has not been reported.</p><p><strong>Objective: </strong>This study aimed to emphasize the diagnostic value of 3D ultrasound in the fetus with dural sinus arteriovenous malformation.</p><p><strong>Case presentation: </strong>A 38-year-old woman was referred for targeted fetal ultrasonography at 37 weeks of gestation due to an ultrasound that showed a cystic lesion in the posterior cranial fossa. The fetus demonstrated obvious dilatation of the torcular herophili, bilateral transverse sinuses, and bilateral sigmoid sinuses, appearing as a novel bull's horn sign on 3D ultrasound. After birth, cerebral angiography confirmed the diagnosis of dural arteriovenous fistula (DAVF) in the occipital sinus region.</p><p><strong>Conclusion: </strong>3D ultrasound is an appealing method for prenatal diagnosis of dural sinus arteriovenous malformation.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367426","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":"A Novel Invasive Weed Optimization and its Variant for the Detection of Polycystic Ovary Syndrome.","authors":"R Saranya","doi":"10.2174/0115734056307615240823074030","DOIUrl":"https://doi.org/10.2174/0115734056307615240823074030","url":null,"abstract":"<p><strong>Introduction: </strong>This study intends to provide a novel Invasive Weed Optimization (IWO) algorithm for the detection of Polycystic Ovary Syndrome (PCOS) from ultrasound ovarian images. PCOS is an intricate anarchy described by hyperandrogenemia and irregular menstruation. Indian women are increasingly finding reproductive disorders, namely PCOS.</p><p><strong>Methods: </strong>The women having PCOS grow more small follicles in their ovaries. The radiologists take a look into women's ovaries by use of ultrasound scanning equipment to manually count the number of follicles and their size for fertility treatment. These may lead to error diagnosis.</p><p><strong>Results: </strong>This paper proposed an automatic follicle detection system for identifying PCOS in the ovary using IWO. The performance of IWO is improved in Modified Invasive Weed Optimization (MIWO). This algorithm imitates the biological weeds' behavior. The MIWO is employed to obtain the optimal threshold by maximizing the between-class variance of the modified Otsu method. The efficiency of the proposed method has been compared with the well-known optimization technique called Particle Swarm Optimization (PSO) and with IWO.</p><p><strong>Conclusion: </strong>Experimental results proved that the MIWO finds an optimal threshold higher than that of IWO and PSO.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300789","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":"Segmentation Synergy with a Dual U-Net and Federated Learning with CNNRF Models for Enhanced Brain Tumor Analysis.","authors":"Vinay Kukreja, Ayush Dogra, Satvik Vats, Bhawna Goyal, Shiva Mehta, Rajesh Kumar Kaushal","doi":"10.2174/0115734056312765240905104112","DOIUrl":"https://doi.org/10.2174/0115734056312765240905104112","url":null,"abstract":"<p><strong>Background: </strong>Brain tumours represent a diagnostic challenge, especially in the imaging area, where the differentiation of normal and pathologic tissues should be precise. The use of up-to-date machine learning techniques would be of great help in terms of brain tumor identification accuracy from MRI data. Objective This research paper aims to check the efficiency of a federated learning method that joins two classifiers, such as convolutional neural networks (CNNs) and random forests (R.F.F.), with dual U-Net segmentation for federated learning. This procedure benefits the image identification task on preprocessed MRI scan pictures that have already been categorized.</p><p><strong>Methods: </strong>In addition to using a variety of datasets, federated learning was utilized to train the CNN-RF model while taking data privacy into account. The processed MRI images with Median, Gaussian, and Wiener filters are used to filter out the noise level and make the feature extraction process easy and efficient. The surgical part used a dual U-Net layout, and the performance assessment was based on precision, recall, F1-score, and accuracy.</p><p><strong>Results: </strong>The model achieved excellent classification performance on local datasets as CRPs were high, from 91.28% to 95.52% for macro, micro, and weighted averages. Throughout the process of federated averaging, the collective model outperformed by reaching 97% accuracy compared to those of 99%, which were subjected to different clients. The correctness of how data is used helps the federated averaging method convert individual model insights into a consistent global model while keeping all personal data private.</p><p><strong>Conclusion: </strong>The combined structure of the federated learning framework, CNN-RF hybrid model, and dual U-Net segmentation is a robust and privacypreserving approach for identifying MRI images from brain tumors. The results of the present study exhibited that the technique is promising in improving the quality of brain tumor categorization and provides a pathway for practical utilization in clinical settings.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300795","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":"Bilateral Symmetrical Mandibular Canines with Two Roots and Two Separate Canals: A Case Report and Literature Review.","authors":"Qiushi Zhang, Xiaohong Ran, Ying Zhao, Kaiqi Qin, Yifan Zhang, Jing Cui, Yanwei Yang","doi":"10.2174/0115734056317207240829101457","DOIUrl":"https://doi.org/10.2174/0115734056317207240829101457","url":null,"abstract":"<p><strong>Background: </strong>The permanent canine usually has a single root and a single root canal. A one-rooted canine with two canals or a canine with two roots and two separate canals may also occur at a lower incidence in the permanent dentition. However, bilateral symmetrical mandibular canines with two roots and two separate canals are less common.</p><p><strong>Case presentation: </strong>This study reported a lower incidence case of bilateral symmetrical mandibular canines with two roots and two separate canals, which was found based on a CBCT examinaton. The patient visited our department and was consulted for orthodontic treatment due to the irregularity of her lower anterior teeth. As the abnormal root morphology of bilateral mandibular canines greatly increased the difficulty of orthodontic treatment, the patient finally gave up orthodontic treatment after communication.</p><p><strong>Conclusion: </strong>This case report provides supplementary data to better understand the complexities of the root canal system of canines.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300790","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":"Multimodal Deep Learning Network for Differentiating Between Benign and Malignant Pulmonary Ground Glass Nodules.","authors":"Gang Liu, Fei Liu, Xu Mao, Xiaoting Xie, Jingyao Sang, Husai Ma, Haiyun Yang, Hui He","doi":"10.2174/0115734056301741240903072017","DOIUrl":"https://doi.org/10.2174/0115734056301741240903072017","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to establish a multimodal deep-learning network model to enhance the diagnosis of benign and malignant pulmonary ground glass nodules [GGNs].</p><p><strong>Methods: </strong>Retrospective data on pulmonary GGNs were collected from multiple centers across China, including North, Northeast, Northwest, South, and Southwest China. The data were divided into a training set and a validation set in an 8:2 ratio. In addition, a GGN dataset was also obtained from our hospital database and used as the test set. All patients underwent chest computed tomography [CT], and the final diagnosis of the nodules was based on postoperative pathological reports. The Residual Network [ResNet] was used to extract imaging data, the Word2Vec method for semantic information extraction, and the Self Attention method for combining imaging features and patient data to construct a multimodal classification model. Then, the diagnostic efficiency of the proposed multimodal model was compared with that of existing ResNet and VGG models and radiologists.</p><p><strong>Results: </strong>The multicenter dataset comprised 1020 GGNs, including 265 benign and 755 malignant nodules, and the test dataset comprised 204 GGNs, with 67 benign and 137 malignant nodules. In the validation set, the proposed multimodal model achieved an accuracy of 90.2%, a sensitivity of 96.6%, and a specificity of 75.0%, which surpassed that of the VGG [73.1%, 76.7%, and 66.5%] and ResNet [78.0%, 83.3%, and 65.8%] models in diagnosing benign and malignant nodules. In the test set, the multimodal model accurately diagnosed 125 [91.18%] malignant nodules, outperforming radiologists [80.37% accuracy]. Moreover, the multimodal model correctly identified 54 [accuracy, 80.70%] benign nodules, compared to radiologists' accuracy of 85.47%. The consistency test comparing radiologists' diagnostic results with the multimodal model's results in relation to postoperative pathology showed strong agreement, with the multimodal model demonstrating closer alignment with gold standard pathological findings [Kappa=0.720, P<0.01].</p><p><strong>Conclusion: </strong>The multimodal deep learning network model exhibited promising diagnostic effectiveness in distinguishing benign and malignant GGNs and, therefore, holds potential as a reference tool to assist radiologists in improving the diagnostic accuracy of GGNs, potentially enhancing their work efficiency in clinical settings.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300794","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":"Solitary Fibrous Tumors: A Rare Tumor Arising from Ubiquitous Anatomical Locations.","authors":"İlhan Hekimsoy, Mertcan Erdoğan, Ezgi Güler, Selen Bayraktaroğlu","doi":"10.2174/0115734056315183240827051940","DOIUrl":"https://doi.org/10.2174/0115734056315183240827051940","url":null,"abstract":"<p><p>Solitary Fibrous Tumors (SFTs) are uncommon mesenchymal tumors of fibroblastic/myofibroblastic origin that stem from various anatomical sites. Most SFTs are asymptomatic and noticed incidentally by various imaging modalities. Although SFTs were initially identified in the pleura and erroneously considered to originate solely from serosal layers, extrapleural SFTs have been reported more commonly than their pleural counterparts. Imaging features are similar in different anatomical locations and are mainly related to the tumor's size and collagen content, characteristically displaying low signal intensity on Magnetic Resonance Imaging (MRI). Smaller tumors typically exhibit uniform enhancement, yet necrotic regions may become evident as the tumor size increases, resulting in heterogeneous enhancement. Less than 30% of SFTs demonstrate unfavorable clinical outcomes regardless of their histological features, warranting surgery as the preferred treatment with long-term follow-up. In this article, we have reviewed the clinical manifestations and imaging features of SFTs, discussed their differential diagnosis based on anatomical site, and provided diagnostic pearls.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142156604","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}