Current Medical Imaging Reviews最新文献

{"title":"A Robust Approach to Early Glaucoma Identification from Retinal Fundus Images using Dirichlet-based Weighted Average Ensemble and Bayesian Optimization.","authors":"Mohamed Mouhafid, Yatong Zhou, Chunyan Shan, Zhitao Xiao","doi":"10.2174/0115734056335762250128095107","DOIUrl":"https://doi.org/10.2174/0115734056335762250128095107","url":null,"abstract":"<p><strong>Objective: </strong>Glaucoma is a leading cause of irreversible visual impairment and blindness worldwide, primarily linked to increased intraocular pressure (IOP). Early detection is essential to prevent further visual impairment, yet the manual diagnosis of retinal fundus images (RFIs) is both time-consuming and inefficient. Although automated methods for glaucoma detection (GD) exist, they often rely on individual models with manually optimized hyperparameters. This study aims to address these limitations by proposing an ensemble-based approach that integrates multiple deep learning (DL) models with automated hyperparameter optimization, with the goal of improving diagnostic accuracy and enhancing model generalization for practical clinical applications.</p><p><strong>Materials and methods: </strong>The RFIs used in this study were sourced from two publicly available datasets (ACRIMA and ORIGA), consisting of a total of 1,355 images for GD. Our method combines a custom Multi-branch convolutional neural network (CNN), pretrained MobileNet, and DenseNet201 to extract complementary features from RFIs. Moreover, to optimize model performance, we apply Bayesian Optimization (BO) for automated hyperparameter tuning, eliminating the need for manual adjustments. The predictions from these models are then combined using a Dirichlet-based Weighted Average Ensemble (Dirichlet-WAE), which adaptively adjusts the weight of each model based on its performance.</p><p><strong>Results: </strong>The proposed ensemble model demonstrated state-of-the-art performance, achieving an accuracy (ACC) of 95.09%, precision (PREC) of 95.51%, sensitivity (SEN) of 94.55%, an F1-score (F1) of 94.94%, and an area under the curve (AUC) of 0.9854. The innovative Dirichlet-based WAE substantially reduced the false positive rate, enhancing diagnostic reliability for GD. When compared to individual models, the ensemble method consistently outperformed across all evaluation metrics, underscoring its robustness and potential scalability for clinical applications.</p><p><strong>Conclusion: </strong>The integration of ensemble learning (EL) and advanced optimization techniques significantly improved the ACC of GD in RFIs. The enhanced WAE method proved to be a critical factor in achieving well-balanced and highly accurate diagnostic performance, underscoring the importance of EL in medical diagnosis.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525119","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":"Application Value of A Clinical Radiomic Nomogram for Identifying Diabetic Nephropathy and Nondiabetic Renal Disease.","authors":"Xiaoling Liu, Weihan Xiao, Jing Qiao, Xiachuan Qin","doi":"10.2174/0115734056332507250210105723","DOIUrl":"https://doi.org/10.2174/0115734056332507250210105723","url":null,"abstract":"<p><strong>Objective: </strong>An ultrasound-based radiomics Machine Learning Model (ML) was utilized to assess non-invasively the conditions of diabetic nephropathy and non-diabetic renal disease in diabetic patients.</p><p><strong>Methods: </strong>A retrospective examination was conducted on 166 diabetic patients who had undergone renal biopsies guided by ultrasound, with the group comprising 114 individuals diagnosed with diabetic nephropathy and 52 with non-diabetic renal disease. The participants were randomly divided into the training set and the testing set (7:3). Following the extraction of radiomics features from the renal ultrasound images, a univariate analysis was conducted, and the Least Absolute Shrinkage And Selection Operator (LASSO) algorithm was applied to select the most significant features. Three ML algorithms were applied to construct the prediction models. Subsequently, the patients' clinical characteristics were evaluated through both univariate and multivariate logistic regression analyses, which facilitated the development of a clinical model, following a clinical radiomics model was formulated, integrating the radiomics scores (Radscore), along with the independent clinical variables identified through the screening process. The diagnostic performance of the three models constructed was evaluated using the receiver operating characteristic (ROC) curve analysis.</p><p><strong>Results: </strong>Among the three radiomics ML models, the logistic regression (LR) model achieved the best performance, with the area under the curve (AUC) values of 0.872 (95%CI, 0.800-0.944) and 0.836 (95%CI, 0.716-0.957) for the training set and the testing set, respectively. The decision curve analysis (DCA) verified the clinical practicability of the ML model. Within the same testing set, the AUC of the clinical model was 0.761 (95%CI, 0.606-0.916). The nomogram model based on clinical features plus Radscore showed the best discrimination, with an AUC value of 0.881 (95%CI, 0.779-0.982), which was better than that of the single clinical model and the radiomics model.</p><p><strong>Conclusion: </strong>The ML model of radiomics based on ultrasound images has potential value in the non-invasive differential diagnosis of patients with diabetic nephropathy. The nomogram constructed based on rad score and clinical features could effectively distinguish DN from NDRD.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494328","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":"Intestinal Lipoma Acting as a Lead Point of Intussusception: A Case Series.","authors":"Mei-Ying Jian, Xiao-Yan Luo, Xiu-Qin Luo, Ai-Fang Jin, Zhe-Huang Luo","doi":"10.2174/0115734056337435250206100026","DOIUrl":"https://doi.org/10.2174/0115734056337435250206100026","url":null,"abstract":"<p><strong>Background: </strong>Lipomas represent a rare benign etiology of intussusception in adults, affecting both the small intestine and the colon. Diagnosing intussusception in adults can be challenging, and there are no reports on the use of positron emission tomography/CT (PET/CT) in the diagnosis of lipoma-induced intussusception. This study aimed to preliminarily explore the potential diagnostic utility of 18F-FDG PET/CT in the diagnosis of intussusception caused by lipomas.</p><p><strong>Methods: </strong>We conducted a retrospective review of the clinical characteristics and imaging findings of three patients diagnosed with lipoma-induced intussusception using 18F-FDG PET/CT from 2019 to 2023 at our hospital.</p><p><strong>Results: </strong>The three cases presented with diverse clinical presentations and were diagnosed based on PET/CT imaging. Surgical confirmation was obtained in two cases. Lipomas were identified in both the small intestine and the colon, with one case displaying increased metabolic activity on FDG uptake, suggesting a possible link between FDG uptake and clinical severity.</p><p><strong>Conclusion: </strong>Lipoma is a benign cause of intussusception that can occur in both the small intestine and the colon. The symptoms of adult intussusception are often atypical and variable. Imaging modalities, particularly PET/CT, are instrumental in diagnosing intussusception due to lipomas, differentiating between benign and malignant causes, and assessing the severity to inform treatment strategies.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460679","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":"Impact of CT-Relevant Skeletal Muscle Parameters on Post-Chemotherapy Survival in Patients with Unresectable Pancreatic Ductal Adenocarcinoma.","authors":"Siying Zhang, Zhenping Wu, Guo Sa, Zhan Feng, Feng Chen","doi":"10.2174/0115734056356822250205174104","DOIUrl":"https://doi.org/10.2174/0115734056356822250205174104","url":null,"abstract":"<p><strong>Purpose: </strong>The study aimed to investigate the association of CT-relevant skeletal muscle parameters, such as sarcopenia and myosteatosis, with survival outcomes in patients receiving chemotherapy for unresectable pancreatic ductal adenocarcinoma (PDAC).</p><p><strong>Methods: </strong>In this retrospective analysis, patients who began chemotherapy for unresectable PDAC were included. Sarcopenia and myosteatosis were assessed on pretreatment CT at the L3 level by skeletal muscle index and mean muscle attenuation with predefined cutoff values. The Cox proportional hazards model was used to analyze the factors associated with progression-free survival (PFS) and overall survival (OS).</p><p><strong>Results: </strong>A total of 150 patients were enrolled. Compared to patients without sarcopenia, patients with sarcopenia had significantly worse PFS (p=0.003) and OS (p<0.001). Patients with myosteatosis had significantly worse PFS (p=0.01) and OS (p=0.002) compared to those without myosteatosis. In multivariate analysis, after adjusting for age, sex, tumor size, location, treatment modality, smoking, drinking, underlying diseases, and partial laboratory tests, sarcopenia remained an independent predictor of PFS (p=0.006) and OS (p<0.001). Myosteatosis remained an independent predictor of OS (p=0.008), but not of PFS.</p><p><strong>Conclusion: </strong>Sarcopenia and myosteatosis are independent prognostic factors for patients with unresectable pancreatic ductal adenocarcinoma after chemotherapy.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451024","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":"I-Brainer: Artificial intelligence/Internet of Things (AI/IoT)-Powered Detection of Brain Cancer.","authors":"Abdullahi Umar Ibrahim, Ikedichukwu Onyemaucheya Nwaneri, Mercel Vubangsi, Fadi Al-Turjman","doi":"10.2174/0115734056333393250117164020","DOIUrl":"https://doi.org/10.2174/0115734056333393250117164020","url":null,"abstract":"<p><strong>Background/objective: </strong>Brain tumour is characterized by its aggressive nature and low survival rate and thus regarded as one of the deadliest diseases. Thus, miss-diagnosis or miss-classification of brain tumour can lead to miss treatment or incorrect treatment and reduce survival chances. Therefore, there is need to develop a technique that can identify and detect brain tumour at early stages.</p><p><strong>Methods: </strong>Here, we proposed a framework titled I-Brainer which is an Artificial Intelligence/Internet of Things (AI/IoT)-powered classification of MRI. We employed a Br35H+SARTAJ brain MRI dataset which contain 7023 total images which include No tumour, pituitary, meningioma and glioma. In order to accurately classified MRI into 4-class, we developed LeNet model from scratch, implemented 2 pretrained models which include EfficientNet and ResNet-50 as well feature extraction of these models coupled with 2 Machine Learning classifiers k-Nearest Neighbours (KNN) and Support Vector Machines (SVM).</p><p><strong>Result: </strong>Evaluation and comparison of the performance of 3 models has shown that EfficientNet+SVM achieved the best result in terms of AUC (99%) and ResNet-50-KNN ranked higher in terms of accuracy (94%) on testing dataset.</p><p><strong>Conclusion: </strong>This framework can be harness by patients residing in remote areas and as confirmatory approach for medical experts.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366807","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":"Morphology and Distribution of Fat Globules in Osteomyelitis on Magnetic Resonance Imaging.","authors":"Li-Yuan Xie, Lei Cao, Wen-Juan Wu, Ji-Cun Liu, Na Zhao, Yong-Li Zheng, Xiao-Na Zhu, Bu-Lang Gao, Gui-Fen Han","doi":"10.2174/0115734056331041250116092101","DOIUrl":"https://doi.org/10.2174/0115734056331041250116092101","url":null,"abstract":"<p><strong>Introduction: </strong>The purpose of this study was to investigate the morphology and distribution characteristics of fat globules in osteomyelitis on magnetic resonance imaging (MRI).</p><p><strong>Materials and methods: </strong>Patients with pathologically-confirmed osteomyelitis and MRI scans were retrospectively enrolled, and fat globules on the MRI images were analyzed.</p><p><strong>Results: </strong>Among 103 patients with non-traumatic osteomyelitis, 75 were fat globule negative and 28 were positive. There was no statistically significant difference in age and gender between patients with and without fat globules (p>0.05). The inflammatory indicators (CRP, ESR, WBC, and NEUT) in the fat globule positive group were significantly higher (p<0.05) than those in the negative group. The lesions were mainly located in the long bones of the limbs in patients with positive fat globules. Twenty-eight patients (27.2% or 28/103) were detected to have fat globules on MRI images, including 20 males (71%) and 8 females (29%) aged 5-64 years (mean 16 years). The time from onset to MRI examination was 8 days to 4 months. The location of fat globules was in the tibia in 10 patients (35.7%), femur in 8 (28.6%), humerus in 4 (14.3%), radius in 2 (7.1%), ulna in 1 (3.6%), calcaneus in 1 (3.6%), sacrum in 1 (3.6%), and fibula in 1 patient (3.6%). On MRI imaging, 28 cases (100%) showed widely distributed patches or tortuous and sinuous abnormal signals in the bone marrow. In 25 cases (89.2%), a grid-like abnormal signal was found in the subcutaneous soft tissue. In 21 patients (75%), pus was found in the adjacent extraosseous soft tissues. Among 28 patients with fat globules, 17 patients (60.7%) had fat globules only in the adjacent extraosseous soft tissue, 6 patients (21.4%) had only intraosseous fat globules [including 5 cases with halo signs around the fat globules and 1 case (3.6%) with fat globules located at the edge of the pus cavity inside the bone without a halo sign], and 5 patients (17.8%) had both intraosseous and extraosseous fat globules. Of 6 patients (21.4% or 6/28) with liquid levels, the liquid level appeared outside the bone.</p><p><strong>Conclusion: </strong>The appearance of fat globules on MRI in patients with osteomyelitis indicates severe infection. Fat globules of osteomyelitis may present with diverse shapes inside and outside the bone marrow as one of the MRI signs of osteomyelitis, with a probability of approximately 27.2%. They have high specificity in diagnosing osteomyelitis and can be used for diagnosis and differential diagnosis.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054277","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":"Sonographic Features of Juvenile Fibroadenoma in Children-a Retrospective Study.","authors":"Jian Shi, Luzeng Chen, Jingming Ye, Shuang Zhang, Hong Zhang, Yuhong Shao, Xiuming Sun","doi":"10.2174/0115734056339987250116102211","DOIUrl":"https://doi.org/10.2174/0115734056339987250116102211","url":null,"abstract":"<p><strong>Aims: </strong>Studies specifically examining the sonographic features of juvenile fibroadenoma in the pediatric population have not been documented. We aimed to analyze sonograms of juvenile fibroadenoma in children.</p><p><strong>Subjects and methods: </strong>Patients aged ≤ 18 years who underwent breast ultrasound examinations at our department and had pathologically proven juvenile fibroadenoma from September 2002 to January 2022 were included in this study. Demographic data, clinical findings, and sonograms were retrospectively analyzed. Patients were further divided into the puberty and post-puberty subgroups, and their results were compared.</p><p><strong>Results: </strong>A total of 24 girls aged 10-18 years with 27 masses diagnosed as juvenile fibroadenomas were identified. The diameter of the masses averaged 5.8 ± 3.3 cm, with a range of 1.5-13.6 cm. Twenty-one (87.5%) patients had a single mass and 3 had double lesions. Over 80% of the lesions were oval-shaped and encapsulated with circumscribed margins and parallel orientation. All masses showed internal hypoechogenicity, either uniform or heterogeneous. For masses that had a diameter > 5 cm, screening with high-frequency transducers revealed no posterior acoustic features or posterior shadowing. However, these features changed to posterior acoustic enhancement when the masses were re-evaluated using low-frequency transducers. Ultrasonic color Doppler showed blood flow in 24 (88.9%) masses. There were no significant differences in the incidence and sonographic features between the two subgroups.</p><p><strong>Conclusion: </strong>Most juvenile fibroadenomas in children are oval, circumscribed, encapsulated masses with detectable blood flow. All juvenile fibroadenomas presented in this study exhibit internal hypoechogenicity with no posterior acoustic shadowing detected in any cases. Our findings suggest that screening with low-frequency transducers should be performed for a mass that has a diameter > 5 cm.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054279","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 Comparison of the Diagnostic Value of Multiorgan Point-of-care Ultrasound between High-risk and Medium-to-low-risk Pulmonary Embolism Cases.","authors":"Weihua Wu, Zhenfei Yu, Kang Cheng, Manqiong Xie, Shunjin Fang, Jianfeng Zhu","doi":"10.2174/0115734056344839250120045737","DOIUrl":"https://doi.org/10.2174/0115734056344839250120045737","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to explore the diagnostic value of multiorgan (heart, lungs, blood vessels) point-of-care ultrasound (PoCUS) in patients with high-risk and medium-to-low-risk pulmonary embolism (PE).</p><p><strong>Methods: </strong>Clinical data of 92 patients with suspected PE, admitted to Hangzhou TCM Hospital affiliated with Zhejiang Chinese Medical University from July 2021 to June 2023, were retrospectively analyzed. According to hemodynamic status, patients were divided into the high-risk (n=28) and the medium-to-low-risk groups (n=64). Using computed tomography (CT) and pulmonary angiography (CTPA) as the gold standard, all patients underwent multiorgan PoCUS examination. The sensitivity, specificity, and accuracy of different methods for diagnosing PE, as well as the time difference between multiorgan PoCUS examination and CTPA, were compared. Differences in measurement values of relevant indicators in all groups were analyzed.</p><p><strong>Results: </strong>In the high-risk group of patients, CTPA identified 15 cases of PE. In contrast, the PoCUS examination confirmed PE diagnosis in 14 cases (true positive), while 10 cases were diagnosed as true negative, one case as false negative, and three cases as false positive. In the medium-to-low-risk group, CTPA identified 50 patients with PE, while multiorgan PoCUS confirmed PE diagnosis in 33 cases (true positive), and identified 9 true negative, 17 false negative, and 5 false positive PE cases. Kappa test of the consistency between the results of multiorgan PoCUS and CTPA showed that multiorgan PoCUS had higher sensitivity, negative predictive value, and accuracy in the high-risk group compared to the medium-tolow- risk group (p<0.05). Cohen's Kappa value of the high-risk group was 0.710, indicating moderate consistency between PoCUS and CTPA results, while Cohen's Kappa value of 0.231 for the medium and low-risk group indicated poor consistency. There was a significant difference in ultrasound parameters between the high-risk and the medium-to-low-risk group (p<0.05). The time required for multiorgan PoCUS in both groups was significantly shorter than that for the CTPA. There was no significant difference in the time required for PoCUS between the two groups (p>0.05).</p><p><strong>Conclusion: </strong>Multiorgan PoCUS has been found to have higher sensitivity and accuracy in diagnosing patients with high-risk PE compared to those with medium-to-low-risk PE, and a shorter imaging time compared to CTPA.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054276","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":"CERVIXNET: An Efficient Approach for the Detection and Classifications of the Cervigram Images Using Modified Deep Learning Architecture.","authors":"N Karthikeyan, Gokul Chandrasekaran, S Sudha","doi":"10.2174/0115734056343690250116020310","DOIUrl":"https://doi.org/10.2174/0115734056343690250116020310","url":null,"abstract":"<p><strong>Introduction: </strong>The earlier detection of cervical cancer in women patients can save human life. This article proposes a novel methodology for detecting abnormal cervigram images from healthy cervigram images and segments the cancer regions in the abnormal cervigram images using the deep learning method. The conventional deep learning architecture has been modified into the proposed CervixNet architecture to improve the cervical cancer detection rate.</p><p><strong>Methods: </strong>This methodology is constituted of a training and testing process, where the training process generates the training sequences individually for healthy cervigram images and the cancer case cervigram images. The testing process tests the cervigram images into either a healthy or cancer cases using the training sequences generated through the training process. During the testing process of the proposed system, the cancer segmentation algorithm was applied on the abnormal cervigram image to detect and segment the pixels belonging to cancer. Finally, the performance has been carried out on the segmented cancer cervical images for the ground truth images. This proposed methodology has been evaluated on the cervigrams on IMODT and Guanacaste databases. Its performance has been analyzed concerning cancer pixel sensitivity, cancer pixel specificity and cancer pixel accuracy.</p><p><strong>Results: </strong>This research work obtains 98.69% Cancer Pixel Sensitivity (CPS), 98.76% Cancer Pixel Specificity (CPSP), and 99.27% Cancer Pixel Accuracy (CPA) for the set of cervigram images in the IMODT database. This research work obtains 99.22% CPS, 99.03% CPSP, and 99.01% CPA for the set of cervigram images in Guanacaste database.</p><p><strong>Conclusion: </strong>These experimental results of the proposed work have been significantly compared with the state-of-the-art methods and show the significance and novelty of the proposed works.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034755","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":"Personalized Respiratory Motion Modeling Incorporating Longitudinal Data through Two-stage Transfer Learning.","authors":"Peizhi Chen, Xupeng Zou, Yifan Guo","doi":"10.2174/0115734056325170250114210309","DOIUrl":"https://doi.org/10.2174/0115734056325170250114210309","url":null,"abstract":"<p><strong>Purpose: </strong>This study aims to develop an accurate image registration framework for personalized respiratory motion modeling.</p><p><strong>Methods: </strong>The proposed framework incorporates longitudinal data through a two-stage transfer learning approach. In the first stage, transfer learning is employed on longitudinal data collected from the same device. In the second stage, a personalized model is constructed using the transfer learning approach, reusing the model from the first stage. A novel cross-error function is introduced to guide the customized adaptation stage.</p><p><strong>Results: </strong>The experiments demonstrate the effectiveness of the proposed framework in respiratory motion modeling. Integrating longitudinal data allows for improved accuracy for personalized respiratory motion modeling.</p><p><strong>Conclusion: </strong>The study presents a novel approach that incorporates longitudinal data into a two-stage transfer learning process for personalized respiratory motion modeling. The framework demonstrates improved accuracy. The results highlight the potential of leveraging longitudinal data to provide personalized image registration solutions.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025753","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}