Current Medical Imaging Reviews最新文献

{"title":"Clinical Efficacy of Ultrasound Guidance in Brachial Plexus Nerve Conduction Study: A Comparative Analysis.","authors":"Zheyuan Zhang, Xiuli Li, Guangju Qi, Huabin Zhang, Xinhong Feng, Zhiyong Bai","doi":"10.2174/0115734056377599250717101905","DOIUrl":"https://doi.org/10.2174/0115734056377599250717101905","url":null,"abstract":"<p><strong>Introduction: </strong>Brachial plexopathy is a diagnostically challenging condition that requires a comprehensive evaluation, including physical examination, imaging, and Electrodiagnostic (EDx).</p><p><strong>Testing: </strong>Ultrasound guidance may improve the efficiency and precision of nerve conduction studies by addressing the limitations of blind techniques, such as discomfort and inaccurate localization.</p><p><strong>Methods: </strong>We prospectively enrolled 30 patients undergoing electrodiagnostic testing. The left upper limb was examined with ultrasound guidance (Group A), while the right upper limb underwent the blind method (Group B). The examined nerves included the median, ulnar, radial, medial and lateral antebrachial cutaneous, axillary, musculocutaneous, suprascapular, and long thoracic nerves. Stimulation duration, number of stimulation attempts, average current, and total examination time were recorded. The differences in data between the two groups were compared and analyzed.</p><p><strong>Results: </strong>Group A demonstrated significantly lower stimulation duration (156.70±50.13 vs. 260.17±53.19 s), fewer stimulation attempts (17.73±3.94 vs. 25.80±5.23), and lower average current [32.45 (30.28, 40.13) vs. 42.75 (37.78,50.68) mA] compared to Group B (all P < 0.001). No significant difference was observed in total examination time (387.40 ± 33.72 vs. 372.00 ± 47.01 s; P = 0.150).</p><p><strong>Discussion: </strong>Ultrasound guidance improves procedural precision and reduces the need for repeated stimulations and higher electrical intensities. These benefits are achieved without extending the total examination time, making it a feasible and patient-friendly approach for routine use in clinical neurophysiology.</p><p><strong>Conclusion: </strong>Ultrasound-guided nerve conduction studies of the brachial plexus enhance procedural efficiency and patient comfort compared to the blind method. Further large-scale studies are recommended to validate these findings and assess broader clinical applications.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144745959","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":"The Long-Term Volumetric and Radiological Changes of COVID-19 on Lung Anatomy: A Quantitative Assessment.","authors":"A Savranlar, M Öztürk, H Sipahioğlu, Y Savranlar, M Tahta Şahingöz","doi":"10.2174/0115734056372497250716235052","DOIUrl":"https://doi.org/10.2174/0115734056372497250716235052","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to assess the long-term volumetric and radiological effects of COVID-19 on lung anatomy. The severity of the disease was evaluated using radiological scoring, and lung volume measurements were performed via 3D Slicer software.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on a total of 127 patients diagnosed with COVID-19 between April 2020 and December 2023. Initial and follow-up chest CT scans were reviewed to analyze lung volumes and radiological findings. Lung lobes were segmented using 3D Slicer software to measure volumes. Severity scores were assigned based on the Chung system, and statistical methods, including logistic regression and Wilcoxon signed-rank tests, were used to analyze outcomes.</p><p><strong>Results: </strong>Follow-up CT scans showed significant improvements in lung volumes and severity scores. The left lung total volume increased significantly (p = 0.038), while right lung total volume and COVID-19-affected lung volumes demonstrated non-significant improvements. Severity scores and the number of affected lobes decreased significantly (p < 0.05). Correlation analyses revealed that age negatively influenced lung volume recovery (r = -0.177, p = 0.047). Persistent pathological findings, such as interstitial thickening and fibrotic bands, were observed.</p><p><strong>Conclusion: </strong>COVID-19 induces lasting changes in lung structure, particularly in elderly and severely affected patients. Long-term follow-up and the consideration of antifibrotic therapies are essential to manage post-COVID-19 complications effectively. A multidisciplinary approach is recommended to support patient recovery and minimize healthcare burdens.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144745960","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 for Detecting Pulmonary Embolisms <i>via</i> CT: A Workflow-oriented Implementation.","authors":"Selim Abed, Klaus Hergan, Jan Dörrenberg, Lucas Brandstetter, Marcus Lauschmann","doi":"10.2174/0115734056367860250630072749","DOIUrl":"https://doi.org/10.2174/0115734056367860250630072749","url":null,"abstract":"<p><strong>Introduction: </strong>Detecting Pulmonary Embolism (PE) is critical for effective patient care, and Artificial Intelligence (AI) has shown promise in supporting radiologists in this task. Integrating AI into radiology workflows requires not only evaluation of its diagnostic accuracy but also assessment of its acceptance among clinical staff.</p><p><strong>Objective: </strong>This study aims to evaluate the performance of an AI algorithm in detecting pulmonary embolisms (PEs) on contrast-enhanced computed tomography pulmonary angiograms (CTPAs) and to assess the level of acceptance of the algorithm among radiology department staff.</p><p><strong>Methods: </strong>This retrospective study analyzed anonymized computed tomography pulmonary angiography (CTPA) data from a university clinic. Surveys were conducted at three and nine months after the implementation of a commercially available AI algorithm designed to flag CTPA scans with suspected PE. A thoracic radiologist and a cardiac radiologist served as the reference standard for evaluating the performance of the algorithm. The AI analyzed 59 CTPA cases during the initial evaluation and 46 cases in the follow-up assessment.</p><p><strong>Results: </strong>In the first evaluation, the AI algorithm demonstrated a sensitivity of 84.6% and a specificity of 94.3%. By the second evaluation, its performance had improved, achieving a sensitivity of 90.9% and a specificity of 96.7%. Radiologists' acceptance of the AI tool increased over time. Nevertheless, despite this growing acceptance, many radiologists expressed a preference for hiring an additional physician over adopting the AI solution if the costs were comparable.</p><p><strong>Discussion: </strong>Our study demonstrated high sensitivity and specificity of the AI algorithm, with improved performance over time and a reduced rate of unanalyzed scans. These improvements likely reflect both algorithmic refinement and better data integration. Departmental feedback indicated growing user confidence and trust in the tool. However, many radiologists continued to prefer the addition of a resident over reliance on the algorithm. Overall, the AI showed promise as a supportive \"second-look\" tool in emergency radiology settings.</p><p><strong>Conclusion: </strong>The AI algorithm demonstrated diagnostic performance comparable to that reported in similar studies for detecting PE on CTPA, with both sensitivity and specificity showing improvement over time. Radiologists' acceptance of the algorithm increased throughout the study period, underscoring its potential as a complementary tool to physician expertise in clinical practice.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709925","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 and Simplified MSI Approach to Predicting the Long-Term Cardiac Function of STEMI.","authors":"Qifei Xie, Meiling Nie, Feifei Zhang, Xiaoliang Shao, Jianfeng Wang, Juan Song, Yuetao Wang","doi":"10.2174/0115734056375695250707070025","DOIUrl":"https://doi.org/10.2174/0115734056375695250707070025","url":null,"abstract":"<p><strong>Introduction: </strong>The Myocardial Salvage Index (MSI) is a valuable indicator in ST-segment Elevation Myocardial Infarction (STEMI) treated with Percutaneous Coronary Intervention (PCI), yet challenges exist in its acquisition. This study aims to calculate MSI using Coronary Angiography (CAG) and myocardial perfusion imaging, and further investigate its correlation with long-term cardiac function.</p><p><strong>Methods: </strong>In 203 STEMI, the myocardium at risk was measured through CAG using the Bypass Angioplasty Revascularization Investigation Myocardial Jeopardy Index (BARI) score. The infarcted myocardium was measured by the Total Perfusion Deficit (TPD) obtained in Myocardial Perfusion Imaging (MPI) after PCI. MSI was computed as (BARI score-TPD)/BARI score. Long-term cardiac function was assessed via echocardiography.</p><p><strong>Results: </strong>The MSI is notably associated with the long-term cardiac function [EF: Beta = 16 (13, 20), P < 0.00; LVD: Beta = -7.3 (-9.3, -5.3), P < 0.001]. TIMI flow grades 2-3 demonstrate a superior MSI compared to grades 0-1 [0.78 (0.32) vs. 0.61 (0.38), P = 0.002]. TIMI flow grades have an impact on MSI [Beta = 0.08 (0.04, 0.13), P < 0.001]. Compared to patients with a Killip grade of < 2, those with a grade ≥ 2 exhibit a lower MSI [0.69 (0.35) vs. 0.48 (0.42), p = 0.005]. The Killip classification has an impact on MSI [Beta = -0.12(-0.19, -0.04), P = 0.003].</p><p><strong>Discussion: </strong>The study indicates the pivotal role of MSI in predicting long-term cardiac function in STEMI, compares the advantages and limitations of SPECT, CMR, and hybrid SPECT/CAG methods, analyzes the impact of residual blood flow and acute heart failure on MSI, and highlights current technological challenges and future research directions.</p><p><strong>Conclusion: </strong>CAG combining MPI after PCI can be used to obtain MSI. MSI is linked to long-term cardiac function. The amount of antegrade flow before PCI and the initial cardiac function upon admission significantly influence MSI.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709895","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":"Evaluation of the Relationship between Presternal Fatty Tissue Thickness, Epicardial Fatty Tissue Volume, and Coronary Artery Disease.","authors":"Turgut Tursem Tokmak","doi":"10.2174/0115734056362293250712131404","DOIUrl":"https://doi.org/10.2174/0115734056362293250712131404","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;This cross-sectional study aimed to evaluate the relationship between presternal adipose tissue thickness and the pericardial adipose tissue volume in relation to coronary artery disease.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A total of 108 patients who underwent coronary computed tomography angiography (CCTA) for suspected coronary disease between 2019 and 2022 were evaluated. Patients whose epicardial adipose tissue could not be optimally measured due to imaging artifacts, those with a pre-existing coronary artery anomaly or known heart disease, individuals under 18 years of age, and those who had undergone sternotomy and bypass surgery were excluded from the study. Accordingly, 95 patients (61 males and 34 females) who met the inclusion criteria and did not meet any of the exclusion criteria were included in the study. CCTA images were analyzed retrospectively. Pericardial adipose tissue volume was measured automatically using Syngo Via software. Presternal fat thickness (PFTT) was measured at the level of the pulmonary artery bifurcation, from the anterior to the posterior surface.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The study sample comprised 64.2% males and 35.7% females. The median thickness of the presternal fat tissue was found to be 11.5 mm, with a range of 3 to 44 mm. The median PFTT was measured at 9 mm (3-23 mm) in the male patient group, while in the female patient group, it was 20 mm (10-44 mm). The median epicardial fat volume (EFV) for the full sample was 83.1 ml (22.3-171 ml), measuring 81.1 ml (37-171 ml) and 79.5 ml (22.3-167 ml) in males and females, respectively. A significant correlation was observed between PFTT and EFV in the full sample (Rho = 0.236, p = 0.02), as well as among male patients (Rho = 0.409, p = 0.001), but not in the female patient group (Rho = 0.264, p = 0.131). In the male cohort, there was no significant difference between EFV and PFTT, and the presence of coronary plaque.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Discussion: &lt;/strong&gt;This study examines the relationship between presternal adipose tissue thickness (PFTT) and coronary heart disease (CHD), building on previous evidence that links epicardial adipose tissue (EAT) to cardiovascular risk. We found a significant correlation between PFTT and epicardial fat volume (EFV) in male patients, but not in females, which is likely due to hormonal influences and variability in breast tissue. Importantly, measurement of PFTT provides a practical, non-invasive method for assessing CAD risk in clinical settings. Although our small sample size limits the generalisability of our findings, these results suggest that PFTT may serve as an indirect marker of CAD risk and highlight the need for further research with larger cohorts to validate its clinical relevance. Incorporating PFTT assessment into routine practice may improve the early identification of high-risk patients and enhance strategies for preventing ischemic heart disease.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/stro","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709926","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":"MDCT-Based Grading of Perirenal Changes Secondary to Acute Unilateral Upper Urinary Tract Obstruction.","authors":"Fukang Zhang, Huayu You, Yanlan Deng, Guiquan Chen, Yihui Qiu, Zhiyong Ling, Huasong Cai, Nan Liu","doi":"10.2174/0115734056329112250710123850","DOIUrl":"https://doi.org/10.2174/0115734056329112250710123850","url":null,"abstract":"<p><strong>Background: </strong>Unilateral upper ureteral obstruction is one of the most common causes of acute kidney function impairment. Grading perirenal changes secondary to acute unilateral upper urinary tract obstruction (AUUTO) with multidetector spiral computed tomography (MDCT) and exploring its association with kidney function are useful for diagnosing and assessing damage to the ipsilateral kidney. However, the correlation between renal function impairment and the severity of perinephric changes secondary to AUUTO has not been reported.</p><p><strong>Objective: </strong>This study aimed to investigate the association of perirenal changes secondary to AUUTO with hydronephrosis and serum creatinine levels, as well as white blood cell counts.</p><p><strong>Methods: </strong>This retrospective study included 376 patients with acute unilateral upper ureteral obstruction, all of whom were subjected to MDCT scans. They were classified into four grades (0-III) according to their perirenal changes on MDCT images. The severity of hydronephrosis was classified into four grades based on MDCT scans. The serum creatinine level and leukocyte counts were compared among the MDCT grade groups, and logistic regression analysis was conducted.</p><p><strong>Results: </strong>Among 376 patients, 77 (20.5%), 103 (27.4%), 140 (37.2%), and 56 (14.9%) cases were graded into MDCT 0, I, II, and III, respectively. The proportions of patients who had normal kidneys in MDCT 0, I, II, and III were 20 (26.0%), 10 (9.7%), 11(7.9%), and 3 (5.4%), respectively. The proportions of patients who had mild hydronephrosis in MDCT 0, I, II, and III were 55 (71.4%), 83 (80.6%), 118 (84.2%), and 46 (82.1%), respectively. The proportions of patients who had moderate and severe hydronephrosis in MDCT 0, I, II, and III were 2(2.6%), 10 (9.7%), 11 (7.9%), 7 (12.5%), respectively. Serum creatinine levels and white blood cell counts were significantly different among the MDCT grade groups (P < 0.001). Univariate and multivariate logistic regression analyses indicated that the serum creatinine level and white blood cell counts were positively associated with the MDCT grades (P < 0.001).</p><p><strong>Conclusion: </strong>Perinephric changes secondary to AUUTO on MDCT images were associated with the degree of obstruction. The severity of perinephric changes can reflect the functional impairment in the ipsilateral kidney. The MDCT grades may aid clinicians in assessing renal function impairment early in patients with AUUTO, which may help patients receive early intervention and avoid the potential risk of infection and deterioration of renal function.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692404","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":"CT Features of Advanced Pericochlear Otosclerosis: Case Report and a Reappraisal of Nomenclature.","authors":"Rowa A Mohamed, Mohamed S Muneer, Tarik F Massoud","doi":"10.2174/0115734056379053250712052649","DOIUrl":"https://doi.org/10.2174/0115734056379053250712052649","url":null,"abstract":"<p><strong>Background: </strong>This case study aimed to report the rare computed tomography (CT) features of advanced pericochlear otosclerosis, with an emphasis on a proposed new nomenclature to describe the imaging findings.</p><p><strong>Case presentation: </strong>A 70-year-old woman with recurrent rhinosinusitis presented to our center for clinical management. The CT scan revealed the incidental rare findings of advanced retrofenestral otosclerosis in the form of extensive symmetrical pericochlear tubular lucencies in bilateral otic capsules. We coined the new term \"C-hoop earring\" sign for this CT appearance. She was asymptomatic and declined further audiological or imaging evaluation.</p><p><strong>Conclusion: </strong>Herein, the CT features of advanced pericochlear otosclerosis are described and the imaging and clinical connotations of the presence of the Choop earring sign are reviewed. This new terminology provides a more intuitive description of the imaging findings in the temporal bones for clearer understanding and communication in clinical radiological practice and education.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692319","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 Case Report on the Dramatic Response of ¹⁷⁷Lu-PSMA Therapy for Metastatic Prostate Cancer.","authors":"Aysenur Sinem Erdogan, Haluk Sayan, Bedri Seven, Berna Okudan","doi":"10.2174/0115734056362468250709045212","DOIUrl":"https://doi.org/10.2174/0115734056362468250709045212","url":null,"abstract":"<p><strong>Introduction: </strong>In nuclear medicine, Prostate-specific Membrane Antigen (PSMA) is a potential target for theranostics. Offering superior diagnostic accuracy to conventional imaging in prostate cancer (PCa), Gallium-68 labeled PSMA (⁶⁸Ga-PSMA) positron emission tomography/computed tomography (PET/CT) is considered the new standard of care in PCa management. Tumor cells identified as PSMA-avid on PET/CT imaging can be targeted and eliminated with PSMA-labeled Lutetium-177 (¹⁷⁷Lu-PSMA) therapy.</p><p><strong>Case presentation: </strong>A sixty-eight years old patient who had metastatic castration-resistant PCa was reported in this study. Prior to receiving ¹⁷⁷Lu-PSMA therapy, the patient's PSA level was 358 ng/ml, and experienced extensive bone discomfort. Following ten cycles of ¹⁷⁷Lu-PSMA therapy, exceptional results were observed.</p><p><strong>Conclusion: </strong>¹⁷⁷Lu-PSMA therapy is likely to result in significantly better outcomes if first- or second-line treatments preserve the patient's bone marrow reserve or if the therapy is administered at earlier stages of the disease.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683543","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":"Translation of Fundus Image to Fundus Fluorescein Angiography Boosted by Structure Self-Supervised Representation Cycle Learning.","authors":"Xiaopeng Wang, Chaoyong Liu, Ruotong Mu, Yi Chen, Di Gong, Qiang Yang, Qiang Liu","doi":"10.2174/0115734056374967250704090646","DOIUrl":"https://doi.org/10.2174/0115734056374967250704090646","url":null,"abstract":"<p><strong>Introduction: </strong>Fundus fluorescein angiography captures detailed images of fundus vasculature, enabling precise disease assessment. Translating fundus images to fundus fluorescein angiography images can assist patients unable to use contrast agents due to physical constraints, facilitating disease analysis. Previous studies on this translation task were limited by the use of only 17 image pairs for training, potentially restricting model performance.</p><p><strong>Methods: </strong>Image pairs were collected from patients through a collaborating hospital to create a larger dataset. A fundus image to fundus fluorescein angiography translation model was developed using structure self-supervised representation cycle learning. This model focuses on vascular structures for self-supervised learning, incorporates an auxiliary branch, and utilizes cycle learning to enhance the main training pipeline.</p><p><strong>Results: </strong>Comparative evaluations on the test set demonstrate superior performance of the proposed model, with significantly improved Fréchet inception distance and kernel inception distance scores. Additionally, generalization experiments conducted on public datasets further confirm the model's advantages in various evaluation metrics.</p><p><strong>Discussion: </strong>The enhanced performance of the proposed model can be attributed to the larger dataset and the novel structure self-supervised cycle learning approach, which effectively captures vascular details critical for accurate translation. The model's robust generalization across public datasets suggests its potential applicability in diverse clinical settings. However, challenges such as computational complexity and the need for further validation in real-world scenarios warrant additional investigation to ensure scalability and clinical reliability.</p><p><strong>Conclusion: </strong>The proposed model effectively translates fundus images to fundus fluorescein angiography images, overcoming limitations of small datasets in previous studies. This approach demonstrates strong generalization capabilities, highlighting its potential to aid in large-scale disease analysis and patient care.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683545","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":"Evaluation of Deep Learning Methods for Pulmonary Disease Classification.","authors":"Ajay Pal Singh, Ankita Nigam, Gaurav Garg","doi":"10.2174/0115734056388107250710120917","DOIUrl":"https://doi.org/10.2174/0115734056388107250710120917","url":null,"abstract":"<p><strong>Introduction: </strong>Driven by environmental pollution and the rise in infectious diseases, the increasing prevalence of lung conditions demands advancements in diagnostic techniques.</p><p><strong>Materials and methods: </strong>This study explores the use of various features, such as spectrograms, chromograms, and Mel Frequency Cepstral Coefficients (MFCC), to extract crucial information from auscultation recordings. It addresses challenges through filter-based audio enhancement methods. The primary goal is to improve disease detection accuracy by leveraging convolutional neural networks (CNNs) for feature extraction and dense neural networks for classification.</p><p><strong>Results: </strong>While deep learning models like CNNs and Recurrent Neural Network (RNN) outperform traditional machine learning models such as Sequence Vector Machine, K-Nearest Neighbours (KNN) and random forest with accuracies ranging from 70% to 85%. The combination of CNN, RNN, and long short-term memory achieved an accuracy of 88%. By integrating MFCC, Chroma Short-Term Fourier Transform (STFT), and spectrogram features with a CNN-based classifier, the proposed multi-feature deep learning model achieved the highest accuracy of 92%, surpassing all other methods.</p><p><strong>Discussion: </strong>The study effectively addresses key issues, including the overrepresentation of Chronic Obstructive Pulmonary Disease (COPD) samples over Lower Respiratory Tract Infections (LRTI) and Upper Respiratory Tract Infections (URTI) which hampers generalization across test audio samples.</p><p><strong>Conclusion: </strong>The proposed methodology caters common challenges like background noise in recordings, and the limited and imbalanced nature of datasets. These findings pave the way for enhanced clinical applications, showcasing the transformative potential of multi-feature deep learning methods in the classification of pulmonary diseases.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683544","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}