Diagnostic and interventional radiology最新文献

{"title":"New imaging techniques and trends in radiology.","authors":"Mecit Kantarcı, Sonay Aydın, Hayri Oğul, Volkan Kızılgöz","doi":"10.4274/dir.2024.242926","DOIUrl":"10.4274/dir.2024.242926","url":null,"abstract":"<p><p>Radiography is a field of medicine inherently intertwined with technology. The dependency on technology is very high for obtaining images in ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). Although the reduction in radiation dose is not applicable in US and MRI, advancements in technology have made it possible in CT, with ongoing studies aimed at further optimization. The resolution and diagnostic quality of images obtained through advancements in each modality are steadily improving. Additionally, technological progress has significantly shortened acquisition times for CT and MRI. The use of artificial intelligence (AI), which is becoming increasingly widespread worldwide, has also been incorporated into radiography. This technology can produce more accurate and reproducible results in US examinations. Machine learning offers great potential for improving image quality, creating more distinct and useful images, and even developing new US imaging modalities. Furthermore, AI technologies are increasingly prevalent in CT and MRI for image evaluation, image generation, and enhanced image quality.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"505-517"},"PeriodicalIF":1.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417918/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of the Kaiser score system in uncertain malignant potential (B3) breast lesions: a pilot study.","authors":"Fatma Çelik Yabul, Hafize Otçu Temur, Bahar Atasoy, Serdar Balsak, Alpay Alkan, Şeyma Yıldız","doi":"10.4274/dir.2025.242401","DOIUrl":"10.4274/dir.2025.242401","url":null,"abstract":"<p><strong>Purpose: </strong>This study aims to evaluate the effectiveness of the Kaiser score (KS) system in assessing breast lesions with uncertain malignant potential (B3).</p><p><strong>Methods: </strong>Breast magnetic resonance imaging (MRI) scans from a total of 76 patients with histologically proven B3 lesions were included in this study. The KS was recorded for each MRI scan. The patients were classified based on biopsy results, and upgraded lesions were identified. Statistical analysis was conducted to evaluate the association between high KS values and upgraded lesions.</p><p><strong>Results: </strong>The mean age of the 76 patients was calculated as 49.6 ± 10.1. A significant association was observed between the KS system and the prediction of malignancy upgrade (<i>P</i> < 0.001). Furthermore, among the descriptors, spiculation, margin, and upgrading prediction demonstrated a statistically significant difference (<i>P</i> < 0.001). Additionally, the specificity improved when the accepted KS cut-off value was set at seven instead of five. A significant association was also observed between the KS system and the papilloma upgrade rate within the B3 lesion subgroups (<i>P</i> < 0.001).</p><p><strong>Conclusion: </strong>Breast radiology plays a crucial role in the diagnosis of B3 lesions. Our findings suggest that the KS system holds promise as a tool for predicting the upgrade potential of B3 lesions.</p><p><strong>Clinical significance: </strong>This study demonstrated that the KS system may assist in predicting the upgrade potential of B3 breast lesions. It also demonstrated that spiculation and margin descriptors within the KS system possess a high positive predictive value for upgrade prediction. Additionally, we believe that the KS system can help prevent unnecessary surgeries in patients with B3 lesions.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"474-479"},"PeriodicalIF":1.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143709206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilization of a steerable microcatheter and adjunctive techniques for prostatic artery embolization in anatomically challenging vesicoprostatic trunks.","authors":"Hippocrates Moschouris, Çağın Şentürk, Konstantinos Stamatiou","doi":"10.4274/dir.2025.243198","DOIUrl":"10.4274/dir.2025.243198","url":null,"abstract":"<p><p>Prostatic artery (PA) origination from a common trunk with the superior vesical artery (SVA) is a frequent cause of technical difficulties in PA catheterization for PA embolization (PAE). These difficulties, which substantially increase the operative time, radiation dose, cost, and technical failure rate of PAE, can often be overcome by the utilization of a steerable microcatheter (MC) with a tip that can be manually adjusted at an angle that optimally conforms to the shape and origin of the common vesicoprostatic trunk. Adjunctive techniques that can be applied when the steerable MC fails to engage the PA include: 1) the protective temporary embolization of the SVA so that a permanent embolic can be redirected into the PA; 2) PAE via collaterals between superior vesical branches and the PA; and 3) embolization from a proximal position of the MC near the PA orifice to exploit preferential flow to the PA. In the authors' recent experience, the utilization of a steerable MC with and without adjunctive techniques (in 12 and 23 patients, respectively) resulted in a 35% increase in the technically successful embolization of PAs originating from vesicoprostatic trunks with no significant complications. Familiarization with alternative devices and techniques may substantially improve the technical outcome of PAE in cases with challenging arterial anatomy.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"496-501"},"PeriodicalIF":1.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility study of computed high b-value diffusion-weighted magnetic resonance imaging for pediatric posterior fossa tumors.","authors":"Semra Delibalta, Barış Genç, Meltem Ceyhan Bilgici, Kerim Aslan","doi":"10.4274/dir.2024.242720","DOIUrl":"10.4274/dir.2024.242720","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the diagnostic efficacy of computed diffusion-weighted imaging (DWI) in pediatric posterior fossa tumors generated using high b-values.</p><p><strong>Methods: </strong>We retrospectively performed our study on 32 pediatric patients who had undergone brain magnetic resonance imaging for a posterior fossa tumor between January 2016 and January 2022. The DWIs were evaluated for each patient by two blinded radiologists. The computed DWI (cDWI) was mathematically derived using a mono-exponential model from images with b = 0 and 1,000 s/mm² and high b-values of 1,500, 2,000, 3,000, and 5,000 s/mm². The posterior fossa tumors were divided into two groups, low grade and high grade, and the tumor/thalamus signal intensity (SI) ratios were compared. The Mann-Whitney U test and receiver operating characteristic (ROC) curves were used to compare the diagnostic performance of the acquired DWI (DWI₁₀₀₀), apparent diffusion coefficient (ADC)₁₀₀₀ maps, and cDWI (cDWI1500, cDWI₂₀₀₀, cDWI₃₀₀₀, and cDWI₅₀₀₀).</p><p><strong>Results: </strong>The comparison of the two tumor groups revealed that the tumor/thalamus SI ratio on the DWI₁₀₀₀ and cDWI (cDWI1500, cDWI₂₀₀₀, cDWI₃₀₀₀, and cDWI₅₀₀₀) was statistically significantly higher in high-grade tumors (<i>P</i> < 0.001). In the ROC curve analysis, higher sensitivity and specificity were detected in the cDWI1500, cDWI₂₀₀₀, cDWI₃₀₀₀, and ADC₁₀₀₀ maps (100%, 90.90%) compared with the DWI₁₀₀₀ (80%, 81.80%). cDWI₃₀₀₀ had the highest area under the curve (AUC) value compared with other parameters (AUC: 0.976).</p><p><strong>Conclusion: </strong>cDWI generated using high b-values was successful in differentiating between low-grade and high-grade posterior fossa tumors without increasing imaging time.</p><p><strong>Clinical significance: </strong>cDWI created using high b-values can provide additional information about tumor grade in pediatric posterior fossa tumors without requiring additional imaging time.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"526-531"},"PeriodicalIF":1.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417922/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142105425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for named entity recognition in Turkish radiology reports.","authors":"Abubakar Ahmad Abdullahi, Murat Can Ganiz, Ural Koç, Muhammet Batuhan Gökhan, Ceren Aydın, Ali Bahadır Özdemir","doi":"10.4274/dir.2025.243100","DOIUrl":"10.4274/dir.2025.243100","url":null,"abstract":"<p><strong>Purpose: </strong>The primary objective of this research is to enhance the accuracy and efficiency of information extraction from radiology reports. In addressing this objective, the study aims to develop and evaluate a deep learning framework for named entity recognition (NER).</p><p><strong>Methods: </strong>We used a synthetic dataset of 1,056 Turkish radiology reports created and labeled by the radiologists in our research team. Due to privacy concerns, actual patient data could not be used; however, the synthetic reports closely mimic genuine reports in structure and content. We employed the four-stage DYGIE++ model for the experiments. First, we performed token encoding using four bidirectional encoder representations from transformers (BERT) models: BERTurk, BioBERTurk, PubMedBERT, and XLM-RoBERTa. Second, we introduced adaptive span enumeration, considering the word count of a sentence in Turkish. Third, we adopted span graph propagation to generate a multidirectional graph crucial for coreference resolution. Finally, we used a two-layered feed-forward neural network to classify the named entity.</p><p><strong>Results: </strong>The experiments conducted on the labeled dataset showcase the approach's effectiveness. The study achieved an F1 score of 80.1 for the NER task, with the BioBERTurk model, which is pre-trained on Turkish Wikipedia, radiology reports, and biomedical texts, proving to be the most effective of the four BERT models used in the experiment.</p><p><strong>Conclusion: </strong>We show how different dataset labels affect the model's performance. The results demonstrate the model's ability to handle the intricacies of Turkish radiology reports, providing a detailed analysis of precision, recall, and F1 scores for each label. Additionally, this study compares its findings with related research in other languages.</p><p><strong>Clinical significance: </strong>Our approach provides clinicians with more precise and comprehensive insights to improve patient care by extracting relevant information from radiology reports. This innovation in information extraction streamlines the diagnostic process and helps expedite patient treatment decisions.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"430-439"},"PeriodicalIF":1.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143523020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gastrointestinal bleeding detection on digital subtraction angiography using convolutional neural networks with and without temporal information.","authors":"Derek Smetanick, Sailendra Naidu, Alex Wallace, M-Grace Knuttinen, Indravadan Patel, Sadeer Alzubaidi","doi":"10.4274/dir.2025.253134","DOIUrl":"10.4274/dir.2025.253134","url":null,"abstract":"<p><strong>Purpose: </strong>Digital subtraction angiography (DSA) offers a real-time approach to locating lower gastrointestinal (GI) bleeding. However, many sources of bleeding are not easily visible on angiograms. This investigation aims to develop a machine learning tool that can locate GI bleeding on DSA prior to transarterial embolization.</p><p><strong>Methods: </strong>All mesenteric artery angiograms and arterial embolization DSA images obtained in the interventional radiology department between January 1, 2007, and December 31, 2021, were analyzed. These images were acquired using fluoroscopy imaging systems (Siemens Healthineers, USA). Thirty-nine unique series of bleeding images were augmented to train two-dimensional (2D) and three-dimensional (3D) residual neural networks (ResUNet++) for image segmentation. The 2D ResUNet++ network was trained on 3,548 images and tested on 394 images, whereas the 3D ResUNet++ network was trained on 316 3D objects and tested on 35 objects. For each case, both manually cropped images focused on the GI bleed and uncropped images were evaluated, with a superimposition post-processing (SIPP) technique applied to both image types.</p><p><strong>Results: </strong>Based on both quantitative and qualitative analyses, the 2D ResUNet++ network significantly outperformed the 3D ResUNet++ model. In the qualitative evaluation, the 2D ResUNet++ model achieved the highest accuracy across both 128 × 128 and 256 × 256 input resolutions when enhanced with the SIPP technique, reaching accuracy rates between 95% and 97%. However, despite the improved detection consistency provided by SIPP, a reduction in Dice similarity coefficients was observed compared with models without post-processing. Specifically, the 2D ResUNet++ model combined with SIPP achieved a Dice accuracy of only 80%. This decline is primarily attributed to an increase in false positive predictions introduced by the temporal propagation of segmentation masks across frames.</p><p><strong>Conclusion: </strong>Both 2D and 3D ResUNet++ networks can be trained to locate GI bleeding on DSA images prior to transarterial embolization. However, further research and refinement are needed before this technology can be implemented in DSA for real-time prediction.</p><p><strong>Clinical significance: </strong>Automated detection of GI bleeding in DSA may reduce time to embolization, thereby improving patient outcomes.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"465-473"},"PeriodicalIF":1.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417900/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144815985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proposal for training: the educational value of a musculoskeletal embolization patellar tendinopathy model.","authors":"Emeric Gremen, Julien Ghelfi, Marylène Bacle, Julien Frandon","doi":"10.4274/dir.2024.243005","DOIUrl":"10.4274/dir.2024.243005","url":null,"abstract":"","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"502-504"},"PeriodicalIF":1.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417919/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Letter to editor: dual-energy computed tomography-based volumetric thyroid iodine quantification: correlation with thyroid hormonal status, pathologic diagnosis, and phantom validation.","authors":"Ahmet Gürkan Erdemir","doi":"10.4274/dir.2025.253412","DOIUrl":"10.4274/dir.2025.253412","url":null,"abstract":"","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"480-481"},"PeriodicalIF":1.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adherence to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM): an umbrella review with a comprehensive two-level analysis.","authors":"Burak Koçak, Fadime Köse, Ali Keleş, Abdurrezzak Şendur, İsmail Meşe, Mehmet Karagülle","doi":"10.4274/dir.2025.243182","DOIUrl":"10.4274/dir.2025.243182","url":null,"abstract":"<p><strong>Purpose: </strong>To comprehensively assess Checklist for Artificial Intelligence in Medical Imaging (CLAIM) adherence in medical imaging artificial intelligence (AI) literature by aggregating data from previous systematic and non-systematic reviews.</p><p><strong>Methods: </strong>A systematic search of PubMed, Scopus, and Google Scholar identified reviews using the CLAIM to evaluate medical imaging AI studies. Reviews were analyzed at two levels: review level (33 reviews; 1,458 studies) and study level (421 unique studies from 15 reviews). The CLAIM adherence metrics (scores and compliance rates), baseline characteristics, factors influencing adherence, and critiques of the CLAIM were analyzed.</p><p><strong>Results: </strong>A review-level analysis of 26 reviews (874 studies) found a weighted mean CLAIM score of 25 [standard deviation (SD): 4] and a median of 26 [interquartile range (IQR): 8; 25^th-75^th percentiles: 20-28]. In a separate review-level analysis involving 18 reviews (993 studies), the weighted mean CLAIM compliance was 63% (SD: 11%), with a median of 66% (IQR: 4%; 25^th-75^th percentiles: 63%-67%). A study-level analysis of 421 unique studies published between 1997 and 2024 found a median CLAIM score of 26 (IQR: 6; 25^th-75^th percentiles: 23-29) and a median compliance of 68% (IQR: 16%; 25^th-75^th percentiles: 59%-75%). Adherence was independently associated with the journal impact factor quartile, publication year, and specific radiology subfields. After guideline publication, CLAIM compliance improved (<i>P</i> = 0.004). Multiple readers provided an evaluation in 85% (28/33) of reviews, but only 11% (3/28) included a reliability analysis. An item-wise evaluation identified 11 underreported items (missing in ≥50% of studies). Among the 10 identified critiques, the most common were item inapplicability to diverse study types and subjective interpretations of fulfillment.</p><p><strong>Conclusion: </strong>Our two-level analysis revealed considerable reporting gaps, underreported items, factors related to adherence, and common CLAIM critiques, providing actionable insights for researchers and journals to improve transparency, reproducibility, and reporting quality in AI studies.</p><p><strong>Clinical significance: </strong>By combining data from systematic and non-systematic reviews on CLAIM adherence, our comprehensive findings may serve as targets to help researchers and journals improve transparency, reproducibility, and reporting quality in AI studies.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"440-455"},"PeriodicalIF":1.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417908/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143398672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic bone age assessment: a Turkish population study.","authors":"Samet Öztürk, Murat Yüce, Gül Gizem Pamuk, Candan Varlık, Ahmet Tan Cimilli, Musa Atay","doi":"10.4274/dir.2025.242999","DOIUrl":"10.4274/dir.2025.242999","url":null,"abstract":"<p><strong>Purpose: </strong>Established methods for bone age assessment (BAA), such as the Greulich and Pyle atlas, suffer from variability due to population differences and observer discrepancies. Although automated BAA offers speed and consistency, limited research exists on its performance across different populations using deep learning. This study examines deep learning algorithms on the Turkish population to enhance bone age models by understanding demographic influences.</p><p><strong>Methods: </strong>We analyzed reports from Bağcılar Hospital's Health Information Management System between April 2012 and September 2023 using \"bone age\" as a keyword. Patient images were re-evaluated by an experienced radiologist and anonymized. A total of 2,730 hand radiographs from Bağcılar Hospital (Turkish population), 12,572 from the Radiological Society of North America (RSNA), and 6,185 from the Radiological Hand Pose Estimation (RHPE) public datasets were collected, along with corresponding bone ages and gender information. A random set of 546 radiographs (273 from Bağcılar, 273 from public datasets) was initially randomly split for an internal test set with bone age stratification; the remaining data were used for training and validation. BAAs were generated using a modified InceptionV3 model on 500 × 500-pixel images, selecting the model with the lowest mean absolute error (MAE) on the validation set.</p><p><strong>Results: </strong>Three models were trained and tested based on dataset origin: Bağcılar (Turkish), public (RSNA-RHPE), and a Combined model. Internal test set predictions of the Combined model estimated bone age within less than 6, 12, 18, and 24 months at rates of 44%, 73%, 87%, and 94%, respectively. The MAE was 9.2 months in the overall internal test set, 7 months on the public test set, and 11.5 months on the Bağcılar internal test data. The Bağcılar-only model had an MAE of 12.7 months on the Bağcılar internal test data. Despite less training data, there was no significant difference between the combined and Bağcılar models on the Bağcılar dataset (<i>P</i> > 0.05). The public model showed an MAE of 16.5 months on the Bağcılar dataset, significantly worse than the other models (<i>P</i> < 0.05).</p><p><strong>Conclusion: </strong>We developed an automatic BAA model including the Turkish population, one of the few such studies using deep learning. Despite challenges from population differences and data heterogeneity, these models can be effectively used in various clinical settings. Model accuracy can improve over time with cumulative data, and publicly available datasets may further refine them. Our approach enables more accurate and efficient BAAs, supporting healthcare professionals where traditional methods are time-consuming and variable.</p><p><strong>Clinical significance: </strong>The developed automated BAA model for the Turkish population offers a reliable and efficient alternative to traditional methods. By utilizing de","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"456-464"},"PeriodicalIF":1.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417913/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}