Radiology-Artificial Intelligence最新文献

{"title":"Deep Learning for Coronary Stenosis Detection in Heavily Calcified Plaques at Coronary CT Angiography: A Stepwise, Multicenter Study.","authors":"Rui Wang, Siwen Wang, LiBo Zhang, U Joseph Schoepf, Fandong Zhang, Wei Chen, Zhen Zhou, Zhe Fang, Bin Hu, Yizhou Yu, Jiayin Zhang, Ximing Wang, Longjiang Zhang, Lei Xu","doi":"10.1148/ryai.250109","DOIUrl":"10.1148/ryai.250109","url":null,"abstract":"<p><p>Purpose To develop and validate a deep learning (DL) model for automated assessment of coronary stenosis in vessels with heavily calcified plaques at coronary CT angiography (CCTA), using quantitative coronary angiography as the reference standard. Materials and Methods A total of 10 101 CCTA examinations (June 2017-December 2020) from three tertiary hospitals in China were retrospectively collected for DL model development. External testing dataset 1 included 442 CCTA examinations (Agatston score > 300) from two independent hospitals (January 2021-May 2022) for performance evaluation. The separate external testing dataset 2 of 120 CCTA examinations was used for a reader study assessing whether DL assistance improved diagnostic accuracy among junior, attending, and senior radiologists. External testing dataset 3 included 150 prospectively collected CCTA examinations (June-July 2023) that were analyzed to compare model performance against clinical reports, simulating real-world deployment. Model diagnostic performance was assessed using receiver operating characteristic analysis, with quantitative coronary angiography as the reference. Results In external testing dataset 1, specificities for detecting 50% or more stenosis were 78%, 72%, and 48% and the areas under the receiver operating characteristic curve (AUC) were 0.89, 0.90, and 0.87 at the segment, vessel, and patient levels, respectively. In external testing dataset 2, DL assistance improved radiologist specificity by 7%-11% (<i>P</i> < .001) with improving AUC and increased interreader agreement (Δκ = 0.155-0.228; <i>P</i> < .05). In external testing dataset 3, the model demonstrated 53% specificity and a higher AUC versus clinical reports (0.91 vs 0.76; <i>P</i> < .001). Conclusion The proposed DL model accurately detected coronary stenosis of heavily calcified plaques at CCTA and improved diagnostic performance of radiologists. <b>Keywords:</b> CT Angiography, Cardiac, Heart, Arteriosclerosis, Calcifications, Calculi, Quantification, Diagnosis <i>Supplemental material is available for this article.</i> © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY 4.0 license. See also commentary by Maiter and Alabed in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250109"},"PeriodicalIF":13.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145769353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Natural Language Processing to Refine Normative Pediatric Renal US Values.","authors":"Irvine Sihlahla","doi":"10.1148/ryai.250979","DOIUrl":"10.1148/ryai.250979","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"8 1","pages":"e250979"},"PeriodicalIF":13.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145913239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reference Trajectories of Extra-Axial Cerebrospinal Fluid during Childhood and Adolescence Defined in a Clinically Acquired MRI Dataset.","authors":"Ayan S Mandal, Lena Dorfschmidt, Jenna M Schabdach, Margaret Gardner, Benjamin E Yerys, Richard A I Bethlehem, Susan Sotardi, M Katherine Henry, Joanne N Wood, Barbara H Chaiyachati, Aaron Alexander-Bloch, Jakob Seidlitz","doi":"10.1148/ryai.250123","DOIUrl":"10.1148/ryai.250123","url":null,"abstract":"<p><p>Purpose To build extra-axial cerebrospinal fluid (eaCSF) growth charts that define key diagnostic criteria for benign enlargement of the subarachnoid space (BESS) by providing an age-related reference benchmark to aid in assessing atypical eaCSF development. Materials and Methods In this retrospective study, T1-weighted MRI scans from patients who underwent imaging at a pediatric health care system between January 2004 and December 2023 were accessed to form a clinical control group. Nine scans from patients diagnosed with BESS by a board-certified pediatric neuroradiologist were also reviewed. T1-weighted scans were segmented into various tissue types, including eaCSF. Growth charts of eaCSF were modeled using the clinical control group. The results of patients with confirmed BESS were then benchmarked against these charts to test the performance of the eaCSF growth charts. Generalized additive models of location, scale, and shape were used. Results The eaCSF measurements were obtained for 1205 patients (619 female; age range, 0.19-19.6 years). Measurements show that eaCSF evolved dynamically with age, steadily decreasing from birth to 2 years, then trending upward in childhood. Seven of the nine patients with a clinical diagnosis of BESS had eaCSF measurements above the 97.5th percentile for at least one measurement. Percentile scores distinguished patients with BESS from controls with areas under the receiver operating characteristic curve of greater than 0.95. Conclusion MRI-derived eaCSF measurements evolved dynamically throughout early life. Patients with atypical CSF development could be differentiated from clinical controls using computational measurements paired with normative modeling. <b>Keywords:</b> MRI, Brain/Brain Stem, Pediatrics, Benign Enlargement of Subarachnoid Space <i>Supplemental material is available for this article.</i> © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY-NC-ND license.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250123"},"PeriodicalIF":20.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12856361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145716001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping the AI \"Mind\": What the AI-STREAM Trial Reveals About Cancers Detected and Missed.","authors":"Synho Do, Manisha Bahl","doi":"10.1148/ryai.251002","DOIUrl":"10.1148/ryai.251002","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"8 1","pages":"e251002"},"PeriodicalIF":13.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145769505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visualizing Radiologic Connections: An Explainable Coarse-to-Fine Foundation Model with Multiview Mammograms and Associated Reports.","authors":"Yuan Gao, Hong-Yu Zhou, Xin Wang, Antonio Portaluri, Tianyu Zhang, Regina Beets-Tan, Luyi Han, Chunyao Lu, Laura Estacio, Anna D'Angelo, Stephan Ursprung, Yizhou Yu, Jonas Teuwen, Tao Tan, Ritse Mann","doi":"10.1148/ryai.240646","DOIUrl":"10.1148/ryai.240646","url":null,"abstract":"<p><p>Purpose To develop a foundational pretraining method for digital mammography that extracts fine-grained visual-language representations from images and reports in label-limited settings. Materials and Methods A multiview mammogram-report pretraining framework for automated breast cancer analysis was developed using retrospectively collected data from January 2010 to December 2020. This framework provides visual explanations of the model's learning, allowing researchers to \"visualize what you learn.\" The abnormality-aware technique was tailored to mammogram characteristics of dense fibroglandular tissue. The proposed framework was evaluated on downstream tasks from four external medical centers, involving label-efficient abnormality recognition in mammograms, including malignancy classification, segmentation, and localization. Statistical analyses were performed using the DeLong test and paired <i>t</i> test for area under the receiver operating characteristic curve and Dice scores, respectively. Results The visualization results, including abnormality-enhanced mammograms and abnormality-awareness maps, could explain that the developed model successfully captures relationships between multiview mammograms and corresponding reports. This reduces the false positives for breast cancer by 37% and enables zero-shot abnormality segmentation. Furthermore, the developed model consistently outperformed existing approaches in fine-tuning for both malignancy classification (area under the receiver operating characteristic curve, INbreast: 0.90 vs 0.78 [<i>P</i> < .001]; Curated Breast Imaging Subset of Digital Database for Screening Mammography [CBIS-DDSM]: 0.85 vs 0.79 [<i>P</i> < .01]; Chinese Mammography Database: 0.85 vs 0.78 [<i>P</i> < .001]; and Cohort of Screen-age Women-Case Control: 0.86 vs 0.77 [<i>P</i> < .001]) and segmentation and localization (Dice score, INbreast: 0.75 vs 0.63 [<i>P</i> < .001]; CBIS-DDSM: 0.76 vs 0.61 [<i>P</i> < .001]). Conclusion The proposed framework enhances interpretability and fine-grained multimodal foundational learning for multiview mammograms and reports. <b>Keywords:</b> Mammography, Breast, Segmentation, Feature Detection, Quantification, Diagnosis, Translation, Transfer Learning, Unsupervised Learning, Breast Cancer, Representation Learning, Visual-Language Foundation Model, Explainable AI <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240646"},"PeriodicalIF":13.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145769463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economic Value of AI in Radiology: A Systematic Review.","authors":"Isabel Molwitz, Inka Ristow, Jennifer Erley, Tugba Akinci D'Antonoli, Ali S Tejani, Michail E Klontzas, Merel Huisman, Gerhard Adam, Stephan Nüesch, Lisa Adams","doi":"10.1148/ryai.250090","DOIUrl":"10.1148/ryai.250090","url":null,"abstract":"<p><p>Purpose To summarize the evidence of artificial intelligence's (AI's) economic value across the radiologic workflow. Materials and Methods A comprehensive search of PubMed, Business Source Ultimate, and EconLit was conducted for original research articles published between January 2010 and November 2024. Medical Subject Headings and keywords included \"artificial intelligence/machine learning/deep learning/natural language processing,\" \"radiology,\" and \"economic value/cost/budget/revenue/efficiency.\" Studies were selected based on explicit quantification of economic outcomes, excluding those with only soft outcome criteria like time savings without cost quantification. Study quality was assessed using the Criteria for Health Economic Quality Evaluation. Results From the initial 1879 search results, 21 studies (1%) met the inclusion criteria. The majority evaluated machine learning tools (10 of 21[48%], nine on deep learning), followed by computer-assisted diagnosis (CAD, seven of 21 [33%]), natural language processing (NLP, two of 21 [10%]), and hypothetical AI models (two of 21 [10%]). AI demonstrated economic value through cost savings or incremental cost-effectiveness ratios in resource-intensive tasks, when accuracy matched human performance and costs were fixed. For instance, AI-based lung cancer screening achieved incremental cost savings of up to $242 U.S. dollars (USD) per patient. AI increased costs when specificity was lower than humans' or when using pay-per-use models, as observed with CAD systems raising mammography screening costs by up to $19 USD per patient. In fast tasks such as radiograph evaluations, AI showed value in settings with radiologist shortages. AI reduced costs through protocol optimization and increased revenue via improved follow-up compliance. Conclusion AI's value in radiology is context dependent, varying with task complexity, examination volume, and implementation model. Further high-quality economic evaluations are essential. <b>Keywords:</b> Cost-effectiveness, Efficacy Studies, Artificial Intelligence, Radiology, Healthcare Economics, Systematic Review <i>Supplemental material is available for this article.</i> © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY-NC-ND license. See also the commentary by Amindarolzarbi and Siegel in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250090"},"PeriodicalIF":13.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145551412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"US-derived Pediatric Kidney Length and Volume Percentiles by Age: A Big Data Approach.","authors":"Bernarda Viteri, Tatiana Morales-Tisnés, Joey Logan, Jarcy Zee, Summer Kaplan, Susan J Back, Erum A Hartung, Madhura Pradhan, Lisa Guay-Woodford, Susan L Furth, Sandra Amaral, Gregory E Tasian, Hansel J Otero","doi":"10.1148/ryai.250056","DOIUrl":"10.1148/ryai.250056","url":null,"abstract":"<p><p>Purpose To calculate new pediatric age-specific normative values and percentiles for kidney length and volume through the use of a natural language processing (NLP) model. Materials and Methods In this cross-sectional study, 24 664 US reports from 18 769 children (birth to 18 years) conducted between January 2012 and December 2022 at a tertiary children's hospital in the northeastern United States were analyzed with an NLP model. Anthropometric data from 12 595 children were used to evaluate the effect of sex and body measurements on kidney length and volume through age-adjusted quantile regression models. Age-related percentiles were established after calibration, using the lambda-mu-sigma (LMS) method by age (year), with detailed subcategories for children younger than 1 year. Volume percentiles by body surface area were also generated using the LMS method. Results A total of 24 664 reports from 18 769 children were included (median age, 7 years [IQR, 11 years]; 10 134 female children). Normative value analysis showed that kidney growth was more pronounced in the 1st year of life (1.8-cm increase in length and 16.9-cm³ increase in volume). The large sample size resulted in standard errors that were 10%-30% less than previous normative values. Quantile regression models showed that body surface area was a better predictor of kidney volume than was age (R¹ = 0.57 [<i>P</i> < .001] vs 0.48 [<i>P</i> < .001]). Conclusion New LMS percentiles for kidney size were established using data from a large pediatric sample. <b>Keywords:</b> Kidney, Natural Language Processing, Pediatrics, Ultrasound <i>Supplemental material is available for this article.</i> © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY 4.0 license See also the commentary by Sihlahla in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250056"},"PeriodicalIF":20.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12853003/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145551132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Futile to Feasible: Improving Assessment of Stenosis in Heavily Calcified Coronary Arteries.","authors":"Ahmed Maiter, Samer Alabed","doi":"10.1148/ryai.251077","DOIUrl":"10.1148/ryai.251077","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"8 1","pages":"e251077"},"PeriodicalIF":13.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146067574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Random Convolutions for Domain Generalization of Deep Learning-based Medical Image Segmentation Models.","authors":"Daniel Scholz, Ayhan Can Erdur, Jan C Peeken, Aswathi Varma, Robert Graf, Jan S Kirschke, Daniel Rueckert, Benedikt Wiestler","doi":"10.1148/ryai.240502","DOIUrl":"10.1148/ryai.240502","url":null,"abstract":"<p><p>Purpose To evaluate random convolutions as an augmentation strategy for improving domain generalization of deep learning-based segmentation models in medical imaging. Materials and Methods In this retrospective study, a random convolution-based augmentation strategy was applied to abdominal organ segmentation (AbdomenCT-1k dataset: 361 CT images; Abdominal Multi Organ Segmentation [AMOS] dataset: 298 CT and 59 MRI scans) and brain tissue segmentation (Information eXtraction from Images [IXI] dataset: 504 T1-weighted images from Guy's and Hammersmith Hospitals, 146 paired T1-weighted and T2-weighted images from the Institute of Psychiatry). Performance was compared with baseline and state-of-the-art segmentation models (TotalSegmentator and deepAtropos). Random convolution configurations were analyzed for effects on in- and out-of-domain performance. Results The random convolution-enhanced U-Net achieved in-domain Dice scores comparable to state-of-the-art baselines (CT: 0.93 vs TotalSegmentator: 0.95; T1-weighted imaging: 0.83 vs deepAtropos: 0.79). Out-of-domain Dice scores were significantly higher (MRI: 0.93, T2-weighted imaging: 0.52) compared with baselines (TotalSegmentator in MRI: 0.85, deepAtropos in T2-weighted imaging: 0.33; false discovery rate-adjusted <i>P</i> < .001). Augmentation probability and configuration influenced the trade-off between in- and out-of-domain performance. Conclusion Random convolutions yielding more robust segmentation models generalized better to unseen domains than models trained without random convolutions and are compatible with diverse segmentation architectures. <b>Keywords:</b> MR-Imaging, CT, Supervised Learning, Segmentation, Abdomen/GI, Experimental Investigations <i>Supplemental material is available for this article.</i> © RSNA, 2025 See also commentary by Mathai in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240502"},"PeriodicalIF":13.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145550006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Randomness: Can It Serve as a Bridge to Domain Generalization?","authors":"Tejas Sudharshan Mathai","doi":"10.1148/ryai.251014","DOIUrl":"10.1148/ryai.251014","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"8 1","pages":"e251014"},"PeriodicalIF":13.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145967197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}