Radiology-Artificial Intelligence最新文献

{"title":"Development of a Deep Learning Algorithm for Posterior Fossa Abnormality Recognition on First-Trimester US Screening Scans: AIRFRAME Study Part 1.","authors":"Alessandra Familiari, Chiara Di Ilio, Andrea Dall'Asta, Enrico Corno, Ruben Ramirez Zegarra, Elvira Di Pasquo, Tiziana Fanelli, Monica Minopoli, Basky Thilaganathan, Carolina Scala, Federico Prefumo, Ricciarda Raffaelli, Alessandra Bovino, Edwin Quarello, Julia Binder, Veronica Falcone, Gianpaolo Grisolia, Jayshree Ramkrishna, Simon Meagher, Huong Elena Tran, Carlotta Bizzarri, Marica Vagni, Luca Boldrini, Paolo Volpe, Tullio Ghi","doi":"10.1148/ryai.250394","DOIUrl":"10.1148/ryai.250394","url":null,"abstract":"<p><p>Purpose To develop a deep learning algorithm to automatically assess the posterior fossa on first-trimester US screening scans and identify open spina bifida (OSB) and cystic posterior fossa (CPF) anomalies. Materials and Methods This was the retrospective part of an international study involving 10 fetal medicine centers. Normal and abnormal (OSB, CPF anomaly) midsagittal fetal brain US images acquired between 11 and 14 weeks of gestation (July 2009-January 2024) with confirmed diagnosis at follow-up were evaluated. Images were manually annotated to delineate the posterior fossa. The dataset was split into a training/validation set (70%) and internal test set (30%). Three convolutional neural networks were trained via threefold cross-validation on the training/validation set, with predictions on the internal test set obtained by ensemble averaging across folds. Model performance in detecting OSB and CPF anomalies was evaluated for the whole cohort and for fetuses with OSB or CPF anomalies separately. Results Images from 251 fetuses were analyzed (mean gestational age [±SD], 12.7 weeks ± 0.65; 150 normal and 101 abnormal [43 OSB and 58 CPF anomalies] images). On the internal test, the MobileNetV3 Large Weights achieved the best performance: area under the receiver operating characteristic curve, 0.94 (95% CI: 0.88, 0.99); accuracy, 88% (67 of 76); recall, 81% (25 of 31); specificity, 93% (42 of 45); precision, 89% (25 of 28); negative predictive value, 88% (42 of 48); and F1 score, 0.85. OSB was classified more accurately (93% [52 of 56] vs 88% [57 of 65]; <i>P</i> = .38) and with higher recall (91% [10 of 11] vs 75% [15 of 20]), although the difference was not significant (<i>P</i> = .38). Conclusion MobileNetV3 Large Weights accurately assessed the fetal posterior fossa between 11 and 14 weeks of gestation, distinguishing normal images from those showing OSB or CPF anomalies. Clinical trial registration no. NCT0579047 <b>Keywords:</b> Artificial Intelligence, First Trimester Ultrasound Screening, Fetal Brain Anomalies, Deep Learning <i>Supplemental material is available for this article.</i> © RSNA, 2026 See also commentary by Rafful in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250394"},"PeriodicalIF":20.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146012556","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":"Turning Routine CT into Reference Values for Quantitative Radiology.","authors":"Yuan Chai, Yu Shi","doi":"10.1148/ryai.251130","DOIUrl":"10.1148/ryai.251130","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"8 2","pages":"e251130"},"PeriodicalIF":13.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158806","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":"Impact of Label Noise from Large Language Model-generated Annotations on Evaluation of Diagnostic Model Performance.","authors":"Mohammadreza Chavoshi, Hari Trivedi, Aawez Mansuri, Janice Newsome, Chiratidzo Rudado Sanyika, Rohan Satya Isaac, Frank Li, Theo Dapamede, Judy Wawira Gichoya","doi":"10.1148/ryai.250477","DOIUrl":"10.1148/ryai.250477","url":null,"abstract":"<p><p>Purpose To systematically examine how large language model (LLM)-generated label noise impacts real-world evaluation of artificial intelligence (AI) binary classification model performance. Materials and Methods A simulation framework was developed to evaluate how LLM label errors affect estimated model performance. A synthetic dataset (10 000 cases) was generated across low- (10% and 30%) and high-prevalence (70% and 90%) conditions. LLM sensitivity and specificity values varied independently from 90% to 100%. AI binary classification models were simulated, with true performance ranging from 90% to 100% for sensitivity and specificity. Apparent performance was calculated with LLM-generated labels as the reference standard. Best- and worst-case performance bounds were calculated analytically, and empirical uncertainty distributions were obtained via Monte Carlo trials. Results Apparent performance was highly sensitive to LLM label quality, with estimation bias strongly modulated by disease prevalence. In low-prevalence settings, small reductions in LLM specificity substantially underestimated model sensitivity. For example, at 10% prevalence, an LLM with 90% specificity yielded an apparent sensitivity of ~53% despite being a perfect model. In high-prevalence conditions, LLM sensitivity reduction led to model specificity underestimation. At 90% prevalence, lowering LLM sensitivity from 100% to 90% reduced apparent specificity from 100% to ~53%, despite perfect true specificity. Monte Carlo simulations revealed consistent downward bias, with apparent values often falling below the true model performance even when within theoretical error bounds. Conclusion LLM-generated labels can introduce systematic prevalence-dependent bias into model evaluation. In low-prevalence tasks, ensuring high LLM specificity during label extraction was critical, as false-positive labels disproportionately biased estimated sensitivity and led to model performance underestimation. <b>Keywords:</b> Large Language Models, Report Labeling, Model Deployment, Diagnostic Performance, Observer Performance, Outcomes Analysis <i>Supplemental material is available for this article.</i> © RSNA, 2025 See also commentary by Maiter and Zapaishchykova in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250477"},"PeriodicalIF":13.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13019331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145821204","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":"Agentic AI in Radiology: Evolution from Large Language Models to Future Clinical Integration.","authors":"Bardia Khosravi, Pouria Rouzrokh, Tugba Akinci D'Antonoli, Mana Moassefi, Shahriar Faghani, Aawez Mansuri, Keno Bressem, Ali Tejani, Judy Gichoya","doi":"10.1148/ryai.250651","DOIUrl":"10.1148/ryai.250651","url":null,"abstract":"<p><p>The introduction of foundational models, specifically large language models, has promised a health care transformation. However, the field is rapidly evolving toward autonomous agent systems, defined as artificial intelligence (AI) entities that perceive and react to their environment to achieve specific goals-representing a paradigm shift from passive information retrieval to proactive, goal-oriented clinical assistance. Agentic AI systems transcend static knowledge limitations through core capabilities including persistent memory systems that maintain context across patient encounters, knowledge retrieval tools connecting to medical repositories through retrieval-augmented generation techniques, and computer use functionality enabling navigation of clinical software interfaces. Agentic workflows introduce sophisticated coordination mechanisms including hierarchical, collaborative, and sequential patterns demonstrating superior performance compared with single-agent approaches. Multiagent systems can autonomously coordinate entire clinical workflows across the entire radiology life cycle, from preacquisition protocol optimization through initial image analysis, specialized tool deployment, and preliminary report generation. However, successful clinical deployment requires systematic consideration of complexity thresholds, economic sustainability, cybersecurity frameworks, bias mitigation strategies, and appropriate governance structures. Critical challenges include managing the probabilistic nature of underlying models within deterministic clinical workflows, ensuring adequate human supervision, and preventing overcomplication of established processes. A structured four-phase implementation roadmap addresses these considerations through incremental progression from low-risk automation to comprehensive workflow orchestration while maintaining rigorous safety standards. As foundation models advance and interoperability standards mature, agentic AI will reshape radiology practice paradigms. Success depends on resolving stakeholder responsibility questions while orchestrating technological capabilities with clinical accountability, ensuring autonomous systems augment rather than replace professional judgment in pursuit of improved patient outcomes. <b>Keywords:</b> Informatics, Named Entity Recognition, Patient Scheduling/No-Show Prediction, Resource Allocation, Impact of AI on Education, Artificial Intelligence, Large Language Models, Agentic AI, Multi-Agent Systems, Radiology Workflow, Clinical Decision Support, Health Care Automation © RSNA, 2026.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250651"},"PeriodicalIF":20.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145967176","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":"The Cost of Convenience: How Labeling Errors by Large Language Models Can Distort Evaluations of AI Performance.","authors":"Ahmed Maiter, Anna Zapaishchykova","doi":"10.1148/ryai.251133","DOIUrl":"10.1148/ryai.251133","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"8 2","pages":"e251133"},"PeriodicalIF":13.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146120362","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":"A Taxonomy of Machine Hallucination in Radiology.","authors":"Frank J Brooks, Mark A Anastasio","doi":"10.1148/ryai.250203","DOIUrl":"10.1148/ryai.250203","url":null,"abstract":"<p><p>Measuring the rate of machine hallucination is critical to assessing the utility and trustworthiness of generative artificial intelligence (AI) deployed in radiology; however, there are multiple widely used notions of what constitutes hallucination. This ambiguity pervades industry, academia, and regulatory discourse, such that even an experienced radiologist cannot be certain that a generative model has been evaluated using a definition of hallucination aligned with clinical use. As a result, the same generative AI system may be characterized as either never hallucinating or always hallucinating, depending on perspective. This article provides a brief, nontechnical explanation of the fundamental disparity between two seemingly incongruous notions of machine hallucination. Using terms familiar to radiologists, a taxonomy of machine hallucination is proposed that explicitly delineates output contingencies for deployed AI systems. By clarifying what constitutes hallucination under different interpretations, this framework aims to reduce ambiguity and facilitate clearer communication among users, developers, vendors, and administrators regarding the performance of generative AI in radiology. <b>Keywords:</b> Case-based Reasoning, Convolutional Neural Network © RSNA, 2026.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250203"},"PeriodicalIF":20.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13019329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147436170","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":"Body Charts from CT Segmentations across the Adult Lifespan: Large-scale Cross-sectional and Longitudinal Analyses.","authors":"Christian Wachinger, Bernhard Renger, Christopher Späth, Marcus R Makowski","doi":"10.1148/ryai.250506","DOIUrl":"10.1148/ryai.250506","url":null,"abstract":"<p><p>Purpose To model the distribution of CT-derived whole-body anatomic volumes across adulthood and establish comprehensive cross-sectional and longitudinal reference charts, addressing the current lack of nonbrain CT-based whole-body standards. Materials and Methods Retrospective CT scans acquired from March 2017 to April 2025 (189 710 scans, 106 563 patients) from the institutional picture archiving and communication system and two external datasets (19 393 and 1158 patients, respectively) were automatically segmented into 104 structures (totaling 7.8 million volumes). An automated quality control pipeline, incorporating a novel outlier removal strategy based on strong correlation between organ sizes, ensured data reliability. Cross-sectional normative models were constructed using generalized additive models for location, scale, and shape to capture nonlinear age effects through fractional polynomial functions. A generalized additive mixed model was used for longitudinal analyses to assess within-patient changes over follow-up visits. Results All anatomic structures followed complex, nonlinear age trajectories, with marked sex differences and distinct CT contrast material effects on vascular structures. Bootstrap resampling confirmed the stability and precision of these volume trajectories in both central tendency and variability. An exemplary cardiomegaly case-control analysis showed significantly increased centile scores (<i>P</i> < .001) for heart volume. The longitudinal analysis further revealed significant age-sex interactions influencing within-patient trajectories. Conclusion Cross-sectional and longitudinal reference models were developed from CT-derived anatomic volumes that map the trajectories of structural body changes across adulthood. These body charts facilitate robust quantification of individual deviations via centile scores. <b>Keywords:</b> Anatomy, Statistics, Multivariate Adaptive Regression Splines (MARS), Regression Algorithms, Machine Learning Algorithms, Segmentation <i>Supplemental material is available for this article.</i> © RSNA, 2026. See also commentary by Chai and Shi in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250506"},"PeriodicalIF":13.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145821197","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":"Self-Supervised Text-Vision Alignment for Automated Brain MRI Abnormality Detection: A Multicenter Study (ALIGN Study).","authors":"David A Wood, Emily Guilhem, Sina Kafiabadi, Ayisha Al Busaidi, Kishan Dissanayake, Ahmed Hammam, Nina Mansoor, Matthew Townend, Siddharth Agarwal, Yiran Wei, Asif Mazumder, Gareth J Barker, Peter Sasieni, Sébastien Ourselin, James H Cole, Nikhil Nair, Anil Geetha, Chike Onyekwuluje, Rob Dineen, Permesh Dhillon, Carolyn Costigan, Kavi Fatania, Mark Igra, Rebecca Nichols, Janak Saada, Arne Juette, Ramona-Rita Barbara, Hilmar Spohr, Thomas C Booth","doi":"10.1148/ryai.240619","DOIUrl":"10.1148/ryai.240619","url":null,"abstract":"<p><p>Purpose To develop a self-supervised text-vision framework to detect abnormalities on brain MRI scans by leveraging free-text neuroradiology reports, eliminating the need for expert-labeled training datasets. Materials and Methods This retrospective and prospective multicenter study included 81 936 brain MRI examinations and corresponding radiology reports for adult patients at two UK National Health Service hospitals from January 2008 to December 2019 for training and internal testing and 1369 prospectively collected examinations between March 2022 and March 2024 from four separate National Health Service hospitals for external testing (ClinicalTrials.gov no. NCT04368481). A neuroradiology language model (NeuroBERT) was trained using self-supervised tasks to generate report embeddings. Convolutional neural networks (one per MRI sequence) were trained to map scans to embeddings by minimizing mean squared error loss. The framework then detected abnormalities in new examinations by scoring scans against query sentences using text-image similarity. Model diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC). Results The framework achieved an AUC of 0.95 (95% CI: 0.94, 0.97) for normal versus abnormal classification and generalized to external sites with examination-level AUCs of 0.90 (95% CI: 0.86, 0.93) in Bedford, 0.87 (95% CI: 0.83, 0.90) in Nottingham, 0.86 (95% CI: 0.83, 0.90) in Norwich, and 0.85 (95% CI: 0.81, 0.89) in Yeovil. In five zero-shot classification tasks-acute stroke, multiple sclerosis, intracranial hemorrhage, meningioma, and hydrocephalus-the framework achieved a mean AUC of 0.89 (range, 0.77-0.93). For visual-semantic image retrieval, mean precision was 0.84 among the top 15 images across seven pathologies. Conclusion The self-supervised text-vision framework accurately detected brain MRI abnormalities without expert-labeled datasets. Clinical trial registration no. NCT04368481 <b>Keywords:</b> Head and Neck, Unsupervised Learning, Convolutional Neural Network (CNN), Neuroradiology © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY 4.0 license. <i>Supplemental material is available for this article.</i> See also commentary by Ghodasara in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240619"},"PeriodicalIF":20.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13019336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145606735","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":"2025 Manuscript Reviewers: A Note of Thanks.","authors":"Jeffrey S Klein, Charles E Kahn","doi":"10.1148/ryai.260176","DOIUrl":"https://doi.org/10.1148/ryai.260176","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"8 2","pages":"e260176"},"PeriodicalIF":13.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147436177","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":"The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset.","authors":"Tyler J Richards, Adam E Flanders, Errol Colak, Luciano M Prevedello, Robyn L Ball, Felipe Kitamura, John Mongan, Maryam Vazirabad, Hui-Ming Lin, Anne Kendell, Thanat Kanthawang, Salita Angkurawaranon, Emre Altinmakas, Hakan Dogan, Paulo Eduardo de Aguiar Kuriki, Arjuna Somasundaram, Christopher Rushton, Deniz Bulja, Naida Spahović, Jennifer Sommer, Sirui Jiang, Eduardo Moreno Júdice de Mattos Farina, Eduardo Caminha Nunes, Michael Brassil, Megan McNamara, Johanna Ortiz, Jacob Peoples, Vinson L Uytana, Anthony Kam, Venkata N S Dola, Daniel Murphy, David Vu, Arsany Hakim, Jason F Talbott","doi":"10.1148/ryai.250480","DOIUrl":"10.1148/ryai.250480","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250480"},"PeriodicalIF":13.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145967166","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}