Radiology-Artificial Intelligence最新文献

{"title":"Accuracy of Fully Automated and Human-assisted Artificial Intelligence-based CT Quantification of Pleural Effusion Changes after Thoracentesis.","authors":"Eui Jin Hwang, Hyunsook Hong, Seungyeon Ko, Seung-Jin Yoo, Hyungjin Kim, Dahee Kim, Soon Ho Yoon","doi":"10.1148/ryai.240215","DOIUrl":"10.1148/ryai.240215","url":null,"abstract":"<p><p>Quantifying pleural effusion change at chest CT is important for evaluating disease severity and treatment response. The purpose of this study was to assess the accuracy of artificial intelligence (AI)-based volume quantification of pleural effusion change on CT images, using the volume of drained fluid as the reference standard. Seventy-nine participants (mean age ± SD, 65 years ± 13; 47 male) undergoing thoracentesis were prospectively enrolled from October 2021 to September 2023. Chest CT scans were obtained just before and after thoracentesis. The volume of pleural fluid on each CT scan, with the difference representing the drained fluid volume, was measured by automated segmentation (fully automated measurement). An expert thoracic radiologist then manually corrected these automated volume measurements (human-assisted measurement). Both fully automated (median percentage error, 13.1%; maximum estimated 95% error, 708 mL) and human-assisted measurements (median percentage error, 10.9%; maximum estimated 95% error, 312 mL) systematically underestimated the volume of drained fluid, beyond the equivalence margin. The magnitude of underestimation increased proportionally to the drainage volume. Agreements between fully automated and human-assisted measurements (intraclass correlation coefficient [ICC], 0.99) and the test-retest reliability of fully automated (ICC, 0.995) and human-assisted (ICC, 0.997) measurements were excellent. These results highlight a potential systematic discrepancy between AI segmentation-based CT quantification of pleural effusion volume change and actual volume change. <b>Keywords:</b> CT-Quantitative, Thorax, Pleura, Segmentation Clinical Research Information Service registration no. KCT0006683 <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240215"},"PeriodicalIF":8.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142984841","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":"Machine Learning to Harmonize Interscanner Variability of Brain MRI Volumetry: Why and How.","authors":"Sven Haller","doi":"10.1148/ryai.240779","DOIUrl":"10.1148/ryai.240779","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 1","pages":"e240779"},"PeriodicalIF":8.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013115","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":"Achieving More with Less: Combining Strong and Weak Labels for Intracranial Hemorrhage Detection.","authors":"Tugba Akinci D'Antonoli, Jeffrey D Rudie","doi":"10.1148/ryai.240670","DOIUrl":"10.1148/ryai.240670","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"6 6","pages":"e240670"},"PeriodicalIF":8.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584303","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":"Addressing the Generalizability of AI in Radiology Using a Novel Data Augmentation Framework with Synthetic Patient Image Data: Proof-of-Concept and External Validation for Classification Tasks in Multiple Sclerosis.","authors":"Gianluca Brugnara, Chandrakanth Jayachandran Preetha, Katerina Deike, Robert Haase, Thomas Pinetz, Martha Foltyn-Dumitru, Mustafa A Mahmutoglu, Brigitte Wildemann, Ricarda Diem, Wolfgang Wick, Alexander Radbruch, Martin Bendszus, Hagen Meredig, Aditya Rastogi, Philipp Vollmuth","doi":"10.1148/ryai.230514","DOIUrl":"10.1148/ryai.230514","url":null,"abstract":"<p><p>Artificial intelligence (AI) models often face performance drops after deployment to external datasets. This study evaluated the potential of a novel data augmentation framework based on generative adversarial networks (GANs) that creates synthetic patient image data for model training to improve model generalizability. Model development and external testing were performed for a given classification task, namely the detection of new fluid-attenuated inversion recovery lesions at MRI during longitudinal follow-up of patients with multiple sclerosis (MS). An internal dataset of 669 patients with MS (<i>n</i> = 3083 examinations) was used to develop an attention-based network, trained both with and without the inclusion of the GAN-based synthetic data augmentation framework. External testing was performed on 134 patients with MS from a different institution, with MR images acquired using different scanners and protocols than images used during training. Models trained using synthetic data augmentation showed a significant performance improvement when applied on external data (area under the receiver operating characteristic curve [AUC], 83.6% without synthetic data vs 93.3% with synthetic data augmentation; <i>P</i> = .03), achieving comparable results to the internal test set (AUC, 95.0%; <i>P</i> = .53), whereas models without synthetic data augmentation demonstrated a performance drop upon external testing (AUC, 93.8% on internal dataset vs 83.6% on external data; <i>P</i> = .03). Data augmentation with synthetic patient data substantially improved performance of AI models on unseen MRI data and may be extended to other clinical conditions or tasks to mitigate domain shift, limit class imbalance, and enhance the robustness of AI applications in medical imaging. <b>Keywords:</b> Brain, Brain Stem, Multiple Sclerosis, Synthetic Data Augmentation, Generative Adversarial Network <i>Supplemental material is available for this article.</i> © RSNA, 2024.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e230514"},"PeriodicalIF":8.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142476382","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":"Boosting Deep Learning for Interpretable Brain MRI Lesion Detection through the Integration of Radiology Report Information.","authors":"Lisong Dai, Jiayu Lei, Fenglong Ma, Zheng Sun, Haiyan Du, Houwang Zhang, Jingxuan Jiang, Jianyong Wei, Dan Wang, Guang Tan, Xinyu Song, Jinyu Zhu, Qianqian Zhao, Songtao Ai, Ai Shang, Zhaohui Li, Ya Zhang, Yuehua Li","doi":"10.1148/ryai.230520","DOIUrl":"10.1148/ryai.230520","url":null,"abstract":"<p><p>Purpose To guide the attention of a deep learning (DL) model toward MRI characteristics of brain lesions by incorporating radiology report-derived textual features to achieve interpretable lesion detection. Materials and Methods In this retrospective study, 35 282 brain MRI scans (January 2018 to June 2023) and corresponding radiology reports from center 1 were used for training, validation, and internal testing. A total of 2655 brain MRI scans (January 2022 to December 2022) from centers 2-5 were reserved for external testing. Textual features were extracted from radiology reports to guide a DL model (ReportGuidedNet) focusing on lesion characteristics. Another DL model (PlainNet) without textual features was developed for comparative analysis. Both models identified 15 conditions, including 14 diseases and normal brains. Performance of each model was assessed by calculating macro-averaged area under the receiver operating characteristic curve (ma-AUC) and micro-averaged AUC (mi-AUC). Attention maps, which visualized model attention, were assessed with a five-point Likert scale. Results ReportGuidedNet outperformed PlainNet for all diagnoses on both internal (ma-AUC, 0.93 [95% CI: 0.91, 0.95] vs 0.85 [95% CI: 0.81, 0.88]; mi-AUC, 0.93 [95% CI: 0.90, 0.95] vs 0.89 [95% CI: 0.83, 0.92]) and external (ma-AUC, 0.91 [95% CI: 0.88, 0.93] vs 0.75 [95% CI: 0.72, 0.79]; mi-AUC, 0.90 [95% CI: 0.87, 0.92] vs 0.76 [95% CI: 0.72, 0.80]) testing sets. The performance difference between internal and external testing sets was smaller for ReportGuidedNet than for PlainNet (Δma-AUC, 0.03 vs 0.10; Δmi-AUC, 0.02 vs 0.13). The Likert scale score of ReportGuidedNet was higher than that of PlainNet (mean ± SD: 2.50 ± 1.09 vs 1.32 ± 1.20; <i>P</i> < .001). Conclusion The integration of radiology report textual features improved the ability of the DL model to detect brain lesions, thereby enhancing interpretability and generalizability. <b>Keywords:</b> Deep Learning, Computer-aided Diagnosis, Knowledge-driven Model, Radiology Report, Brain MRI <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e230520"},"PeriodicalIF":8.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605145/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142393849","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":"The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset.","authors":"Jeffrey D Rudie, Hui-Ming Lin, Robyn L Ball, Sabeena Jalal, Luciano M Prevedello, Savvas Nicolaou, Brett S Marinelli, Adam E Flanders, Kirti Magudia, George Shih, Melissa A Davis, John Mongan, Peter D Chang, Ferco H Berger, Sebastiaan Hermans, Meng Law, Tyler Richards, Jan-Peter Grunz, Andreas Steven Kunz, Shobhit Mathur, Sandro Galea-Soler, Andrew D Chung, Saif Afat, Chin-Chi Kuo, Layal Aweidah, Ana Villanueva Campos, Arjuna Somasundaram, Felipe Antonio Sanchez Tijmes, Attaporn Jantarangkoon, Leonardo Kayat Bittencourt, Michael Brassil, Ayoub El Hajjami, Hakan Dogan, Muris Becircic, Agrahara G Bharatkumar, Eduardo Moreno Júdice de Mattos Farina, Errol Colak","doi":"10.1148/ryai.240101","DOIUrl":"10.1148/ryai.240101","url":null,"abstract":"<p><p>\u0000 <i>Supplemental material is available for this article.</i>\u0000 </p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240101"},"PeriodicalIF":8.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605137/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509376","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":"WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data.","authors":"Krzysztof Bartnik, Tomasz Bartczak, Mateusz Krzyziński, Krzysztof Korzeniowski, Krzysztof Lamparski, Piotr Węgrzyn, Eric Lam, Mateusz Bartkowiak, Tadeusz Wróblewski, Katarzyna Mech, Magdalena Januszewicz, Przemysław Biecek","doi":"10.1148/ryai.240296","DOIUrl":"10.1148/ryai.240296","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240296"},"PeriodicalIF":8.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509377","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":"Breaking Ground on the Application of AI to HCC: It's All about Data.","authors":"Ryan Bitar, Julius Chapiro","doi":"10.1148/ryai.240660","DOIUrl":"10.1148/ryai.240660","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"6 6","pages":"e240660"},"PeriodicalIF":8.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605136/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142733009","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":"Precise Image-level Localization of Intracranial Hemorrhage on Head CT Scans with Deep Learning Models Trained on Study-level Labels.","authors":"Yunan Wu, Michael Iorga, Suvarna Badhe, James Zhang, Donald R Cantrell, Elaine J Tanhehco, Nicholas Szrama, Andrew M Naidech, Michael Drakopoulos, Shamis T Hasan, Kunal M Patel, Tarek A Hijaz, Eric J Russell, Shamal Lalvani, Amit Adate, Todd B Parrish, Aggelos K Katsaggelos, Virginia B Hill","doi":"10.1148/ryai.230296","DOIUrl":"10.1148/ryai.230296","url":null,"abstract":"<p><p>Purpose To develop a highly generalizable weakly supervised model to automatically detect and localize image-level intracranial hemorrhage (ICH) by using study-level labels. Materials and Methods In this retrospective study, the proposed model was pretrained on the image-level Radiological Society of North America dataset and fine-tuned on a local dataset by using attention-based bidirectional long short-term memory networks. This local training dataset included 10 699 noncontrast head CT scans in 7469 patients, with ICH study-level labels extracted from radiology reports. Model performance was compared with that of two senior neuroradiologists on 100 random test scans using the McNemar test, and its generalizability was evaluated on an external independent dataset. Results The model achieved a positive predictive value (PPV) of 85.7% (95% CI: 84.0, 87.4) and an area under the receiver operating characteristic curve of 0.96 (95% CI: 0.96, 0.97) on the held-out local test set (<i>n</i> = 7243, 3721 female) and 89.3% (95% CI: 87.8, 90.7) and 0.96 (95% CI: 0.96, 0.97), respectively, on the external test set (<i>n</i> = 491, 178 female). For 100 randomly selected samples, the model achieved performance on par with two neuroradiologists, but with a significantly faster (<i>P</i> < .05) diagnostic time of 5.04 seconds per scan (vs 86 seconds and 22.2 seconds for the two neuroradiologists, respectively). The model's attention weights and heatmaps visually aligned with neuroradiologists' interpretations. Conclusion The proposed model demonstrated high generalizability and high PPVs, offering a valuable tool for expedited ICH detection and prioritization while reducing false-positive interruptions in radiologists' workflows. <b>Keywords:</b> Computer-Aided Diagnosis (CAD), Brain/Brain Stem, Hemorrhage, Convolutional Neural Network (CNN), Transfer Learning <i>Supplemental material is available for this article.</i> © RSNA, 2024 See also the commentary by Akinci D'Antonoli and Rudie in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e230296"},"PeriodicalIF":8.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142081915","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":"Watch Your Back! How Deep Learning Is Cracking the Real World of CT for Cervical Spine Fractures.","authors":"Riccardo Levi, Letterio S Politi","doi":"10.1148/ryai.240604","DOIUrl":"10.1148/ryai.240604","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"6 6","pages":"e240604"},"PeriodicalIF":8.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142733028","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}