Radiology-Artificial Intelligence最新文献

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Boosting Deep Learning for Interpretable Brain MRI Lesion Detection through the Integration of Radiology Report Information. 通过整合放射学报告信息促进深度学习,实现可解释的脑磁共振成像病灶检测。
IF 8.1
Radiology-Artificial Intelligence Pub Date : 2024-11-01 DOI: 10.1148/ryai.230520
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
{"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}
引用次数: 0
Precise Image-level Localization of Intracranial Hemorrhage on Head CT Scans with Deep Learning Models Trained on Study-level Labels. 利用研究级标签训练的深度学习模型对头部 CT 扫描颅内出血进行图像级精确定位。
IF 8.1
Radiology-Artificial Intelligence Pub Date : 2024-11-01 DOI: 10.1148/ryai.230296
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
{"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}
引用次数: 0
WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data. WAW-TACE:包含分割、放射组学特征和临床数据的肝细胞癌多相 CT 数据集。
IF 8.1
Radiology-Artificial Intelligence Pub Date : 2024-11-01 DOI: 10.1148/ryai.240296
Krzysztof Bartnik, Tomasz Bartczak, Mateusz Krzyziński, Krzysztof Korzeniowski, Krzysztof Lamparski, Piotr Węgrzyn, Eric Lam, Mateusz Bartkowiak, Tadeusz Wróblewski, Katarzyna Mech, Magdalena Januszewicz, Przemysław Biecek
{"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}
引用次数: 0
Breaking Ground on the Application of AI to HCC: It's All about Data. 将人工智能应用于 HCC 的突破性进展:关键在于数据。
IF 8.1
Radiology-Artificial Intelligence Pub Date : 2024-11-01 DOI: 10.1148/ryai.240660
Ryan Bitar, Julius Chapiro
{"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}
引用次数: 0
The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset. RSNA 腹部创伤 CT (RATIC) 数据集。
IF 8.1
Radiology-Artificial Intelligence Pub Date : 2024-11-01 DOI: 10.1148/ryai.240101
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
{"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}
引用次数: 0
Watch Your Back! How Deep Learning Is Cracking the Real World of CT for Cervical Spine Fractures. 小心背后!深度学习如何破解颈椎骨折 CT 的真实世界。
IF 8.1
Radiology-Artificial Intelligence Pub Date : 2024-11-01 DOI: 10.1148/ryai.240604
Riccardo Levi, Letterio S Politi
{"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}
引用次数: 0
AI-integrated Screening to Replace Double Reading of Mammograms: A Population-wide Accuracy and Feasibility Study. 人工智能整合筛查取代乳房 X 光片双读:全人口准确性和可行性研究。
IF 8.1
Radiology-Artificial Intelligence Pub Date : 2024-11-01 DOI: 10.1148/ryai.230529
Mohammad T Elhakim, Sarah W Stougaard, Ole Graumann, Mads Nielsen, Oke Gerke, Lisbet B Larsen, Benjamin S B Rasmussen
{"title":"AI-integrated Screening to Replace Double Reading of Mammograms: A Population-wide Accuracy and Feasibility Study.","authors":"Mohammad T Elhakim, Sarah W Stougaard, Ole Graumann, Mads Nielsen, Oke Gerke, Lisbet B Larsen, Benjamin S B Rasmussen","doi":"10.1148/ryai.230529","DOIUrl":"10.1148/ryai.230529","url":null,"abstract":"<p><p>Mammography screening supported by deep learning-based artificial intelligence (AI) solutions can potentially reduce workload without compromising breast cancer detection accuracy, but the site of deployment in the workflow might be crucial. This retrospective study compared three simulated AI-integrated screening scenarios with standard double reading with arbitration in a sample of 249 402 mammograms from a representative screening population. A commercial AI system replaced the first reader (scenario 1: integrated AI<sub>first</sub>), the second reader (scenario 2: integrated AI<sub>second</sub>), or both readers for triaging of low- and high-risk cases (scenario 3: integrated AI<sub>triage</sub>). AI threshold values were chosen based partly on previous validation and setting the screen-read volume reduction at approximately 50% across scenarios. Detection accuracy measures were calculated. Compared with standard double reading, integrated AI<sub>first</sub> showed no evidence of a difference in accuracy metrics except for a higher arbitration rate (+0.99%, <i>P</i> < .001). Integrated AI<sub>second</sub> had lower sensitivity (-1.58%, <i>P</i> < .001), negative predictive value (NPV) (-0.01%, <i>P</i> < .001), and recall rate (-0.06%, <i>P</i> = .04) but a higher positive predictive value (PPV) (+0.03%, <i>P</i> < .001) and arbitration rate (+1.22%, <i>P</i> < .001). Integrated AI<sub>triage</sub> achieved higher sensitivity (+1.33%, <i>P</i> < .001), PPV (+0.36%, <i>P</i> = .03), and NPV (+0.01%, <i>P</i> < .001) but lower arbitration rate (-0.88%, <i>P</i> < .001). Replacing one or both readers with AI seems feasible; however, the site of application in the workflow can have clinically relevant effects on accuracy and workload. <b>Keywords:</b> Mammography, Breast, Neoplasms-Primary, Screening, Epidemiology, Diagnosis, Convolutional Neural Network (CNN) <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":"e230529"},"PeriodicalIF":8.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605135/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142126863","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}
引用次数: 0
Assessing the Performance of Models from the 2022 RSNA Cervical Spine Fracture Detection Competition at a Level I Trauma Center. 评估 2022 年 RSNA 颈椎骨折检测竞赛模型在一级创伤中心的性能。
IF 8.1
Radiology-Artificial Intelligence Pub Date : 2024-11-01 DOI: 10.1148/ryai.230550
Zixuan Hu, Markand Patel, Robyn L Ball, Hui Ming Lin, Luciano M Prevedello, Mitra Naseri, Shobhit Mathur, Robert Moreland, Jefferson Wilson, Christopher Witiw, Kristen W Yeom, Qishen Ha, Darragh Hanley, Selim Seferbekov, Hao Chen, Philipp Singer, Christof Henkel, Pascal Pfeiffer, Ian Pan, Harshit Sheoran, Wuqi Li, Adam E Flanders, Felipe C Kitamura, Tyler Richards, Jason Talbott, Ervin Sejdić, Errol Colak
{"title":"Assessing the Performance of Models from the 2022 RSNA Cervical Spine Fracture Detection Competition at a Level I Trauma Center.","authors":"Zixuan Hu, Markand Patel, Robyn L Ball, Hui Ming Lin, Luciano M Prevedello, Mitra Naseri, Shobhit Mathur, Robert Moreland, Jefferson Wilson, Christopher Witiw, Kristen W Yeom, Qishen Ha, Darragh Hanley, Selim Seferbekov, Hao Chen, Philipp Singer, Christof Henkel, Pascal Pfeiffer, Ian Pan, Harshit Sheoran, Wuqi Li, Adam E Flanders, Felipe C Kitamura, Tyler Richards, Jason Talbott, Ervin Sejdić, Errol Colak","doi":"10.1148/ryai.230550","DOIUrl":"10.1148/ryai.230550","url":null,"abstract":"<p><p>Purpose To evaluate the performance of the top models from the RSNA 2022 Cervical Spine Fracture Detection challenge on a clinical test dataset of both noncontrast and contrast-enhanced CT scans acquired at a level I trauma center. Materials and Methods Seven top-performing models in the RSNA 2022 Cervical Spine Fracture Detection challenge were retrospectively evaluated on a clinical test set of 1828 CT scans (from 1829 series: 130 positive for fracture, 1699 negative for fracture; 1308 noncontrast, 521 contrast enhanced) from 1779 patients (mean age, 55.8 years ± 22.1 [SD]; 1154 [64.9%] male patients). Scans were acquired without exclusion criteria over 1 year (January-December 2022) from the emergency department of a neurosurgical and level I trauma center. Model performance was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. False-positive and false-negative cases were further analyzed by a neuroradiologist. Results Although all seven models showed decreased performance on the clinical test set compared with the challenge dataset, the models maintained high performances. On noncontrast CT scans, the models achieved a mean AUC of 0.89 (range: 0.79-0.92), sensitivity of 67.0% (range: 30.9%-80.0%), and specificity of 92.9% (range: 82.1%-99.0%). On contrast-enhanced CT scans, the models had a mean AUC of 0.88 (range: 0.76-0.94), sensitivity of 81.9% (range: 42.7%-100.0%), and specificity of 72.1% (range: 16.4%-92.8%). The models identified 10 fractures missed by radiologists. False-positive cases were more common in contrast-enhanced scans and observed in patients with degenerative changes on noncontrast scans, while false-negative cases were often associated with degenerative changes and osteopenia. Conclusion The winning models from the 2022 RSNA AI Challenge demonstrated a high performance for cervical spine fracture detection on a clinical test dataset, warranting further evaluation for their use as clinical support tools. <b>Keywords:</b> Feature Detection, Supervised Learning, Convolutional Neural Network (CNN), Genetic Algorithms, CT, Spine, Technology Assessment, Head/Neck <i>Supplemental material is available for this article.</i> © RSNA, 2024 See also commentary by Levi and Politi in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e230550"},"PeriodicalIF":8.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142297036","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}
引用次数: 0
AI as a Second Reader Can Reduce Radiologists' Workload and Increase Accuracy in Screening Mammography. 人工智能作为第二阅读器可减轻放射医师的工作量并提高乳腺 X 射线摄影筛查的准确性。
IF 8.1
Radiology-Artificial Intelligence Pub Date : 2024-11-01 DOI: 10.1148/ryai.240624
Abhinav Suri
{"title":"AI as a Second Reader Can Reduce Radiologists' Workload and Increase Accuracy in Screening Mammography.","authors":"Abhinav Suri","doi":"10.1148/ryai.240624","DOIUrl":"10.1148/ryai.240624","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"6 6","pages":"e240624"},"PeriodicalIF":8.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605140/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509378","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}
引用次数: 0
Transformers in the Womb: Swin-UNETR Takes on Fetal Brain Imaging. 子宫里的变形金刚Swin-UNETR 对胎儿大脑成像的研究。
IF 8.1
Radiology-Artificial Intelligence Pub Date : 2024-11-01 DOI: 10.1148/ryai.240677
Sanjay P Prabhu
{"title":"Transformers in the Womb: Swin-UNETR Takes on Fetal Brain Imaging.","authors":"Sanjay P Prabhu","doi":"10.1148/ryai.240677","DOIUrl":"10.1148/ryai.240677","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"6 6","pages":"e240677"},"PeriodicalIF":8.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605138/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142628786","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}
引用次数: 0
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