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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 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":"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。","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":null,"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":8.1000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"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\":null,\"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\":8.1000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radiology-Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1148/ryai.230296\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiology-Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1148/ryai.230296","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

"刚刚接受 "的论文经过同行评审,已被接受在《放射学》上发表:人工智能》上发表。这篇文章在以最终版本发表之前,还将经过校对、排版和校对审核。请注意,在制作最终校对稿的过程中,可能会发现影响文章内容的错误。目的 建立一个高度通用的弱监督模型,利用研究级标签自动检测和定位图像级颅内出血(ICH)。材料与方法 在这项回顾性研究中,利用基于注意力的双向长短期记忆网络,在图像级 RSNA 数据集上对所提出的模型进行了预训练,并在本地数据集上对其进行了微调。该本地训练数据集包括来自 7469 名患者的 10,699 张非对比头部 CT 扫描图像,这些图像带有从放射学报告中提取的 ICH 研究级标签。使用 McNemar 检验将模型的性能与两位资深神经放射学专家在 100 个随机测试扫描中的性能进行了比较,并在外部独立数据集上评估了模型的普适性。结果 在本地测试集(n = 7243,3721 名女性)上,该模型的阳性预测值(PPV)为 85.7%(95% CI:[84.0%, 87.4%]),AUC 为 0.96(95% CI:[0.96, 0.97]);在外部测试集(n = 491,178 名女性)上,该模型的阳性预测值(PPV)为 89.3%(95% CI:[87.8%, 90.7%]),AUC 为 0.96(95% CI:[0.96, 0.97])。在随机抽取的 100 个样本中,该模型的表现与两名神经放射科医生相当,但诊断时间明显更快(P < .05),每次扫描仅需 5.04 秒(而两名神经放射科医生的诊断时间分别为 86 秒和 22.2 秒)。该模型的注意力权重和热图与神经放射科医生的解释一致。结论 所提出的模型具有很高的普适性和 PPV 值,为加快 ICH 检测和优先排序提供了有价值的工具,同时减少了放射医师工作流程中假阳性的中断。©RSNA,2024。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Precise Image-level Localization of Intracranial Hemorrhage on Head CT Scans with Deep Learning Models Trained on Study-level Labels.

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 (n = 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 (n = 491, 178 female). For 100 randomly selected samples, the model achieved performance on par with two neuroradiologists, but with a significantly faster (P < .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. Keywords: Computer-Aided Diagnosis (CAD), Brain/Brain Stem, Hemorrhage, Convolutional Neural Network (CNN), Transfer Learning Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Akinci D'Antonoli and Rudie in this issue.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
16.20
自引率
1.00%
发文量
0
期刊介绍: Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.
期刊最新文献
Deep Learning Applied to Diffusion-weighted Imaging for Differentiating Malignant from Benign Breast Tumors without Lesion Segmentation. Integrated Deep Learning Model for the Detection, Segmentation, and Morphologic Analysis of Intracranial Aneurysms Using CT Angiography. RSNA 2023 Abdominal Trauma AI Challenge Review and Outcomes Analysis. SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans. Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1