ArticleRadiology. Artificial intelligence2022
Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning.
Article in Radiology. Artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification.IEEE access : practical innovations, open solutions · 2026Article
- Utility of the virtual imaging trials methodology for objective characterization of AI systems and training data.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- A Pan-Organ Vision-Language Model for Generalizable 3D CT Representations.medRxiv : the preprint server for health sciences · 2025Article
- A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification.NPJ digital medicine · 2025Article
- Annotation-free multi-organ anomaly detection in abdominal CT using free-text radiology reports: a multi-centre retrospective study.EBioMedicine · 2024Article
- The unintended consequences of artificial intelligence in paediatric radiology.Pediatric radiology · 2024Article
- Examination-Level Supervision for Deep Learning-based Intracranial Hemorrhage Detection on Head CT Scans.Radiology. Artificial intelligence · 2024Article
- Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning.BMC medical informatics and decision making · 2022Article
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8 authors.
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Abstract
purposeTo design multidisease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports. MATERIALS AND
methodsThis retrospective study included a total of 12 092 patients (mean age, 57 years ± 18 [standard deviation]; 6172 women) for model development and testing. Rule-based algorithms were used to extract 19 225 disease labels from 13 667 body CT scans performed between 2012 and 2017. Using a three-dimensional DenseVNet, three organ systems were segmented: lungs and pleura, liver and gallbladder, and kidneys and ureters. For each organ system, a three-dimensional convolutional neural network classified each as no apparent disease or for the presence of four common diseases, for a total of 15 different labels across all three models. Testing was performed on a subset of 2158 CT volumes relative to 2875 manually derived reference labels from 2133 patients (mean age, 58 years ± 18; 1079 women). Performance was reported as area under the receiver operating characteristic curve (AUC), with 95% CIs calculated using the DeLong method.
resultsManual validation of the extracted labels confirmed 91%-99% accuracy across the 15 different labels. AUCs for lungs and pleura labels were as follows: atelectasis, 0.77 (95% CI: 0.74, 0.81); nodule, 0.65 (95% CI: 0.61, 0.69); emphysema, 0.89 (95% CI: 0.86, 0.92); effusion, 0.97 (95% CI: 0.96, 0.98); and no apparent disease, 0.89 (95% CI: 0.87, 0.91). AUCs for liver and gallbladder were as follows: hepatobiliary calcification, 0.62 (95% CI: 0.56, 0.67); lesion, 0.73 (95% CI: 0.69, 0.77); dilation, 0.87 (95% CI: 0.84, 0.90); fatty, 0.89 (95% CI: 0.86, 0.92); and no apparent disease, 0.82 (95% CI: 0.78, 0.85). AUCs for kidneys and ureters were as follows: stone, 0.83 (95% CI: 0.79, 0.87); atrophy, 0.92 (95% CI: 0.89, 0.94); lesion, 0.68 (95% CI: 0.64, 0.72); cyst, 0.70 (95% CI: 0.66, 0.73); and no apparent disease, 0.79 (95% CI: 0.75, 0.83).
conclusionWeakly supervised deep learning models were able to classify diverse diseases in multiple organ systems from CT scans.
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