ArticleRespiratory investigation2026
Performance validation of a closed loop fully automated AI model for lung nodule stratification in screening cases.
Article in Respiratory investigation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
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Authors and funding
5 authors.
Funding
Abstract
backgroundSeveral limitations hinder the effectiveness of human-based lung cancer screening (LCS): high false-positive rates leading to unnecessary follow-up imaging, procedures, and surgeries; inter-reader variability; inconsistent Lung-RADS adherence; and fatigue-related diagnostic errors. Additionally, most artificial intelligence (AI) models address only one task (nodule detection or risk stratification) and require manual image processing, which is time-consuming and costly. We developed Bronchosolve, a closed-loop, fully-automated software that processes scans without manual input, aiming to improve consistency, accuracy, and throughput in LCS.
methodsThe software integrates pre-processing, analysis, and result generation, using a deep-learning convolutional neural network (CNN) for pulmonary nodule triaging. Inputs were full chest CT scans in DICOM format, without clinical or demographic data. Automated steps included: 1) optimal CT series selection, 2) normalization and preprocessing, 3) AI-based detection and classification of suspicious nodules, and 4) report generation. The model was trained on a multi-center high-prevalence set of 2358 cases (malignant and benign nodules). Validation used a U.S.-based, multi-site cohort (n = 184; 8 sites). Positive cases were biopsy-confirmed within 1 year; negatives had biopsy or ≥2-year follow-up.
resultsAll cases completed automatically (100 % success). Median age was 62.5 years (IQR 58.5-66.5); 45 % former smokers, 55 % current smokers, and 40 % female. The model achieved an AUC of 0.898 [0.851-0.940], outperforming Lung-RADS (pAUC 0.669) and the Brock model (AUC 0.783). Sensitivity was 83.6 %; specificity was 86.3 %. Performance remained consistent across scanner types and slice thicknesses.
conclusionsBronchosolve enables accurate, fully-automated risk classification of lung nodules and may enhance non-invasive diagnostic workflows.
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