ArticleRespirology (Carlton, Vic.)2025
Enhancing Interstitial Lung Disease Diagnoses Through Multimodal AI Integration of Histopathological and CT Image Data.
Article in Respirology (Carlton, Vic.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Toward precision imaging in interstitial lung disease: advances in quantitative imaging and artificial intelligence.Current opinion in pulmonary medicine · 2026Review
- Transbronchial Lung Cryobiopsy and Awake Video-Assisted Thoracic Surgery in Interstitial Lung Disease: Complementary Roles in a Stepwise Diagnostic Approach.Diagnostics (Basel, Switzerland) · 2026Review
- Graph attention network-based multimodal approach for lung diseases classification.Scientific reports · 2026Article
- Diagnosis of Lung Diseases in the Artificial Intelligence Era.International journal of molecular sciences · 2026Article
- Artificial intelligence in idiopathic pulmonary fibrosis: advances, challenges and future directions.The European respiratory journal · 2026Review
- Enhancing Interstitial Lung Disease Diagnoses Through Multimodal AI Integration of Histopathological and CT Image Data.Respirology (Carlton, Vic.) · 2025Article
- Clinical Insights and Therapeutic Strategies for the Treatment of Interstitial Lung Disease in Patients with Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis: Current Trends and Future Directions.Journal of clinical medicine · 2025Review
Corrections and comments
- Commented on by
Authors and funding
26 authors.
Funding
Abstract
BACKGROUND AND
objectiveThe diagnosis of interstitial lung diseases (ILDs) often relies on the integration of various clinical, radiological, and histopathological data. Achieving high diagnostic accuracy in ILDs, particularly for distinguishing usual interstitial pneumonia (UIP), is challenging and requires a multidisciplinary approach. Therefore, this study aimed to develop a multimodal artificial intelligence (AI) algorithm that combines computed tomography (CT) and histopathological images to improve the accuracy and consistency of UIP diagnosis.
methodsA dataset of CT and pathological images from 324 patients with ILD between 2009 and 2021 was collected. The CT component of the model was trained to identify 28 different radiological features. The pathological counterpart was developed in our previous study. A total of 114 samples were selected and used for testing the multimodal AI model. The performance of the multimodal AI was assessed through comparisons with expert pathologists and general pathologists.
resultsThe developed multimodal AI demonstrated a substantial improvement in distinguishing UIP from non-UIP, achieving an AUC of 0.92. When applied by general pathologists, the diagnostic agreement rate improved significantly, with a post-model κ score of 0.737 compared to 0.273 pre-model integration. Additionally, the diagnostic consensus rate with expert pulmonary pathologists increased from κ scores of 0.278-0.53 to 0.474-0.602 post-model integration. The model also increased diagnostic confidence among general pathologists.
conclusionCombining CT and histopathological images, the multimodal AI algorithm enhances pathologists' diagnostic accuracy, consistency, and confidence in identifying UIP, even in cases where specialised expertise is limited.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.