Background Interstitial lung disease (ILD) encompasses diverse pulmonary disorders with varied prognoses. Current pathological diagnoses suffer from inter-observer variability,necessitating more standardized approaches. We developed an ensemble model AI for cryobiopsy, CRAI, an artificial intelligence model to analyze transbronchial lung cryobiopsy (TBLC) specimens and predict patient outcomes.

Methods We developed an explainable AI model, CRAI, to analyze TBLC. CRAI comprises seven modules for detecting histological features, generating 19 pathologically significant findings. A downstream XGBoost classifier was developed to predict disease progression using these findings. The model’s performance was evaluated using respiratory function changes and survival analysis in cross-validation and external test cohorts.

Findings In the internal cross-validation (135 cases), the model predicted 105 cases without disease progression and 30 with disease progression. The annual Δ%FVC was −1.293 in the non-progressive group versus −5.198 in the progressive group, outperforming most pathologists’ diagnoses. In the external test cohort (48 cases), the model predicted 38 non-progressive and 10 progressive cases. Survival analysis demonstrated significantly shorter survival times in the progressive group (p=0.034).

Interpretation CRAI provides a comprehensive, interpretable approach to analyzing TBLC specimens, offering potential for standardizing ILD diagnosis and predicting disease progression. The model could facilitate early identification of progressive cases and guide personalized therapeutic interventions.

Funding New Energy and Industrial Technology Development Organization (NEDO) and Japanese Ministry of Health, Labor, and Welfare.

The authors have declared no competing interest.

This paper is based on results obtained from a project, JPNP20006, commissioned by the New Energy and Industrial Technology Development Organization (NEDO) and partly supported by the research group of diffuse lung disease under the Japanese Ministry of Health, Labor, and Welfare (20FC1033).

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Ethics Committee of Nagasaki University Hospital gave ethical approval for this work

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↵† CRYOSOLUTION Wendy Cooper (Tissue Pathology and Diagnostic Oncology, NSW Health Pathology, Royal Prince Alfred Hospital), Luka Brcic (Department of Pathology, Hospital Graz II, Graz, Department of Pathology, Medical University of Vienna), Yoshiaki Zaizen (Nagasaki University), Joanne Lynne Wright (Department of Pathology, St Paul’s Hospital, University of British Columbia), Angela Maria Takano Pena (Singapore General Hospital and Duke-NUS Medical School), Sosuke Ishijima (Nagasaki University Graduate School of Biomedical Sciences), Amna R. Almutrafi (Toronto General Hospital, Research Institute), Mary Beth Beasley (Mount Sinai Medical Center), Alberto Cavazza (Azienda USL Reggio Emilia), Izidor Kern (University Clinic Golnik), Wei-Chin Chang (Taipei Medical University Hospital), Lee Fong Wan (Sarawak General Hospital), Tereza Losmanova (Institute of Pathology, University of Bern), Noriah binti Othman (Hospital Serdang), Alexandre Todorovic Fabro (Ribeirão Preto Medical School – University of São Paulo), Masayo Yoshimura (Fukuoka University School of Medicine and Hospital), Akira Hebisawa (Asahi General Hospital), Koji Okudera (Saitama Medical University), Rosane Duarte Achcar (National Jewish Health), Andrew Churg (The University of British Columbia, Vancouver General Hospital), and Richard Luther Attanoos (Cardiff University)

Upon approval from the Institutional Review Board at Nagasaki University Hospital, a subset of individual participant data can be shared for research purposes upon reasonable request to the corresponding author.