Deep-Learning-Aided Detection of Mycobacteria in Pathology Specimens Increases the Sensitivity in Early Diagnosis of Pulmonary Tuberculosis Compared with Bacteriology Tests.
Yoshiaki ZaizenYuki KanahoriSousuke IshijimaYuka KitamuraHan-Seung YoonMutsumi OzasaHiroshi MukaeAndrey BychkovTomoaki HoshinoJunya FukuokaPublished in: Diagnostics (Basel, Switzerland) (2022)
The histopathological diagnosis of mycobacterial infection may be improved by a comprehensive analysis using artificial intelligence. Two autopsy cases of pulmonary tuberculosis, and forty biopsy cases of undetected acid-fast bacilli (AFB) were used to train AI (convolutional neural network), and construct an AI to support AFB detection. Forty-two patients underwent bronchoscopy, and were evaluated using AI-supported pathology to detect AFB. The AI-supported pathology diagnosis was compared with bacteriology diagnosis from bronchial lavage fluid and the final definitive diagnosis of mycobacteriosis. Among the 16 patients with mycobacteriosis, bacteriology was positive in 9 patients (56%). Two patients (13%) were positive for AFB without AI assistance, whereas AI-supported pathology identified eleven positive patients (69%). When limited to tuberculosis, AI-supported pathology had significantly higher sensitivity compared with bacteriology (86% vs. 29%, p = 0.046). Seven patients diagnosed with mycobacteriosis had no consolidation or cavitary shadows in computed tomography; the sensitivity of bacteriology and AI-supported pathology was 29% and 86%, respectively ( p = 0.046). The specificity of AI-supported pathology was 100% in this study. AI-supported pathology may be more sensitive than bacteriological tests for detecting AFB in samples collected via bronchoscopy.
Keyphrases
- artificial intelligence
- end stage renal disease
- deep learning
- pulmonary tuberculosis
- ejection fraction
- newly diagnosed
- chronic kidney disease
- computed tomography
- big data
- mycobacterium tuberculosis
- peritoneal dialysis
- machine learning
- emergency department
- convolutional neural network
- magnetic resonance
- mass spectrometry
- squamous cell carcinoma
- hiv aids
- multidrug resistant
- gram negative
- ultrasound guided
- radiation therapy
- sensitive detection