Deep-Learning-Aided Detection of Mycobacteria in Pathology Specimens Increases the Sensitivity in Early Diagnosis of Pulmonary Tuberculosis Compared with Bacteriology Tests.
artificial intelligence
bronchial lavage
bronchoscopy
mycobacteria
tuberculosis
Journal
Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402
Informations de publication
Date de publication:
14 Mar 2022
14 Mar 2022
Historique:
received:
28
02
2022
revised:
10
03
2022
accepted:
12
03
2022
entrez:
25
3
2022
pubmed:
26
3
2022
medline:
26
3
2022
Statut:
epublish
Résumé
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%,
Identifiants
pubmed: 35328262
pii: diagnostics12030709
doi: 10.3390/diagnostics12030709
pmc: PMC8946921
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Fukuoka Financial Group
ID : a research grant from KYUTEC
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