An exploratory deep learning approach to investigate tuberculosis pathogenesis in nonhuman primate model: Combining automated radiological analysis with clinical and biomarkers data.
J Med Primatol
; 53(4): e12722, 2024 Aug.
Article
en En
| MEDLINE
| ID: mdl-38949157
ABSTRACT
BACKGROUND:
Tuberculosis (TB) kills approximately 1.6 million people yearly despite the fact anti-TB drugs are generally curative. Therefore, TB-case detection and monitoring of therapy, need a comprehensive approach. Automated radiological analysis, combined with clinical, microbiological, and immunological data, by machine learning (ML), can help achieve it.METHODS:
Six rhesus macaques were experimentally inoculated with pathogenic Mycobacterium tuberculosis in the lung. Data, including Computed Tomography (CT), were collected at 0, 2, 4, 8, 12, 16, and 20 weeks.RESULTS:
Our ML-based CT analysis (TB-Net) efficiently and accurately analyzed disease progression, performing better than standard deep learning model (LLM OpenAI's CLIP Vi4). TB-Net based results were more consistent than, and confirmed independently by, blinded manual disease scoring by two radiologists and exhibited strong correlations with blood biomarkers, TB-lesion volumes, and disease-signs during disease pathogenesis.CONCLUSION:
The proposed approach is valuable in early disease detection, monitoring efficacy of therapy, and clinical decision making.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Biomarcadores
/
Tomografía Computarizada por Rayos X
/
Aprendizaje Profundo
/
Macaca mulatta
/
Mycobacterium tuberculosis
Límite:
Animals
Idioma:
En
Revista:
J Med Primatol
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos