Retrospective validation of MetaSystems' deep-learning-based digital microscopy platform with assistance compared to manual fluorescence microscopy for detection of mycobacteria.
Claudine DesruisseauxConor BroderickValéry LavergneKim SyDuang-Jai GarciaGaurav BarotKerstin LocherCharlene PorterMélissa CazaMarthe K CharlesPublished in: Journal of clinical microbiology (2024)
This manuscript presents a full validation of MetaSystems' automated acid-fast bacilli (AFB) smear microscopy scanning and deep-learning-based image analysis module using a probability threshold of 96% including accuracy, precision studies, and evaluation of limit of AFB detection on respiratory samples when the technology is used with assistance. This study is complementary to the conversation started by Tomasello et al. on the use of image analysis artificial intelligence software in routine mycobacterial diagnostic activities within the context of high-throughput laboratories with low incidence of tuberculosis.
Keyphrases
- deep learning
- high throughput
- artificial intelligence
- label free
- single molecule
- high resolution
- machine learning
- big data
- convolutional neural network
- mycobacterium tuberculosis
- single cell
- pulmonary tuberculosis
- loop mediated isothermal amplification
- high speed
- real time pcr
- optical coherence tomography
- risk factors
- cross sectional
- gram negative
- hiv aids
- emergency department
- mass spectrometry
- case control
- multidrug resistant
- electron microscopy
- adverse drug
- sensitive detection
- drug induced
- hiv infected
- quantum dots