Machine Learning Takes Laboratory Automation to the Next Level
Good article on ML applications for microbiology lab.
There are two commercially available Food and Drug Administration (FDA)-approved microbiology laboratory automation platforms in the United States, namely, WASPLab (Copan Diagnostics Inc.) and Kiestra (Becton Dickinson) (6). Each system is highly customizable and consists of front-end processing, “smart” incubation according to laboratory protocol, and plate imaging. The processing unit performs medium selection, application of patient information and barcodes for tracking, medium inoculation, and plate streaking. Automation of these processes cuts down on and improves the consistency of repetitive tasks previously performed by technologists.
Image analysis software is not currently FDA approved, so the algorithm it deploys qualifies as a high-complexity laboratory-developed test when used to make definitive calls about microorganism presence/absence or culture significance. In this context, the end user need not understand the internal workings any more than they understand the inner workings of most computers. Additionally, as with most laboratory software, manufacturer assistance is provided in training the algorithm. Labs may, therefore, validate performance according to familiar sensitivity and specificity (for significant growth), precision and accuracy (for quantification), and procedural variation (coefficients of variation, Kappa statistics). As with any test, revalidation must be performed if components of the test change. The number of samples needed to train the algorithm (hundreds to thousands) will be algorithm dependent but easily available due to their common nature, facilitating both initial and revalidation using new plate images. Validation of machine learning image analysis for laboratory automation may, overall, be comparable to that performed for whole-slide imaging as used in histopathology, where the object of validation is a process as much as a machine (12) and where modest interobserver agreement may set a similarly modest benchmark for machine learning performance.
Eivissa autèntica, Joaquim Gomis
