Development of a Machine learning image segmentation-based algorithm for the determination of the adequacy of Gram-stained sputum smear images

Abstract

BackgroundMachine learning (ML) prepares and trains a model through supervised or unsupervised learning methods. Sputum, a respiratory tract secretion, is a common laboratory specimen that aids in diagnosing respiratory diseases, including pulmonary tuberculosis (TB). Gram stain is an easy, cost-effective stain, which may be applied to sputum smears to screen out an unsatisfactory sample. ML model may help in screening sputum smears. MethodsThis collaborative project was carried out from June 2020-July 2021. In this study, a color-based segmentation ML algorithm using K-Means clustering was developed. A library of stained sputum smears was built. The Bartletts criteria (based on neutrophil and squamous cell count) for screening and selecting satisfactory sputum smears were used. A smartphone camera was used to take several photographs of satisfactory, as well as unsatisfactory, smears. The image segmentation algorithm was applied to medical image analysis, color-segmentation of sputum images was done. The hue saturation value (HSV) color ranges were defined on a prototype image. Then, all connected pixels were identified as a single object, and morphological operations were applied. ResultsUsage of AI-driven model on the slide-image revealed the slide adequacy as the cell count was acceptable based on Bartlett’s criteria. Both the manual cell counts (Range: 126–203 neutrophils, 14–47 squamous cells) and the model counts (Range: 117–242 neutrophils, 14–37 squamous cells) are within acceptable limits. ConclusionThe use of a model to screen a large number of sputum slides may be a boon in resource-limited settings where trained microscopists may not be easily available.