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

Background: Machine 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. Methods: This 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. Results: Usage 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. Conclusion: The 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.