A Cost-Effective Framework for Transforming Conventional Microscopes into Intelligent Diagnostic Platforms using Systematic Multi-Field Acquisition Protocol and Multimodal LLMs

Abstract

Urine and stool cultures under the microscope are a main method of diagnostic work, but manual analysis is not only dependent on experience of the examiner but also suffers visual fatigue and subjectivity. Although, automated systems are available, their cost is so high that they are not often integrated in resource-limited environments. In this paper,we proposes a MicroScan AI, a low-cost solution that will be used to convert a standard optical microscope into a smart platform capable of diagnostic tasks. Morphological reasoning is done by connecting a digital microscopic camera with a 0.5x reduction lens with multimodal Large Language Models (LLM), namely ChatGPT-4o, Gemini, and Claude, and conducting the reasoning through encrypted API channels. A counting-based aggregation methodology was used to counteract a 60% field-of-view (FOV) hardware-induced field limit by a structured five-field acquisition scheme (F1–F5) scheme and Systematic Multi-Field for image pre-processing protocol . The validation of the experiment was done using 20 clinical specimens and 200 diagnostic parameters against expert human opinion. The system had an agreement rate of 97% with a Cohen Kappa coefficient of 0.94 which signifies an almost perfect agreement. Examination of the 3-percent discrepancy indicated that the error occurred in adjoining semi-quantitative grading frequencies and not resultant categorical detection errors. These results make MicroScan AI a scalable clinical decision support system that improves diagnostic consistency and decreases operator dependency without necessitating the complete replacement of hardware.

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