Lateral flow and colorimetric assay for ketamine detection reinforced with deep learning model interfaced with mobile app for smart alert.

Point-of-care (POC) devices have grown in popularity due to their ease of use, low cost, and speedy on-site diagnostic capabilities. This study focuses on ketamine detection by colorimetric and lateral flow assays (LFA), with aptamer-based LFA emerging as a potential alternative to antibody-based approaches due to its stability, repeatability, and simplicity of modification. Two methods were investigated: (1) This approach used gold nanoparticles and an in-solution adsorption technique to create colorimetric aptasensors integrated with a UV-Vis spectrophotometer for the detection of the drug ketamine, and (2) innovative LFA tests with a detection limit of 0.1 µg/mL in synthetic urine samples. A dual-stage deep learning framework (YOLOv5 and ResNet50) was also built to categorize. This method proposes a dual-stage deep learning system for the effective classification of lateral flow assay (LFA) strip data. The technology proved accuracy, speed, and dependability, providing a portable, cost-effective alternative for point-of-care diagnostics.