Smartphone-based biosensing: a review of optical imaging, microfluidic integration, and AI-enhanced analysis

Area of Science:

• Biomedical Engineering
• Point-of-Care Diagnostics
• Mobile Health Technology

Background:

• Smartphone-based biosensing platforms offer portable, low-cost alternatives to traditional lab diagnostics.
• Advances in mobile imaging, sensors, microfluidics, and wireless connectivity enable real-time detection of various biological targets.
• These technologies facilitate point-of-care testing for a wide range of applications.

Purpose of the Study:

• To review recent (since 2020) smartphone-integrated biosensing technologies.
• To examine various sensing modalities and enabling components.
• To highlight the role of artificial intelligence (AI) and deep learning in enhancing diagnostic accuracy.

Main Methods:

• Review of scientific literature published since 2020 focusing on smartphone biosensing.
• Analysis of different sensing modalities (optical, electrochemical, hybrid) and hardware components (CMOS imagers, microfluidics).
• Evaluation of AI/deep learning integration for image enhancement and automated quantification.

Main Results:

• Smartphone biosensing encompasses optical (brightfield, fluorescence, dark-field), electrochemical, and hybrid systems.
• AI and deep learning significantly improve diagnostic accuracy through image processing and automated analysis.
• Key biomedical applications include detecting ocular, metabolic, urological diseases, cancers, and infectious agents.

Conclusions:

• Despite progress, challenges in hardware standardization and clinical validation remain.
• Future developments require modular attachments, open-source hardware, and cloud analytics for scalability.
• Smartphone biosensing holds significant potential for decentralized healthcare, environmental monitoring, and global health initiatives.