The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review

Area of Science:

• Biotechnology
• Artificial Intelligence
• Pharmacology

Background:

• Organs-on-chips (OoCs) are advanced in vitro models using microfluidic systems.
• Deep learning (DL) is a machine learning technique for extracting complex data relationships.
• Integrating OoCs and DL offers synergistic potential for drug discovery.

Purpose of the Study:

• To review the fundamental concepts of deep learning applied to organs-on-chips.
• To showcase successful use cases of DL in various OoC applications.
• To discuss future challenges and opportunities for combining OoCs and DL.

Main Methods:

• Literature review of deep learning techniques in organs-on-chips research.
• Analysis of successful applications and case studies.
• Discussion of future trends and challenges.

Main Results:

• Deep learning effectively extracts patterns from OoC data, enhancing model capabilities.
• Successful applications include disease modeling, drug efficacy, and toxicity testing.
• OoCs integrated with DL show promise for creating complex human-on-chip systems.

Conclusions:

• The combination of OoCs and deep learning significantly advances in vitro drug screening.
• Future development focuses on image processing, automation, and complex physiological models.
• Addressing challenges is key to realizing the full potential of this interdisciplinary approach.