Research Progress of Automatic Diatom Test by Artificial Intelligence

Area of Science:

• Forensic Medicine
• Analytical Chemistry
• Biotechnology

Background:

• The diatom test is crucial for diagnosing drowning in forensic medicine.
• It helps distinguish antemortem from postmortem drowning and identify drowning locations.
• Current methods rely on morphological characteristics of diatoms found in tissues and organs.

Purpose of the Study:

• To review the progress of AI in automatic diatom recognition and classification for drowning diagnosis.
• To discuss the application of AI deep learning algorithms in diatom testing.
• To explore the potential of AI-assisted diatom testing in forensic drowning investigations.

Main Methods:

• Review of morphological diatom test methods.
• Analysis of research progress in AI-based automatic diatom recognition and classification.
• Discussion of AI deep learning algorithms applied to diatom identification.

Main Results:

• AI algorithms can automatically identify and classify diatoms based on morphological characteristics.
• AI deep learning facilitates objective, accurate, and efficient qualitative and quantitative analysis of diatoms.
• AI shows promise in enhancing the diagnostic capabilities of the diatom test.

Conclusions:

• AI-driven automatic diatom testing represents a significant innovation in forensic drowning diagnosis.
• AI deep learning algorithms can assist in achieving more reliable diatom test results.
• This technology is expected to become a future direction for research in forensic drowning investigations.