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High accurate and explainable multi-pill detection framework with graph neural network-assisted multimodal data
Anh Duy Nguyen1,2, Huy Hieu Pham3,2, Huynh Thanh Trung4
1School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam.
Plos One
|September 28, 2023
Summary
Accurate pill identification is critical to prevent misuse and save lives. This study introduces a novel AI framework for multi-pill detection in real-world conditions, significantly improving accuracy over existing methods.
Area of Science:
- Computer Science
- Artificial Intelligence
- Medical Informatics
Background:
- Pill misuse is a global health crisis, causing one-third of worldwide deaths.
- Current pill identification methods often fail with visually similar pills or in real-world settings.
- Existing datasets lack diversity, featuring only single pills in controlled environments.
Purpose of the Study:
- To address the challenge of multi-pill detection and identification in unconstrained, real-world scenarios.
- To develop a robust AI framework capable of distinguishing hard-to-identify pills.
- To introduce a novel dataset of multi-pill images captured under realistic conditions.
Main Methods:
- Proposed a novel method for constructing heterogeneous a priori graphs, integrating co-occurrence, relative size, and visual semantic correlations.
- Developed a framework to combine a priori information with visual features for enhanced pill detection.
- Created and utilized a new multi-pill image dataset captured in unconstrained environments.
Main Results:
- The proposed framework demonstrated superior robustness, reliability, and explainability.
- Achieved significant improvements in COCO mAP: 9.4% over Faster R-CNN and 12.0% over YOLOv5.
- Outperformed all existing detection benchmarks across all evaluation metrics.
Conclusions:
- The AI-based pill identification solution offers a promising approach to reduce medication errors.
- The developed framework effectively tackles multi-pill detection in real-world settings.
- This research opens new avenues for patient safety through advanced AI.
