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Canvas Insight: Enhancing AI-Driven Scheduling for Depression Diagnosis Using the House-Tree-Person Projective Test
Conference proceeding

Canvas Insight: Enhancing AI-Driven Scheduling for Depression Diagnosis Using the House-Tree-Person Projective Test

Pakeeza Akram, Ammara Ghalib, Syeda Fizzah Hashmi and IEEE COMPUTER SOC
2025 IEEE Conference on Artificial Intelligence (CAI), pp.594-597
05/05/2025
DOI: https://doi.org/10.1109/CAI64502.2025.00110

Abstract

Accuracy Data preprocessing Depression Depression Screening Feature detection House-Tree-Person (H-T-P) test Labeling Machine Learning Patient Scheduling Psychology Resource management Supervised learning Transfer learning YOLO You Only Look Once (YOLO)
The rising prevalence of depression and psychiatrist shortages highlight the need for efficient diagnostic tools. Canvas Insight leverages AI with the House-Tree-Person (H-T-P) test to automate depression screening. This study evaluates YOLO-V3, YOLO-V4, and YOLO-V5 on an annotated dataset, with YOLOV3 achieving the highest accuracy (mAP 89% for houses, 88% for persons and 81 % for tree). Using a structured scoring system, the model assesses depression severity to prioritize scheduling, ensuring timely care for those most in need. The intention is not to provide a diagnosis but to assist clinicians in optimizing resource allocation. This approach enhances efficiency, particularly in resource-limited areas, while maintaining AI as a supportive tool for human expertise.

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