Detection of Schistosoma Eggs Using an AI-Based Deep Learning Model on Urinary Bladder Histopathology Images
Abstract
OBJECTIVE
To assess the accuracy and clinical applicability of YOLO-based segmentation models for detecting Schistosoma eggs in urinary bladder histopathology slide images, focusing on both bounding box and mask segmentation tasks.
STUDY DESIGN
A descriptive, cross-sectional study. Place and Duration of the Study: Artificial Intelligence Technology Centre, National Centre for Physics, Islamabad, Pakistan, from September to November 2024.
METHODOLOGY
A high-quality dataset was compiled using histopathological slides obtained from real patient samples available on the open-source platform PathPresenter. All images were meticulously annotated by expert histopathologists. The dataset included 681 images containing 2,751 schistosomes, divided into 476 training images (1,932 schistosomes), 136 validation images (539 schistosomes), and 69 testing images (280 schistosomes). Data pre-processing techniques were applied to optimise the quality of training and evaluation datasets. Multiple YOLO-based segmentation models, such as YOLOv5, YOLOv8, YOLOv9, and YOLOv11 variants (n/s/m/l/x/c/e), were trained and evaluated for both bounding box and mask detection. Model performance was evaluated using precision, recall, F1 score, mean Average Precision at 50% Intersection over Union (IoU; mAP50), and mAP across 50 to 95% IoU (mAP50-95) for both bounding box and mask segmentation tasks.
RESULTS
Among the models, YOLOv8l demonstrated the highest diagnostic accuracy, achieving an F1 score of…
How strong is the evidence?
Evidence Hub reads this as a cross-sectional. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause.
Read the paper
Related studies
- Quantum dots: synthesis, bioapplications, and toxicity
- A Guide to In Silico Drug Design
- Quantum Computing for Healthcare: A Review
- Automated in Silico Design of Homogeneous Catalysts
- Identification of Novel 3-Hydroxy-pyran-4-One Derivatives as Potent HIV-1 Integrase Inhibitors Using in silico Structure-Based Combinatorial Library Design Approach
- In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.