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Updated: Aug 9, 2026
Quantitative 3D Imaging of Trypanosoma cruzi-Infected Cells, Dormant Amastigotes, and T Cells in Intact Clarified Organs
Published on: June 23, 2022
Convolutional Neural Networks for Chagas' Parasite Detection in Histopathological Images
Insights
This study introduces a deep learning method for detecting Trypanosoma cruzi (T. cruzi) amastigotes in heart tissue images. The AI tool aids in diagnosing Chagas disease more efficiently and accurately.
Area of Science:
- Biomedical Engineering
- Parasitology
- Computational Pathology
Background:
- Chagas disease, caused by Trypanosoma cruzi (T. cruzi), often progresses unnoticed until significant myocardial damage occurs.
- Histopathological analysis of endomyocardium biopsies for T. cruzi amastigotes is time-consuming and subjective, leading to potential diagnostic errors.
Purpose of the Study:
- To develop and evaluate a deep learning-based method for automated detection of T. cruzi amastigotes in histopathological images.
- To improve the efficiency and objectivity of Chagas disease diagnosis using artificial intelligence.
Main Methods:
- Implementation and training of a U-Net convolutional neural network architecture from scratch.
- Utilizing histopathological images from endomyocardium biopsies in an experimental murine model of Chagas disease.
Main Results:
- The deep learning model achieved a high accuracy of 99.19%.
- A Jaccard index of 49.43% was obtained, indicating the model's effectiveness in segmenting amastigote nests.
- The results demonstrate the potential of the approach for reliable amastigote detection.
Conclusions:
- The proposed deep learning method shows promise as an automated tool for detecting T. cruzi amastigotes in histopathological images.
- This approach can significantly aid in the analysis and diagnosis of Chagas disease, potentially leading to earlier intervention.
Abstract:
Chagas disease is a widely spreaded illness caused by the parasite Trypanosoma cruzi (T. cruzi). Most cases go unnoticed until the accumulated myocardial damage affect the patient. The endomyocardium biopsy is a tool to evaluate sustained myocardial damage, but analyzing histopathological images takes a lot of time and its prone to human error, given its subjective nature. The following work presents a deep learning method to detect T. cruzi amastigotes on histopathological images taken from a endomyocardium biopsy during an experimental murine model. A U-Net convolutional neural network architecture was implemented and trained from the ground up. An accuracy of 99.19% and Jaccard index of 49.43% were achieved. The obtained results suggest that the proposed approach can be useful for amastigotes detection in histopathological images.Clinical relevance- The proposed method can be incorporated as automatic detection tool of amastigotes nests, it can be useful for the Chagas disease analysis and diagnosis.
