Evidence map›Paper›PMID 42799032›Full record

ArticleParasite epidemiology and control2026

Automated detection and classification of intestinal protozoan cysts using a nuclei structure-based deep learning framework for epidemiological surveillance.

Je-Chiang Tsai, Yi-Ru Chen, Shu-Min Tan, Kai-Lun Jin, Chih-Chi Wu, Jie-Hong Lai, Kuan-Lin Huang, Chia-Chieh Chu, Shih-Hsun Hung, Jiunn-Yuan Lin and 11 more

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Article in Parasite epidemiology and control, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Je-Chiang TsaiDepartment of Mathematics, National Tsing Hua University, Hsinchu, Taiwan.
Yi-Ru ChenDepartment of Mathematics, National Tsing Hua University, Hsinchu, Taiwan.
Shu-Min TanDepartment of Mathematics, National Tsing Hua University, Hsinchu, Taiwan.
Kai-Lun JinDepartment of Mathematics, National Tsing Hua University, Hsinchu, Taiwan.
Chih-Chi WuDepartment of Applied Mathematics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Jie-Hong LaiDepartment of Mathematics, National Tsing Hua University, Hsinchu, Taiwan.
Kuan-Lin HuangDepartment of Mathematics, National Tsing Hua University, Hsinchu, Taiwan.
Chia-Chieh ChuDepartment of Mathematics, National Tsing Hua University, Hsinchu, Taiwan.
Shih-Hsun HungDepartment of Mathematics, National Tsing Hua University, Hsinchu, Taiwan.
Jiunn-Yuan LinDepartment of Physics, National Chung Cheng University, Chiayi, Taiwan.
Sukhdeep Singh BalWellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
Fan-Pei YangDepartment of Foreign Languages and Literature, National Tsing Hua University, Hsinchu, Taiwan.
Kang-Shuo LeeDepartment of Foreign Languages and Literature, National Tsing Hua University, Hsinchu, Taiwan.
Tsai-Wei ChenDepartment of Foreign Languages and Literature, National Tsing Hua University, Hsinchu, Taiwan.
Chih-Hsing HungKaohsiung Municipal Siaogang Hospital, No. 482, Shanming Rd., Siaogang Dist., Kaohsiung City 812, Taiwan.
Ching-Chao LiangKaohsiung Municipal Siaogang Hospital, No. 482, Shanming Rd., Siaogang Dist., Kaohsiung City 812, Taiwan.
Huei-Jiun TsaiKaohsiung Municipal Siaogang Hospital, No. 482, Shanming Rd., Siaogang Dist., Kaohsiung City 812, Taiwan.
I-Pin ChenKaohsiung Municipal Siaogang Hospital, No. 482, Shanming Rd., Siaogang Dist., Kaohsiung City 812, Taiwan.
Chiu-Hui LiKaohsiung Municipal Siaogang Hospital, No. 482, Shanming Rd., Siaogang Dist., Kaohsiung City 812, Taiwan.
Ya-Nien HsiehKaohsiung Municipal Siaogang Hospital, No. 482, Shanming Rd., Siaogang Dist., Kaohsiung City 812, Taiwan.
Chin-Ling TsaiKaohsiung Municipal Siaogang Hospital, No. 482, Shanming Rd., Siaogang Dist., Kaohsiung City 812, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intestinal parasitic infections (IPIs) remain a critical global public health concern, particularly for epidemiological surveillance in resource-limited tropical regions. Standard laboratory diagnostics heavily rely on the manual microscopic examination of stool samples, a process that is notoriously time-consuming, labor-intensive, and highly dependent on the scarce availability of experienced parasitologists. To alleviate medical workloads and enhance community-wide screening efficiency, reliable, automated diagnostic tools are urgently needed. From an epidemiological control perspective, such tools should not only improve image-level classification but also support scalable screening, case triage, and prioritization of specimens requiring confirmatory testing in routine surveillance programs. However, most existing frameworks operate as end-to-end "black boxes" that rely solely on pixel-level data, significantly lacking the clinical explainability and morphological reasoning required for high-stakes medical decisions. To address this critical limitation, this study introduces an innovative hybrid framework that integrates deep learning algorithms with expert morphological domain knowledge to classify seven commonly encountered intestinal protozoan cysts (

Indexed as

AmebiasisEntamoeba complexEpidemiological surveillanceExplainable artificial intelligenceIntestinal protozoaMorphological screeningNeglected tropical diseasesParasite controlSDG 3: Good health and well-being

Identifiers

PMID42799032
PMCPMC13613540

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.