Evidence map›Paper›PMID 41430609›Full record

ArticleBMC medical education2025

Effectiveness and user satisfaction of an online cell morphology self-study and self-test platform on blood smear diagnostic skills.

Peichao Zhu, Yingbo Miao, Wei Zhou, Qiang Zhao, Huijing Wu, Chong Xu

Abstract readMulticenter Study
In one paragraph

Article in BMC medical education, 2025. 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
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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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

6 authors.

Peichao Zhu *Shanghai Centre for Clinical Laboratory, Department of Clinical Hematology, No. 528, Hong Shan Road, Pu Dong Area, Shanghai, 200126, China.
Yingbo Miao *Shanghai Centre for Clinical Laboratory, Department of Clinical Hematology, No. 528, Hong Shan Road, Pu Dong Area, Shanghai, 200126, China.
Wei ZhouShanghai Centre for Clinical Laboratory, Department of Clinical Hematology, No. 528, Hong Shan Road, Pu Dong Area, Shanghai, 200126, China.
Qiang ZhaoShanghai Centre for Clinical Laboratory, Department of Clinical Hematology, No. 528, Hong Shan Road, Pu Dong Area, Shanghai, 200126, China.
Huijing WuClinical Laboratory, Xinhong Community Health Service Center, No. 618, Shen Bin Road, Min Hang District, Shanghai, 201106, China. wuhuijing266@163.com.
Chong XuShanghai Centre for Clinical Laboratory, Department of Clinical Hematology, No. 528, Hong Shan Road, Pu Dong Area, Shanghai, 200126, China. xuchong@sccl.org.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the effectiveness and user satisfaction of an online cell morphology self-study and self-test platform designed to enhance the blood smear morphology diagnostic skills of hospital laboratory technicians.

methodsWe developed an online platform incorporating machine learning algorithms for automated recognition and classification of blood cell morphology. The platform allows users to assess cell types and provides real-time feedback. We conducted a multi-center study involving 1,034 registered users from various hospitals. User satisfaction was assessed using a 5-point Likert scale survey. The platform's effectiveness was evaluated by comparing users' diagnostic accuracy before and after using the platform for three months. We also compared the platform's performance with other automated morphology recognition systems.

resultsUser satisfaction was high, with mean scores of 4.5/5 for the platform's usefulness in recognizing morphological features, 4.4/5 for assisting in case diagnosis, 4.6/5 for serving as an educational tool, and 4.5/5 for facilitating morphology reporting. Users' diagnostic accuracy significantly improved from 65.3 to 89.6% after using the platform (p < 0.001). The platform's cell recognition accuracy (92.1%) was comparable to other state-of-the-art automated systems.

conclusionThe online cell morphology self-study and self-test platform is an effective tool for enhancing blood smear diagnostic skills of hospital laboratory technicians, with high user satisfaction and performance comparable to other automated systems. Widespread adoption of this platform could improve the quality of blood smear morphology assessment in clinical practice. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Blood CellsClinical CompetenceHumansMachine LearningBlood smear examinationDiagnostic skillsOnline education platformSelf-study

Identifiers

PMID41430609
PMCPMC12882271

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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.