Evidence map›Paper›PMID 41623806›Full record

ArticleBioinformation2025

AI-assisted diagnosis of anemia through peripheral smear image analysis: A cross-validation study.

Ashita Nain, Sangeeta Gupta, Sylvester Noeldoss Lazarus, Kawalinder Kaur Girgla, Parth Jani, Amrit Podder, Sreemoyee Dutta, Ravi Babu Surisetti

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Ashita NainDepartment of Physiology, Lala Lajpat Rai Memorial Medical College, Meerut Uttar Pradesh, India.
Sangeeta GuptaDepartment of Physiology, AIIMS Gorakhpur, Uttar Pradesh, India.
Sylvester Noeldoss LazarusDepartment of Pathology, American University of Barbados, Wildey, Barbados (Caricom).
Kawalinder Kaur GirglaDepartment of Physiology, SGRD Institute of Medical Sciences and Research, Sri Amritsar, Punjab, India.
Parth JaniDepartment of General Medicine, All India Institute of Medical Sciences, Rajkot, Gujarat, India.
Amrit PodderDepartment of Physiology, Teerthanker Mahaveer Medical College & Research Centre, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India.
Sreemoyee DuttaDepartment of Periodontology, Kalinga Institute of Dental Sciences, Bhubaneswar, Odisha, India.
Ravi Babu SurisettiDepartment of Zoology, Trans, Disciplinary Research Hub, Andhra University, Visakhapatnam Andhra Pradesh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A deep semi-supervised learning model for automating anemia detection and classification from peripheral blood smear images is of interest. A convolutional neural network was trained on 3,200 images, with only 25% annotated by expert hematologists. The model achieved a classification accuracy of 93.4% and F1-scores above 90% for key anemia subtypes, demonstrating strong agreement with expert diagnoses (κ = 0.89). It significantly reduced diagnostic time and performed well in detecting microcytic and sickle cell anemia. This AI-based framework shows great potential for accurate anemia diagnosis, especially in resource-limited settings.

Indexed as

Anemia diagnosisdeep learningmedical image analysisperipheral blood smearred blood cellssemi-supervised learning

Identifiers

PMID41623806
PMCPMC12859316

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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.