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

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.

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Publication Details

Journal
Bioinformation
Published
2025-10-31
DOI
https://doi.org/10.6026/973206300213668
Primary Topic
Digital Imaging for Blood Diseases
Type
article
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article

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

Piyush Jani, Kawalinder Girgla, Amrit Podder, Steven S. Lazarus et al.
Bioinformation
Digital Imaging for Blood Diseases
article

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

Piyush Jani, Kawalinder Girgla, Amrit Podder, Steven S. Lazarus, Ravi Babu Surisetti, Ajeet Singh Nain, Sangeeta Gupta, Shyamali Dutta
article en

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.

BioinformationVol. 21(10)
Andhra University (IN), Lala Lajpat Rai Memorial Medical College (IN), Baba Raghav Das Medical College (IN), Government Medical College, Amritsar (IN), Ross University School of Medicine (BB), Teerthanker Mahaveer Medical College & Research Centre (IN), All India Institute of Medical Sciences (IN), Deen Dayal Upadhyaya Gorakhpur University (IN)
Openalex Percentile: Top 67%
Digital Imaging for Blood Diseases
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AI-assisted diagnosis of anemia through peripheral smear image analysis: A cross-validation study — Piyush Jani, Kawalinder Girgla, et al. · Bioinformation (2025) | TGRS Research Map | TGRS