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New research · Ophthalmology
Bioengineering (Basel, Switzerland) · 2d
AI / informaticsBioengineering (Basel, Switzerland) · 2026

Development of a Deep Learning Model to Estimate Anemia from Palpebral Conjunctiva Taken with a Portable Slit-Lamp Microscope.

Yo Nakahara, Eisuke Shimizu, Takahiro Mizukami … Kazuno Negishi
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OphthalmologyAI / informatics

Deep learning detected anemia from eyelid photos with moderate accuracy

Development of a Deep Learning Model to Estimate Anemia from Palpebral Conjunctiva Taken with a Portable Slit-Lamp Microscope.

Yo Nakahara … Kazuno Negishi
Bioengineering (Basel, Switzerland) · 2026
Background

Anemia is a common systemic condition associated with adverse maternal, perioperative, and cardiovascular outcomes.

Purpose

This study aimed to develop and validate a deep learning system to estimate hemoglobin (Hb) concentration and screen for anemia using palpebral conjunctiva images captured with a smartphone-compatible slit-lamp microscope.

Methods

In this prospective observational study, 225 Japanese participants (20-92 years) underwent conjunctival imaging and blood testing.

n = 225 Japanese participants
Results
AUC of 0.75
correctly told anemia from no anemia about three-quarters of the time
n = 225 Japanese participants
More results

Video-level predicted Hb values moderately correlated with measured Hb ( r = 0.42).

Video-level aggregation achieved 69% accuracy.

“
Conclusion · 1 of 2

Deep learning analysis of palpebral conjunctiva images acquired with a portable slit-lamp microscope demonstrated the feasibility of non-invasive hemoglobin estimation and anemia screening.

Conclusion · 2 of 2

Although the proposed approach achieved moderate performance, further improvements in model accuracy and prospective multi-center validation are required before clinical implementation.

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