ReviewHealth science reports2026
Applications of Machine Learning in Noninvasive Anemia Diagnosis: A Systematic Review Based on Cross-Industry Standard Process for Data Mining.
Review in Health science reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: Anemia is a disorder caused by insufficient red blood cell count or hemoglobin concentration, affecting nearly a quarter of the global population each year. An undiagnosed condition can escalate into life-threatening complications, particularly among pregnant women and children. Although anemia causes identifiable symptoms, accurate diagnosis typically requires invasive venipuncture with quality-controlled analyzers and reagents operated by trained personnel. This diagnostic procedure is laborious and costly in deprived regions and a less resource-demanding alternative would be beneficial. This systematic review aims at outlining the recent efforts for creating such noninvasive alternative procedures via machine learning (ML). Methods: A search was conducted through PubMed, Web of Science, and Scopus via tailored queries, yielding 1923 entries. Following screening, 58 eligible records were included in the qualitative synthesis. The Cross-Industry Standard Process for Data Mining (CRISP-DM) phases guided the selection of the main characteristics of each paper. Results: The contributions span a variety of algorithms and input data. The most frequent acquisition sites and data types were eye conjunctiva images, fingertip PPG signals, and palmar area images, respectively. Estimation of hemoglobin was of more interest than anemia classification. The light source in most experiments was at low energy or near-infrared spectrum. The smartphone served as a convenient device for signal acquisition or model deployment via a mobile app. Conclusion: Substantial progress has been achieved in ML applications for noninvasive anemia detection, leading to breakthroughs in prevention and diagnosis. Nonetheless, there were challenges to building robust procedures, such as data scarcity and imbalance, limited diversity, and confounding factors, some of which have been addressed recently.
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Registered trials
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.