ArticleCureus2024
Enhancing Anemia Detection With Non-invasive Anemia Detection With AI (NiADA): Insights From Clinical Validations and Physician Observations.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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
2 citing papers in PubMed.
- Feasibility of a Noncontact Photoplethysmography-Based Mobile App for Noninvasive Hemoglobin Monitoring: Exploratory Observational Study.JMIR formative research · 2026Observational
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Anemia, a critical public health issue, affects nearly two billion people globally. Frequent monitoring of blood hemoglobin levels is essential for managing its burden. While point-of-care testing (POCT) devices facilitate hemoglobin testing in resource-limited settings, most are invasive and have inherent limitations. The Non-Invasive Anemia Detection App (NiADA) (Monere, UT) offers a non-invasive alternative, utilizing artificial intelligence (AI) to estimate hemoglobin levels from images of the lower eyelid. This low-cost, real-time solution employs a custom computer vision deep-learning algorithm for hemoglobin levels, offering significant potential for early diagnosis and management of anemia. Methods This study evaluated NiADA in two phases. In the first phase, its performance was compared to laboratory measurements and the minimally invasive POCT device, Hemocue Hb 301. In this study, the current version of NiADA version 2 (V2) is also compared against the previous version of NiADA version 1 (V1) to show the improvement in the last six months. In the second phase, NiADA's results were compared against hemoglobin estimations made by a group of medical professionals, as well as against lab analyzers. For both phases, NiADA performance was evaluated using the Bland-Altman plot, regression coefficients, percentage of acceptable limit, Pearson correlation coefficient, and Lin's concordance correlation coefficient. Results The mean difference between NiADA-V2 and laboratory-estimated hemoglobin values was -0.11 g/dL, with limits of agreement (LOA) ranging from +2.86 to -2.64 g/dL, where the upper limit is comparable with HemoCue. The NiADA-V2-acceptable range (percentage of samples falling within ±1 g/dL absolute error) increased to 54% compared to 40% in NiADA-V1. Additionally, NiADA outperformed medical professionals, showing a mean difference of 0.07 g/dL compared to medical professionals' 0.42 g/dL. Conclusion NiADA, as a non-invasive application, exhibits performance comparable to minimally invasive tools and other POCT devices. Its accuracy exceeds that of medical professionals, making it a viable option for anemia screening and monitoring, particularly in community medicine and regions with limited healthcare resources.
Indexed as
Identifiers
What OpenQuestion holds
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.