Iron-deficiency
Conditions
Keywords
Iron deficiency, eyes, women
Brief summary
The objective of our work is to predict the value of ferritin from the eye, thus constituting an original, non-invasive diagnostic method of iron deficiency. To be usable in real life, the algorithm must be comparable to the performance of the reference diagnostic test (determination of ferritin), allowing to obtain a sensitivity of about 90% and a specificity \> 95%.
Detailed description
Currently, the diagnosis of iron deficiency is invasive, as it requires a venous puncture for serum ferritin assay and blood count analysis to diagnose iron deficiency anemia. This dosage is expensive and represents a major brake in the large-scale screening of iron deficiency, especially in developing countries. Most of the clinical signs of iron deficiency (asthenia, cheilitis, glossitis, alopecia, restless legs syndrome) are not very specific and the diagnosis is most often fortuitous or carried out as part of screening in a population at risk. Iron is essential for many functions of the body, including the synthesis of collagen: in case of deficiency, it is produced with an altered and finer structure. In the eyes, the sclera consists of collagen type IV, whose thinning causes the visualization of the choroidal vessels responsible for a characteristic blue tint. A preliminary work carried out by our team made it possible to measure the increase in the amount of blue color in the sclera of deficient patients, objectifying this clinical sign for the first time. From photographs of patients' eyes, we extracted the percentile of blue contained in the pixels of the digital images of the sclera. This work continued with the automation of the recognition of eye structures, especially the sclera. In order to improve the diagnostic performance of this original and non-invasive method, we want to apply deep-learning methods, which have already been proven in several areas: related to ophthalmology but also in a very encouraging way in the non-invasive diagnosis of anemia. The objective of our work is to predict the value of ferritin from the eye, thus constituting an original, non-invasive diagnostic method of iron deficiency. To be usable in real life, the algorithm must be comparable to the performance of the reference diagnostic test (determination of ferritin), allowing to obtain a sensitivity of about 90% and a specificity \> 95%.
Interventions
OTHERphotographs of each eye
All subjects included will take 5 photographs of each eye according to a standardised procedure in terms of distance, lighting and framing
Sponsors
Université d'Auvergne
University Hospital, Clermont-Ferrand
Study design
Observational model
CASE_ONLY
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Female sex * Age ≥ 18 years old * Able to express non-opposition to participation in rese * Patients affiliated to a social security scheme * Screenng for iron deficiency within 15 days of inclusion, including * Blood count : value of hemoglobin, mean blood volume * Serum ferritin
Exclusion criteria
* Personal history of severe trauma or surgery of both eyes (apart from refractive surgery performed more than 3 months ago) * Personal history of hereditary connective tissue pathology including Marfan's disease, Ehler Danlos syndrome, imperfect osteogenesis. * Personal history of pathology responsible for chronic hemolysis due to yellow coloration induced by hyperbilirubinemia: sickle cell disease, major thalassemia. * Prolonged treatment with minocycline (\> 1 month). * Oral or intravenous martial supplementation started more than 15 days prior to taking the sclera photographs. * Person deprived of liberty by administrative or judicial decision or placed under judicial protection (guardianship or supervision) * Pregnant or breastfeeding woman * Expression of opposition to research.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Validate in a real clinical situation (systematic screening for iron deficiency) | evaluation 15 day after diagnostic | Validate in a real clinical situation (systematic screening for iron deficiency) a tool for predicting ferritin levels based on digital photographs of the ocular sclera, with confrontation of a learning base treated by deep learning, and a test base |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| To study the informational value of photographic data | evaluation 15 day after diagnostic | To study the informational value of photographic data from the sclera concerning (in pixels) other biological parameters, in particular hemoglobin levels (in g/dl). |
| Identify external factors influencing the quality of the ferritin | evaluation 15 day after diagnostic | Identify external factors influencing the quality of the ferritin prediction algorithm (in particular, exposure and light polarization, which will be data automatically recorded by the camera allowing shooting, but also phototype according fitzpatrick classification) |
Countries
France
Contacts
Primary ContactLise Laclautre
Outcome results
None listed