Diagnosis of Iron Deficiency by Artificial Intelligence Analysis of Eye Photography.

Study Overview
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Brief Summary
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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
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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%.

Official Title
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Diagnosis of Iron Deficiency by Artificial Intelligence Analysis of Eye Photography.

Conditions
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Iron-deficiency

Intervention / Treatment
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* Other: photographs of each eye


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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Female

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Other: photographs 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|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Validate in a real clinical situation (systematic screening for iron deficiency) | 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 | evaluation 15 day after diagnostic | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| To study the informational value of photographic data | To study the informational value of photographic data from the sclera concerning (in pixels) other biological parameters, in particular hemoglobin levels (in g/dl). | evaluation 15 day after diagnostic | 
| Identify external factors influencing the quality of the ferritin | 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) | evaluation 15 day after diagnostic | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Iron deficiency, eyes, women