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

NCT ID: NCT05395468

Last Updated: 2022-07-25

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 200 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-09-30
• Study Completion Date: 2024-06-30

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%.

Conditions

Iron-deficiency

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: PROSPECTIVE

Interventions

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

Intervention Type: OTHER

Eligibility Criteria

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.

• Minimum Eligible Age: 18 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: No

Sponsors

Université d'Auvergne

OTHER

Sponsor Role: collaborator

University Hospital, Clermont-Ferrand

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Hervé LOBBES

Role: PRINCIPAL_INVESTIGATOR

University Hospital, Clermont-Ferrand

Locations

CHU Clermont-Ferrand

Clermont-Ferrand, , France

SSU Université Clermont Auvergne

Clermont-Ferrand, , France

Countries

France

Central Contacts

Lise Laclautre

Role: CONTACT

promo_interne_drci@chu-clermontferrand.fr

+33 473 754 963

Facility Contacts

Lise Laclautre

Role: primary

Lise Laclautre

Role: primary

Other Identifiers

2021-A03087-34

Identifier Type: OTHER

Identifier Source: secondary_id

AOI 2021 LOBBES

Identifier Type: -

Identifier Source: org_study_id