Diagnosis of Iron Deficiency by Artificial Intelligence Analysis of Eye Photography. (CaFerIA)

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%.

Study Overview

• Status: Not yet recruiting
• Conditions: Iron-deficiency
• Intervention / Treatment: Other: photographs of each eye

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%.

• Study Type: Observational
• Enrollment (Anticipated): 200

Contacts and Locations

• Study Contact: Name: Lise Laclautre; Phone Number: +33 473 754 963; Email: promo_interne_drci@chu-clermontferrand.fr

Study Locations

• France
  • Clermont-Ferrand, France
    • Chu Clermont-Ferrand
    • Principal Investigator: Hervé LOBBES
  • Clermont-Ferrand, France
    • SSU Université Clermont Auvergne
    • Principal Investigator: Laurent GERBAUD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: Female

• Sampling Method: Non-Probability Sample

Study Population

Women 18 years or older with iron deficiency

Description

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.

Study Plan

How is the study designed?

What is the study measuring?

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

Collaborators and Investigators

• Sponsor: University Hospital, Clermont-Ferrand
• Collaborators: Université d'Auvergne
• Investigators: Principal Investigator: Hervé LOBBES, University Hospital, Clermont-Ferrand

Study record dates

Study Major Dates

• Study Start (Anticipated): September 1, 2022
• Primary Completion (Anticipated): December 1, 2023
• Study Completion (Anticipated): June 1, 2024

Study Registration Dates

• First Submitted: May 12, 2022
• First Submitted That Met QC Criteria: May 24, 2022
• First Posted (Actual): May 27, 2022

Study Record Updates

• Last Update Posted (Actual): July 25, 2022
• Last Update Submitted That Met QC Criteria: July 21, 2022
• Last Verified: May 1, 2022

More Information

Terms related to this study

• Keywords: women; Iron deficiency; eyes
• Additional Relevant MeSH Terms: Metabolic Diseases; Hematologic Diseases; Anemia, Hypochromic; Anemia; Iron Metabolism Disorders; Anemia, Iron-Deficiency
• Other Study ID Numbers: AOI 2021 LOBBES; 2021-A03087-34 (Other Identifier: ANSM)

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No