Clinical trial NCT05395468: Diagnosis of Iron Deficiency by Artificial Intelligence Analysis of Eye Photography.
Official title: Diagnosis of Iron Deficiency by Artificial Intelligence Analysis of Eye Photography.
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Iron-deficiency
Keywords: Iron deficiency
eyes
women
Lead sponsor: University Hospital, Clermont-Ferrand
Sponsor class: OTHER
Collaborators: name: Université d'Auvergne
First posted: 2022-05-27
Enrollment: 200
Sex: FEMALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Women 18 years or older with iron deficiency

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%.

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.

Primary outcomes:
measure: Validate in a real clinical situation (systematic screening for iron deficiency)
timeFrame: evaluation 15 day after diagnostic
Secondary outcomes:
measure: To study the informational value of photographic data
timeFrame: evaluation 15 day after diagnostic
measure: Identify external factors influencing the quality of the ferritin
timeFrame: evaluation 15 day after diagnostic
Other outcomes:
Locations:
city: Clermont-Ferrand
country: France
facility: CHU Clermont-Ferrand
city: Clermont-Ferrand
country: France
facility: SSU Université Clermont Auvergne
Study officials:
affiliation: University Hospital, Clermont-Ferrand
name: Hervé LOBBES
role: PRINCIPAL_INVESTIGATOR
References: