Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-24 @ 5:39 PM
Ignite Modification Date:
2025-12-24 @ 5:39 PM
NCT ID:
NCT05395468
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%.
Study:
NCT05395468
Study Brief:
Protocol Section:
NCT05395468