Documenting the de-identification process of clinical and imaging data for AI for health imaging projects.

Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive "sick-care" approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients. This paper explores the approaches in the domain of five EU projects working on the creation of ethically compliant and GDPR-regulated European medical imaging platforms, focused on cancer-related data. It presents the individual approaches to the de-identification of imaging data, and describes the problems and the solutions adopted in each case. Further, lessons learned are provided, enabling future projects to optimally handle the problem of data de-identification. CRITICAL RELEVANCE STATEMENT: This paper presents key approaches from five flagship EU projects for the de-identification of imaging and clinical data offering valuable insights and guidelines in the domain. KEY POINTS: ΑΙ models for health imaging require access to large amounts of data. Access to large imaging datasets requires an appropriate de-identification process. This paper provides de-identification guidelines from the AI for health imaging (AI4HI) projects.

Enhanced depth OCT imaging of the lamina cribrosa for 24 hours.

The lamina cribrosa thickness (LCT) could be affected by dynamic changes in its structure. Using Spectral-domain-optical coherence tomography (SD-OCT), we have studied the behaviour of the laminar region in 14 young subjects over 24h. Significant changes in LCT were observed, depending on the time at which the measurement was taken, with the maximum thickness being observed at 7.30 p.m., and the minimum at 7.30 a.m. This finding could suggests a circadian pattern in the LCT thickness in healthy subjects, which could have implications for the classification, diagnosis and prognosis of both normal and glaucomatous subjects.

[Simulation in ophthalmology]. / Simulación en oftalmología.

CLINICAL CASES: Four cases of patients simulating eye problems are reported. The patients included: an adopted 9 year-old girl complaining of bilateral visual loss, a 10 year-old girl with school and family problems complaining of decreased visual acuity, a 22 year-old female mentioning unilateral visual loss after a car accident, and a 30 year-old female complaining of decreased visual acuity after refractive surgery. Structural and functional tests were performed on all 4 patients and none of the tests revealed any pathological findings. The 4 cases were diagnosed as patients simulating ophthalmological symptoms. DISCUSSION: A non-organic cause of visual loss is a common problem in Ophthalmology. Simulation is the diagnosis used when an adult consciously tries to obtain some kind of benefit without proven pathology. In the case of children, obtaining benefit is not their intention, but psycho-social and psychiatric problems are frequently associated in these cases.