Using Artificial Intelligence to Detect Early Signs of Alzheimer's Disease in People With Memory Concerns

AHEAD: AI-driven Brain Health for Early Alzheimer's Disease Detection in Individuals With Subjective Cognitive Decline

AHEAD is a prospective, longitudinal, risk-stratified single-arm interventional study enrolling 300 patients with Subjective Cognitive Decline (SCD) at IRCCS San Raffaele Hospital, Milan, Italy.

The study uses artificial intelligence (AI) to integrate multimodal data - including MRI, EEG, Optical Coherence Tomography (OCT), neuropsychological assessments, and plasma biomarkers - to identify individuals with underlying Alzheimer's disease (AD) biology and predict cognitive progression.

Only participants found to be AD plasma biomarker positive (SCD+) undergo longitudinal follow-up at 12 and 24 months. Participants classified as high risk additionally receive a 6-month personalized multidisciplinary intervention combining high-frequency transcranial magnetic stimulation (TMS), digital cognitive training, structured physical exercise, and targeted management of modifiable vascular and behavioral risk factors.

Subjective Cognitive Decline (SCD) refers to the self-perception of worsening cognitive abilities despite normal performance on standardized neuropsychological testing. It affects approximately 10% of the general population and 20-35% of patients attending memory clinics. Although the majority of individuals with SCD do not progress to clinical forms of Alzheimer's disease (AD), they show a higher prevalence of AD-related pathological biomarkers compared with individuals without subjective cognitive complaints, with rates of cognitive decline estimated at approximately 20% per 1,000 person-years in memory clinic patients.

Plasma biomarkers for AD represent minimally invasive and easily accessible diagnostic tools; however, their large-scale implementation in the broad SCD population is neither economically nor ethically sustainable because of costs, the risk of overdiagnosis, and the associated psychological burden. Artificial intelligence (AI) may represent a transformative tool for addressing the complexity of SCD management. By integrating multimodal data including cognitive assessments, MRI, EEG, and OCT, AI may help identify those individuals with SCD most likely to benefit from further diagnostic investigations, including plasma biomarker assessment.

At baseline (T0), all participants undergo a minimum assessment dataset including clinical evaluation, standard neuropsychological assessment, structural MRI, and blood sampling. A subset additionally undergoes a comprehensive risk assessment, extended neuropsychological evaluation including digital cognitive testing and the Preclinical Alzheimer Cognitive Composite (PACC), resting-state EEG, and retinal imaging through Optical Coherence Tomography (OCT).

Only patients found to be AD plasma biomarker positive (SCD+) undergo longitudinal follow-up visits at 12 months (M12) and 24 months (M24), including clinical evaluation, neuropsychological assessments, and blood sampling to monitor cognitive and biological progression.

Participants stratified as high risk - defined as plasma p-tau217 greater than 0.1325 pg/mL, and/or APOE epsilon4 carrier, and/or elevated CAIDE Dementia Risk Score - enter a 6-month single-arm multidisciplinary intervention comprising: (1) targeted management of modifiable vascular and behavioral risk factors with monthly remote follow-up; (2) high-frequency TMS during the first 4 weeks (2-3 sessions per week); (3) home-based digital cognitive training, 2 sessions per week of 30 minutes each over 5 months; (4) structured physical exercise (walking, cycling, resistance training), 2 sessions per week of 30 minutes each.

A retrospective SCD cohort (rSCD), comprising patients who underwent the minimum assessment dataset within one year prior to enrollment and were found to be AD plasma biomarker positive, undergoes follow-up at M12 and M24 according to the same longitudinal protocol, with the intervention starting at M12.

AI models will integrate multimodal baseline data using machine learning (logistic regression, random forest), deep learning (CNNs for MRI/OCT, RNNs/Transformers for EEG), and survival analysis (Cox proportional hazards, DeepSurv). All models will be validated using k-fold cross-validation with performance metrics including AUC, sensitivity, specificity, balanced accuracy, and positive and negative predictive values.

Inclusion Criteria:

1. Diagnosis of Subjective Cognitive Decline (SCD) according to international diagnostic criteria (Jessen et al., 2014).
2. Self-experienced persistent decline in cognitive capacity in comparison with a previously normal status and unrelated to an acute event.
3. Normal age-, gender-, and education-adjusted performance on standardized cognitive tests used to classify mild cognitive impairment (MCI) or prodromal AD.
4. Age greater than or equal to 40 years.
5. Native Italian Speaker.
6. Stable pharmacological treatment for at least 4 weeks prior to enrollment.
7. Provision of oral and written informed consent to study participation.

Exclusion Criteria:

1. Presence of MCI, prodromal AD, or dementia.
2. Any major systemic, psychiatric, or neurological disturbance.
3. Medical conditions or substance abuse that could interfere with cognition.
4. Pacemaker and/or other implanted neurostimulation devices in the head/neck district.
5. Contraindications to undergoing MRI examination.
6. Brain damage at routine MRI, including extensive cerebrovascular disorders.
7. Traumatic or surgical wounds that could determine a risk of infection at the site of non-invasive stimulation.
8. Scalp alterations that could determine the spread of excessive current from the device.
9. Known history of epilepsy (due to small risk of seizure induction from rTMS in epileptic patients).
10. Denial of oral and written informed consent to study participation.