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

Condition(s) studied

Investigational drug(s) / intervention(s)

High-frequency Transcranial Magnetic Stimulation (TMS): High-frequency repetitive TMS (rTMS) delivered according to an intensive protocol during the first 4 weeks of the intervention period (2-3 sessions per week). Applied to brain regions associated with cognitive function to optimize brain health and reduce risk of cognitive decline. Administered by trained professionals following international safety guidelines (Rossi et al., 2009).

Digital Cognitive Training: Home-based cognitive training delivered via digital platform over 5 months: 2 sessions per week of 30 minutes each. Targets perceived cognitive deficits and related domains (memory, executive functions, attention, visuospatial abilities, language) with progressive adaptation to individual performance level. Compliance monitored via dedicated applications and/or activity diaries. Monthly remote meetings with neuropsychologists to monitor progress.

Structured Physical Exercise: Home-based structured physical exercise program over 5 months: 2 sessions per week of 30 minutes each. Activities include walking, cycling, and global resistance training. An in-person familiarization session is conducted before program start. Monthly remote meetings with physiotherapists to monitor progress and adapt the program. Compliance remotely monitored via dedicated applications and/or activity diaries.

Management of Modifiable Vascular and Behavioral Risk Factors: Personalized pharmacological and non-pharmacological interventions targeting modifiable vascular and behavioral risk factors including blood pressure, cholesterol, BMI, physical inactivity, dietary habits, sleep quality, and social isolation. Monthly remote follow-up visits over 6 months to monitor treatment adherence and optimize risk factor control.

Study summary

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.

Eligibility

Primary outcome measure(s)

• Diagnostic accuracy of AI models in identifying AD biomarker-positive SCD subjects (AUC) — Baseline (study entry)
  Area under the ROC curve (AUC), sensitivity, and specificity of AI models in discriminating between plasma AD biomarker-positive and biomarker-negative SCD subjects, assessed at study entry.

Trial sites (1)

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