Innovative diagnostic tools for early detection of Alzheimer's disease.

Current state-of-the-art diagnostic measures of Alzheimer's disease (AD) are invasive (cerebrospinal fluid analysis), expensive (neuroimaging) and time-consuming (neuropsychological assessment) and thus have limited accessibility as frontline screening and diagnostic tools for AD. Thus, there is an increasing need for additional noninvasive and/or cost-effective tools, allowing identification of subjects in the preclinical or early clinical stages of AD who could be suitable for further cognitive evaluation and dementia diagnostics. Implementation of such tests may facilitate early and potentially more effective therapeutic and preventative strategies for AD. Before applying them in clinical practice, these tools should be examined in ongoing large clinical trials. This review will summarize and highlight the most promising screening tools including neuropsychometric, clinical, blood, and neurophysiological tests.

Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs.

BACKGROUND/OBJECTIVES: Artificial intelligence can assist with ocular image analysis for screening and diagnosis, but it is not yet capable of autonomous full-spectrum screening. Hypothetically, false-positive results may have unrealized screening potential arising from signals persisting despite training and/or ambiguous signals such as from biomarker overlap or high comorbidity. The study aimed to explore the potential to detect clinically useful incidental ocular biomarkers by screening fundus photographs of hypertensive adults using diabetic deep learning algorithms. SUBJECTS/METHODS: Patients referred for treatment-resistant hypertension were imaged at a hospital unit in Perth, Australia, between 2016 and 2022. The same 45° colour fundus photograph selected for each of the 433 participants imaged was processed by three deep learning algorithms. Two expert retinal specialists graded all false-positive results for diabetic retinopathy in non-diabetic participants. RESULTS: Of the 29 non-diabetic participants misclassified as positive for diabetic retinopathy, 28 (97%) had clinically useful retinal biomarkers. The models designed to screen for fewer diseases captured more incidental disease. All three algorithms showed a positive correlation between severity of hypertensive retinopathy and misclassified diabetic retinopathy. CONCLUSIONS: The results suggest that diabetic deep learning models may be responsive to hypertensive and other clinically useful retinal biomarkers within an at-risk, hypertensive cohort. Observing that models trained for fewer diseases captured more incidental pathology increases confidence in signalling hypotheses aligned with using self-supervised learning to develop autonomous comprehensive screening. Meanwhile, non-referable and false-positive outputs of other deep learning screening models could be explored for immediate clinical use in other populations.

Pupil response biomarkers distinguish amyloid precursor protein mutation carriers from non-carriers.

CONTEXT: Alzheimer's disease (AD) is usually only diagnosed many years after pathology begins. Earlier detection would allow emerging interventions to have a greater chance to preserve healthy brain function. A rare form of Alzheimer's disease, caused by autosomal-dominant mutations, affects carriers with 100% certainty and at a younger age specific to their mutation. Studying families with these mutations allows a unique investigation of the temporal sequence of biomarker changes in Alzheimer's disease. OBJECTIVE: To determine whether the pupil flash response (PFR), previously reported to be altered in sporadic Alzheimer's disease, is different in pre-symptomatic mutation carriers. DESIGN: Researchers blinded to participant mutation status collected pupil response data from cognitively normal participants in the Dominantly Inherited Alzheimer's Network (DIAN) Study during 2010-2011. SETTING: The pupil response was examined at the McCusker Alzheimer's Research Foundation in Perth, Western Australia. PARTICIPANTS: Participants were from a single family harboring an Amyloid-Beta Precursor Protein genetic mutation (APPGlu693Gln). Six carriers and six non-carriers were available for pupil testing (age 43.0±8.3 years old, 2 males and 10 females, 4 with hypertension). MAIN OUTCOME MEASURE: Pupil response parameter comparison between mutation carriers and non-carriers. RESULTS: 75% recovery time was longer in mutation carriers (p<0.0003, ROC AUC 1.000, Sensitivity 100%, Specificity 100%) and percentage recovery 3.5 seconds after stimulus was less in mutation carriers (p<0.006, ROC AUC 1.000, Sensitivity 100%, Specificity 100%). CONCLUSIONS: PFR changes occur pre-symptomatically in autosomal dominant AD mutation carriers, supporting further investigation of PFR for early detection of AD.