Artificial intelligence in biology and medicine, and radioprotection research: perspectives from Jerusalem.

While AI is widely used in biomedical research and medical practice, its use is constrained to few specific practical areas, e.g., radiomics. Participants of the workshop on "Artificial Intelligence in Biology and Medicine" (Jerusalem, Feb 14-15, 2023), both researchers and practitioners, aimed to build a holistic picture by exploring AI advancements, challenges and perspectives, as well as to suggest new fields for AI applications. Presentations showcased the potential of large language models (LLMs) in generating molecular structures, predicting protein-ligand interactions, and promoting democratization of AI development. Ethical concerns in medical decision making were also addressed. In biological applications, AI integration of multi-omics and clinical data elucidated the health relevant effects of low doses of ionizing radiation. Bayesian latent modeling identified statistical associations between unobserved variables. Medical applications highlighted liquid biopsy methods for non-invasive diagnostics, routine laboratory tests to identify overlooked illnesses, and AI's role in oral and maxillofacial imaging. Explainable AI and diverse image processing tools improved diagnostics, while text classification detected anorexic behavior in blog posts. The workshop fostered knowledge sharing, discussions, and emphasized the need for further AI development in radioprotection research in support of emerging public health issues. The organizers plan to continue the initiative as an annual event, promoting collaboration and addressing issues and perspectives in AI applications with a focus on low-dose radioprotection research. Researchers involved in radioprotection research and experts in relevant public policy domains are invited to explore the utility of AI in low-dose radiation research at the next workshop.

Early identification of subjective cognitive functional decline among patients with Parkinson's disease: a longitudinal pilot study.

Practical methods for early identification of Parkinson's disease (PD) mild cognitive impairment (PD-MCI) through changes in real-life daily functioning are scarce. The aim of the study was to examine whether the cognitive functional (CF) feature, comprising of seven self-reported Movement Disorder Society's (MDS) Unified Parkinson's Disease Rating Scale (UPDRS) items, predicts PD patients' cognitive functional status after a year. We conducted a 1-year follow-up of 34 PD patients (50-78 year; 70.6% men) suspected of MCI using the following measures: the MDS-UPDRS, UPDRS-CF feature, Beck Depression Inventory (BDI), Montreal Cognitive Assessment (MoCA), Trail Making Test (TMT), Parkinson's Disease Cognitive Functional Rating Scale (PD-CFRS), and Daily Living Questionnaire (DLQ). The first and second UPDRS-CF feature scores, and additional measures at the 1-year follow-up significantly correlated. Hierarchical regression revealed that the initial MoCA, TMT, and BDI scores predicted the second UPDRS-CF, and the first UPDRS-CF predicted 31% of the second PD-CFRS score variance. Depression moderated the relationship between the first UPDRS-CF score and the DLQ Part A. These results suggest practical, self-reported, daily functional markers for identifying gradual decline in PD patients. They consider the patients' heterogeneity, underlying cognitive pathology, and implications on daily functioning, health, and well-being.

Capturing Subjective Mild Cognitive Decline in Parkinson's Disease.

This study aimed to capture subjective daily functional cognitive decline among patients with Parkinson's disease. Participants (40-79 y; 78 with Parkinson's disease and 41 healthy matched controls) completed the Unified Parkinson's Disease Rating Scale (MDS-UPDRS), Parkinson's Disease Cognitive Functional Rating Scale (CFRS), Daily Living Questionnaire (DLQ), and Time Organisation and Participation Scale (TOPS) questionnaires. Patients with Parkinson's disease were divided into groups with or without suspected mild cognitive decline according to their scores on the Cognitive Functional (CF) feature, which is based on certain items of the MDS-UPDRS. Significant between-group differences were found in the DLQ and TOPS scores. Significant correlations were found among the questionnaire results, with specific DLQ and TOPS items accounting for 35% of the variance in the CF feature, which correlated with daily cognitive functional states. This study's results are relevant for detecting subtle deficits in Parkinson's disease patients suspected of mild cognitive decline, which can affect health and quality of life and relates to risk for later dementia.

Patients' Self-Report and Handwriting Performance Features as Indicators for Suspected Mild Cognitive Impairment in Parkinson's Disease.

Early identification of mild cognitive impairment (MCI) in Parkinson's disease (PD) patients can lessen emotional and physical complications. In this study, a cognitive functional (CF) feature using cognitive and daily living items of the Unified Parkinson's Disease Rating Scale served to define PD patients as suspected or not for MCI. The study aimed to compare objective handwriting performance measures with the perceived general functional abilities (PGF) of both groups, analyze correlations between handwriting performance measures and PGF for each group, and find out whether participants' general functional abilities, depression levels, and digitized handwriting measures predicted this CF feature. Seventy-eight participants diagnosed with PD by a neurologist (25 suspected for MCI based on the CF feature) completed the PGF as part of the Daily Living Questionnaire and wrote on a digitizer-affixed paper in the Computerized Penmanship Handwriting Evaluation Test. Results indicated significant group differences in PGF scores and handwriting stroke width, and significant medium correlations between PGF score, pen-stroke width, and the CF feature. Regression analyses indicated that PGF scores and mean stroke width accounted for 28% of the CF feature variance above age. Nuances of perceived daily functional abilities validated by objective measures may contribute to the early identification of suspected PD-MCI.

DailyCog: A Real-World Functional Cognitive Mobile Application for Evaluating Mild Cognitive Impairment (MCI) in Parkinson's Disease.

Parkinson's disease (PD) is the second most common progressive neurodegenerative disorder affecting patient functioning and quality of life. Aside from the motor symptoms of PD, cognitive impairment may occur at early stages of PD and has a substantial impact on patient emotional and physical health. Detecting these early signs through actual daily functioning while the patient is still functionally independent is challenging. We developed DailyCog-a smartphone application for the detection of mild cognitive impairment. DailyCog includes an environment that simulates daily tasks, such as making a drink and shopping, as well as a self-report questionnaire related to daily events performed at home requiring executive functions and visual-spatial abilities, and psychomotor speed. We present the detailed design of DailyCog and discuss various considerations that influenced the design. We tested DailyCog on patients with mild cognitive impairment in PD. Our case study demonstrates how the markers we used coincide with the cognitive levels of the users. We present the outcome of our usability study that found that most users were able to use our app with ease, and provide details on how various features were used, along with some of the difficulties that were identified.

The Montreal Cognitive Assessment: Is It Suitable for Identifying Mild Cognitive Impairment in Parkinson's Disease?

BACKGROUND: Administering an abbreviated global cognitive test, such as the Montreal Cognitive Assessment (MoCA), is necessary for the recommended first-level diagnostic criteria for mild cognitive impairment (MCI) in Parkinson's disease (PD). Level II requires administering cognitive functioning neuropsychological tests. The MoCA's suitability for identifying PD-MCI is questionable and, despite the importance of cognitive deficits reflected through daily functioning in identifying PD-MCI, knowledge about it is scarce. OBJECTIVES: To explore neuropsychological test scores of patients with PD who were categorized based on their MoCA scores and to analyze correlations between this categorization and patients' self-reports about daily functional-related cognitive abilities. METHODS: A total of 78 patients aged 42 to 78 years participated: 46 with low MoCA scores (22-25) and 32 with high MoCA scores (26-30). Medical assessments and level II neuropsychological assessment tools were administered along with standardized self-report questionnaires about daily functioning that reflects patients' cognitive abilities. RESULTS: A high percentage of the low MoCA group obtained neuropsychological test scores within the normal range; a notable number in the high MoCA group were identified with MCI-level scores on various neuropsychological tests. Suspected PD-MCI according to the level I criteria did not correspond well with the level II criteria. Positive correlations were found among the 3 self-report questionnaires. CONCLUSIONS: These results support the ongoing discussion of the complexity of capturing PD-MCI. Considering the neuropsychological tests results, assessments that reflect cognitive encounters in real life daily confrontations are warranted among people diagnosed with PD who are at risk for cognitive decline.