Semi-global Parameterization of Online Handwriting Features for Characterizing Early-Stage Alzheimer and Mild Cognitive Impairment

Use of artificial intelligence techniques for detection of mild cognitive impairment: A systematic scoping review

AIMS AND OBJECTIVES

The objective of this scoping review is to explore the types and mechanisms of Artificial intelligence (AI) techniques for detecting mild cognitive impairment (MCI).

BACKGROUND

Early detection of MCI is crucial because it may progress to Alzheimer's disease.

DESIGN

A systematic scoping review.

METHODS

Five-step framework of Arksey and O'Malley was used following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews checklist. A total of 11 databases (PubMed, EMBASE, CINAHL, Cochrane Library, Scopus, Web of Science, IEEE Explore, Science.gov, ACM digital library, arXIV and ProQuest) was used to search from inception till 17th December 2021. Grey literature and reference list were searched. Articles screening and data charting were conducted by two independent reviewers.

RESULTS

There were a total of 70 articles included from 2011 to 2022 across 16 countries. Four types of AI techniques were found, namely machine learning (ML), deep learning (DL), fuzzy logic (FL) and technique combinations. Herein, ML detects similar pattern within preselected data to classify subjects into non-MCI or MCI groups. Meanwhile, DL performs classification based on data patterns and data analyses are performed by themselves. Furthermore, FL utilises human-defined rules to decide the degree to which a person has MCI. A combination of AI techniques enhances the feature preparation phase for ML or DL to perform accurate classification.

CONCLUSION

Although AI-based MCI detection tool is critical for healthcare decision-making, clinical utility and risks remain underexplored. Hopefully, this review equips clinicians with background AI knowledge to address these clinical concerns. Hence, future research should explore more techniques and representative datasets to improve AI development.

RELEVANCE TO CLINICAL PRACTICE

Results of this review can increase the knowledge of AI-based MCI detection tools.

REVIEW REGISTRATION

This study protocol was registered in the Open Science Framework Registries (https://osf.io/45rdt).

Handwriting Changes in Alzheimer's Disease: A Systematic Review

BACKGROUND

Handwriting is a complex process involving fine motor skills, kinesthetic components, and several cognitive domains, often impaired by Alzheimer's disease (AD).

OBJECTIVE

Provide a systematic review of handwriting changes in AD, highlighting the effects on motor, visuospatial and linguistic features, and to identify new research topics.

METHODS

A search was conducted on PubMed, Scopus, and Web of Science to identify studies on AD and handwriting. The review followed PRISMA norms and analyzed 91 articles after screening and final selection.

RESULTS

Handwriting is impaired at all levels of the motor-cognitive hierarchy in AD, particularly in text, with higher preservation of signatures. Visuospatial and linguistic features were more affected. Established findings for motor features included higher variability in AD signatures, higher in-air/on-surface time ratio and longer duration in text, longer start time/reaction time, and lower fluency. There were conflicting findings for pressure and velocity in motor features, as well as size, legibility, and pen lifts in general features. For linguistic features, findings were contradictory for error patterns, as well as the association between agraphia and severity of cognitive deficits.

CONCLUSIONS

Further re-evaluation studies are needed to clarify the divergent results on motor, general, and linguistic features. There is also a lack of research on the influence of AD on signatures and the effect of AD variants on handwriting. Such research would have an impact on clinical management (e.g., for early detection and patient follow-up using handwriting tasks), or forensic examination aimed at signatory identification.