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Random Forest Classification of Cognitive Impairment Using Digital Tree Drawing Test (dTDT) Data
(2024)
Early detection and diagnosis of dementia is a major challenge for medical research and practice. Hence, in the last decade, digital drawing tests became popular, showing sometimes even better performance than their paper-and-pencil versions. Combined with machine learning algorithms, these tests are used to differentiate between healthy people and people with mild cognitive impairment (MCI) or early Alzheimer’s disease (eAD), commonly using data from the Clock Drawing Test (CDT). In this investigation, a Random Forest Classification (RF) algorithm is trained on digital Tree Drawing Test (dTDT) data, containing socio-medical information and process data of 86 healthy people, 97 people with MCI, and 74 people with eAD. The results indicate that the binary classification works well for homogeneous groups, as demonstrated by a sensitivity of 0.85 and a specificity of 0.9 (AUC of 0.94). In contrast, the performance of both binary and multiclass classification degrades for groups with het erogeneous characteristics, which is reflected in a sensitivity of 0.91 and 0.29 and a specificity of 0.44 and 0.36 (AUC of 0.74 and 0.65), respectively. Nevertheless, as the early detection of cognitive impairment becomes increasingly important in healthcare, the results could be useful for models that aim for automatic identification
Introduction: Drawing tasks are an elementary component of psychological assessment in the evaluation of mental health. With the rise of digitalization not only in psychology but healthcare in general, digital drawing tools (dDTs) have also been developed for this purpose. This scoping review aims at summarizing the state of the art of dDTs available to assess mental health conditions in people above preschool age. Methods: PubMed, PsycInfo, PsycArticles, CINAHL, and Psychology and Behavioral Sciences Collection were searched for dDTs from 2000 onwards. The focus was on dDTs, which not only evaluate the final drawing, but also process data. Results: After applying the search and selection strategy, a total of 37 articles, comprising unique dDTs, remained for data extraction. Around 75 % of these articles were published after 2014 and most of them target adults (86.5 %). In addition, dDTs were mainly used in two areas: tremor detection and assessment of cognitive states, utilizing, for example, the Spiral Drawing Test and the Clock Drawing Test. Conclusion: Early detection of mental diseases is an increasingly important field in healthcare. Through the integration of digital and art based solutions, this area could expand into an interdisciplinary science. This review shows that the first steps in this direction have already been taken and that the possibilities for further research, e.g., on the optimized application of dDTs, are still open.
Hintergrund
Während der SARS-CoV-2-Pandemie ist es vorrangig, die Mitarbeiter vor Infektionsrisiken zu schützen und die Geschäftstätigkeit zu sichern. Neue Virusvarianten mit erhöhter Ansteckungsgefahr erfordern eine weiterentwickelte Risikostrategie.
Material und Methoden
Mehrere Standardmaßnahmen wie Tests, Isolierung und Quarantäne werden zu einer neuartigen Risikostrategie kombiniert. Epidemiologische Modellrechnungen und wissenschaftliche Erkenntnisse über den Verlauf der SARS-CoV-2-Infektiosität werden zur Optimierung dieser Strategie herangezogen. Das Verfahren ist in einem einfach zu bedienenden Rechner auf Excel-Basis implementiert.
Aufbau in der Praxis und Ergebnisse
Alternative Maßnahmenkombinationen und praktische Aspekte werden erörtert. Anhand von Beispielrechnungen wird die Wirkung der diskutierten Maßnahmen demonstriert.
Schlussfolgerung
Der aus diesen Grundlagen abgeleitete Quarantäne-Rechner ermöglicht es auch Nicht-Fachleuten, eine differenzierte Risikoanalyse durchzuführen und optimierte Maßnahmen einzuleiten. Gezielte Prüfroutinen und alternative Maßnahmen sichern die Personalverfügbarkeit.
Background: Emotions play a central role in mental disorder and especially in depression. They are sensed in the body, and it has recently been shown in healthy participants that these sensations can be differentiated between emotions. The aim of the current study was to assess bodily sensations for basic emotions induced by emotion eliciting pictures in depression.
Methods: 30 healthy controls (HC), 30 individuals suffering from Major depressive disorder (MDD) without medication use (MDDnm) and 30 individuals with MDD with medication use (MDDm) were shown emotional and neutral pictures and were asked to paint areas in an empty body silhouette where they felt an increase or decrease in activation. Body sensation maps were then calculated and statistical pattern recognition applied.
Results Results indicated statistically separable activation patterns for all three groups. MDDnm showed less overall activation than HCs, especially in sadness and fear, while MDDm showed stronger deactivation for all emotions than the other two groups.
Conclusions: We could show that emotion experience was associated with bodily sensations that are weaker in depression than in healthy controls and that antidepressant medication was correlated with an increased feeling of bodily deactivation.
Results give insights into the relevance for clinicians to acknowledge bodily sensations in the treatment of depression.
Background: Priority during the SARS-CoV2 pandemic is that employees need to be protected from infection risks and business activities need to be ensured. New virus variants with increased infection risks require an evolved risk strategy.
Material and methods: Several standard measures such as testing, isolation and quarantine are com-bined to a novel risk strategy. Epidemiological model calculations and scientific knowledge about the course of SARS-CoV2 infectivity are used to optimize this strategy. The procedure is implemented in an easy-to-use calculator based on Excel.
Layout in practice and results: Alternative combinations of measures and practical aspects are dis-cussed. Example calculations are used to demonstrate the effect of the discussed measures.
Conclusion: That quarantine calculator derived from these principles enables even non-specialists to perform a differentiated risk analysis and to introduce optimized measures. Targeted testing routines and alternative measures ensure staff availability.
KI-Lösungen und -Systemen wird im wirtschaftlichen sowie gesellschaftlichen Kontext zunehmend Bedeutung beigemessen. Doch aufgrund damit verbundener Implikationen darf die Diskussion hinsichtlich der Verantwortung beispielsweise mit Blick auf die Haftung für die Verursachung von Schäden nicht ausbleiben. Die Frage ist jedoch: Wer trägt die Verantwortung wofür? Insbesondere unter dem Aspekt, dass sich Verantwortung nicht umfassend reglementieren lässt. Zur Beantwortung und entsprechender Handlungsweise bedarf es hier eines gemeinsamen Spielverständnisses aller Beteiligten – denn nur so lässt sich gewährleisten, dass KI verantwortungsvoll eingesetzt werden kann, auch mit Blick auf die Zukunft. Im Beitrag wird hierfür ein Lösungsansatz vorgestellt.

