ArticleJournal of biomedical physics & engineering2017
Diagnosis of the OCD Patients using Drawing Features of the Bender Gestalt Shapes.
Article in Journal of biomedical physics & engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- The Bender-Gestalt Test: A Systematic Review.Cureus · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSince psychological tests such as questionnaire or drawing tests are almost qualitative, their results carry a degree of uncertainty and sometimes subjectivity. The deficiency of all drawing tests is that the assessment is carried out after drawing the objects and lots of information such as pen angle, speed, curvature and pressure are missed through the test. In other words, the psychologists cannot assess their patients while running the tests. One of the famous drawing tests to measure the degree of Obsession Compulsion Disorder (OCD) is the Bender Gestalt, though its reliability is not promising.
objectiveThe main objective of this study is to make the Bender Gestalt test quantitative; therefore, an optical pen along with a digital tablet is utilized to preserve the key drawing features of OCD patients during the test. MATERIALS AND
methodsAmong a large population of patients who referred to a special clinic of OCD, 50 under therapy subjects voluntarily took part in this study. In contrast, 50 subjects with no sign of OCD performed the test as a control group. This test contains 9 shapes and the participants were not constraint to draw the shapes in a certain interval of time; consequently, to classify the stream of feature vectors (samples through drawing) Hidden Markov Model (HMM) is employed and its flexibility increased by incorporating the fuzzy technique into its learning scheme.
resultsApplying fuzzy HMM classifier to the data stream of subjects could classify two groups up to 95.2% accuracy, whereas the results by applying the standard HMM resulted in 94.5%. In addition, multi-layer perceptron (MLP), as a strong static classifier, is applied to the features and resulted in 86.6% accuracy.
conclusionApplying the pair of T-test to the results implies a significant supremacy of the fuzzy HMM to the standard HMM and MLP classifiers.
Indexed as
Identifiers
28462208PMC5401134What OpenQuestion holds
Registered trials
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