A diagnostic support system based on pain drawings: binary and k-disease classification of EDS, GBS, FSHD, PROMM, and a control group with Pain2D
Orphanet Journal of Rare Diseases, vol. 18, pp. 70
Abstract
BACKGROUND AND OBJECTIVE: The diagnosis of rare diseases (RDs) is often challenging due to their rarity, variability and the high number of individual RDs, resulting in a delay in diagnosis with adverse effects for patients and healthcare systems. The development of computer assisted diagnostic decision support systems could help to improve these problems by supporting differential diagnosis and by prompting physicians to initiate the right diagnostic tests. Towards this end, we developed, trained and tested a machine learning model implemented as part of the software called Pain2D to classify four rare diseases (EDS, GBS, FSHD and PROMM), as well as a control group of unspecific chronic pain, from pen-and-paper pain drawings filled in by patients. METHODS: Pain drawings (PDs) were collected from patients suffering from one of the four RDs, or from unspecific chronic pain. The latter PDs were used as an outgroup in order to test how Pain2D handles more common pain causes. A total of 262 (59 EDS, 29 GBS, 35 FSHD, 89 PROMM, 50 unspecific chronic pain) PDs were collected and used to generate disease specific pain profiles. PDs were then classified by Pain2D in a leave-one-out-cross-validation approach. RESULTS: Pain2D was able to classify the four rare diseases with an accuracy of 61-77% with its binary classifier. EDS, GBS and FSHD were classified correctly by the Pain2D k-disease classifier with sensitivities between 63 and 86% and specificities between 81 and 89%. For PROMM, the k-disease classifier achieved a sensitivity of 51% and specificity of 90%. CONCLUSIONS: Pain2D is a scalable, open-source tool that could potentially be trained for all diseases presenting with pain.
Authors 10
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University of Bonn · University Hospital Bonn
Affiliation as printed
Center for Rare Diseases Bonn (ZSEB), University Hospital Bonn, Bonn, Germany
Institute for Virology, University Hospital Bonn, Bonn, Germany
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Ostfalia University of Applied Sciences · Helmholtz Centre for Infection Research
Affiliation as printed
Biostatistics Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
Institute for Information Engineering, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany
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University of Bonn · University Hospital Bonn
Affiliation as printed
Center for Rare Diseases Bonn (ZSEB), University Hospital Bonn, Bonn, Germany
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University of Bonn · University Hospital Bonn
Affiliation as printed
Center for Rare Diseases Bonn (ZSEB), University Hospital Bonn, Bonn, Germany
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Affiliation as printed
Clinic for Anesthesiology and Operative Intensive Care Medicine, Department of Pain Medicine, University Hospital Bonn, Bonn, Germany
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Affiliation as printed
Clinic for Anesthesiology and Operative Intensive Care Medicine, Department of Pain Medicine, University Hospital Bonn, Bonn, Germany
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Ostfalia University of Applied Sciences · Helmholtz Centre for Infection Research
Affiliation as printed
Biostatistics Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
Institute for Information Engineering, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany
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Rupert Conrad corresponding
University of Münster · University Hospital Münster
Affiliation as printed
Department of Psychosomatic Medicine and Psychotherapy, University Hospital Muenster, Muenster, Germany. rupert.conrad@ukmuenster.de
Department of Psychosomatic Medicine and Psychotherapy, University Hospital Muenster, Muenster, Germany
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Martin Mücke corresponding Aachen Center for Rare Diseases Aachen (ZSEA) Institute for Digitalization and General Medicine
Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Center for Rare Diseases Aachen (ZSEA), University Hospital RWTH Aachen, Aachen, Germany. mamuecke@ukaachen.de
Institute for Digitalization and General Medicine, University Hospital RWTH Aachen, Aachen, Germany. mamuecke@ukaachen.de
Institute for Digitalization and General Medicine, University Hospital RWTH Aachen, Aachen, Germany
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Julia Sellin corresponding Aachen Center for Rare Diseases Aachen (ZSEA) Institute for Digitalization and General Medicine
Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Center for Rare Diseases Aachen (ZSEA), University Hospital RWTH Aachen, Aachen, Germany. jsellin@ukaachen.de
Institute for Digitalization and General Medicine, University Hospital RWTH Aachen, Aachen, Germany. jsellin@ukaachen.de
Institute for Digitalization and General Medicine, University Hospital RWTH Aachen, Aachen, Germany
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