基于疼痛绘图的诊断支持系统：EDS、GBS、FSHD、PROMM 的二进制和 k 疾病分类，以及使用 Pain2D 的对照组。

A diagnostic support system based on pain drawings: binary and k-disease classification of EDS, GBS, FSHD, PROMM, and a control group with Pain2D.

机构信息

Center for Rare Diseases Bonn (ZSEB), University Hospital Bonn, Bonn, Germany.

Institute for Virology, University Hospital Bonn, Bonn, Germany.

出版信息

Orphanet J Rare Dis. 2023 Mar 28;18(1):70. doi: 10.1186/s13023-023-02663-z.

DOI:10.1186/s13023-023-02663-z

PMID:36978184

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10053427/

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.

摘要

背景和目的

由于罕见疾病（RDs）的罕见性、变异性和大量的个体 RD，其诊断常常具有挑战性，导致诊断延迟，对患者和医疗保健系统产生不利影响。开发计算机辅助诊断决策支持系统可以通过支持鉴别诊断并促使医生启动正确的诊断测试来帮助改善这些问题。为此，我们开发、培训和测试了一个机器学习模型，该模型作为名为 Pain2D 的软件的一部分实现，用于对四种罕见疾病（EDS、GBS、FSHD 和 PROMM）以及一组非特异性慢性疼痛进行分类，这些患者通过纸笔填写疼痛图来进行诊断。

方法

从患有四种罕见疾病之一或非特异性慢性疼痛的患者中收集疼痛图（PDs）。后者的 PD 用作外群，以测试 Pain2D 如何处理更常见的疼痛原因。共收集了 262 份 PD（59 份 EDS、29 份 GBS、35 份 FSHD、89 份 PROMM、50 份非特异性慢性疼痛），用于生成疾病特异性疼痛特征。然后通过 Pain2D 在留一法交叉验证方法中对 PD 进行分类。