Background The diagnosis of rare diseases poses a particular challenge to clinicians. This study analyzes whether patients’ pain drawings (PDs) help in the differentiation of two pain-associated rare diseases, Ehlers-Danlos Syndrome (EDS) and Guillain-Barré Syndrome (GBS). Method The study was designed as a prospective, observational, single-center study. The sample comprised 60 patients with EDS (3 male, 52 female, 5 without gender information; 39.2 ± 11.4 years) and 32 patients with GBS (10 male, 20 female, 2 without gender information; 50.5 ± 13.7 years). Patients marked areas afflicted by pain on a sketch of a human body with anterior, posterior, and lateral views. PDs were electronically scanned and processed. Each PD was classified based on the Ružička similarity to the EDS and the GBS averaged image (pain profile) in a leave-one-out cross validation approach. A receiver operating characteristic (ROC) curve was plotted. Results 60–80% of EDS patients marked the vertebral column with the neck and the tailbone and the knee joints as pain areas, 40–50% the shoulder-region, the elbows and the thumb saddle joint. 60–70% of GBS patients marked the dorsal and plantar side of the feet as pain areas, 40–50% the palmar side of the fingertips, the dorsal side of the left palm and the tailbone. 86% of the EDS patients and 96% of the GBS patients were correctly identified by computing the Ružička similarity. The ROC curve yielded an excellent area under the curve value of 0.95. Conclusion PDs are a useful and economic tool to differentiate between GBS and EDS. Further studies should investigate its usefulness in the diagnosis of other pain-associated rare diseases. This study was registered in the German Clinical Trials Register, No. DRKS00014777 (Deutsches Register klinischer Studien, DRKS), on 01.06.2018.

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Purpose The recent COVID-19 pandemic has resulted in an increasing overload of the medical system. Healthcare workers (HCW) in radiology departments are exposed to a high infection risk similar to HCWs in the ICU or dedicated COVID wards. The goal of our paper is to evaluate the prevalence of IgG antibody against SARS-CoV-2 among radiology HCWs in two different hospitals and regions in Germany with a low and high COVID-19 prevalence and to compare it to the prevalence in other clinical personnel. Additionally, we assessed the number of radiological procedures performed in patients with a positive PCR test (C+) followed by a short review of the risk for nosocomial infections of radiology HCWs. Materials and Methods During the first COVID-19 wave between March and July 2020, we evaluated a region with one of the highest COVID-19 rates (776–1570/100 000) in Germany (Hospital A). Additionally, we assessed Hospital B in a region with a low prevalence (65/100 000). We tested the serum prevalence of SARS-CoV-2 IgG antibodies among the whole staff with a subgroup analysis for radiology in both hospitals. We calculated the total number of different radiological procedures performed in C+ patients. Results In Hospital A 594 PCR-proven C+ patients were treated resulting in 2723 radiological procedures. 24 % (n = 6) of the radiology technicians and 13.35 (n = 2) of radiologists had a positive IgG test. The rates were similar to positive rates in HCWs in COVID-19 wards and ICUs within the hospital. The most frequently performed procedures in C+ patients were chest X-rays (3.17/patient) and CT examinations (1.15/patient). In Hospital B 50 C+ patients were treated, resulting in 64 radiological procedures. None of the HCWs tested IgG positive. The most frequently performed examinations were also chest X-rays (1.04/patient) and CT (0.2/patient). Conclusion HCWs in radiology have a high occupational infection risk similar to that of HCWs in ICUs and dedicated COVID wards. Key Points:  Citation Format

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.

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