Introduction We describe the use of telepathology in countries with restricted resources using two diagnosis assistance systems (Isabel and Memem7) in addition to the diagnoses made by experts in pathology via the iPath-Network. Methods A total of 156 cases, largely from Afghanistan, were analysed; 18 cases had to be excluded because of poor image quality. Results Of the remaining 138 cases (100%), a responsible physician provided a tentative diagnosis for 61.6% of them. With a diagnosis from a consultant pathologist, it was then possible to make a definite diagnosis in 84.8% of cases on the basis of images taken from hematoxylin and eosin staining sections alone. The use of the diagnosis assistance systems resulted in an ordered list of differential diagnoses in 82.6% (IsabelHealth) and in 74.6% (Memem7) of cases, respectively. Adding morphological terminology reduced the list of possible diagnoses to 52.2% (72 cases, Memem7), but improved their quality. Discussion In summary, diagnosis assistance systems are promising approaches to provide physicians in countries with restricted resources with lists of probable differential diagnoses, thus increasing the plausibility of the diagnosis of the consultant pathologist.

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Background Medical decision support systems (CDSSs) are increasingly used in medicine, but their utility in daily medical practice is difficult to evaluate. One variant of CDSS is a generator of differential diagnoses (DDx generator). We performed a feasibility study on three different, publicly available data sets of medical cases in order to identify the frequency in which two different DDx generators provide helpful information (either by providing a list of differential diagnosis or recognizing the expert diagnosis if available) for a given case report. Methods Used data sets were n = 105 cases from a web-based forum of telemedicine with real life cases from Afghanistan (Afghan data set; AD), n = 124 cases discussed in a web-based medical forum (Coliquio data set; CD). Both websites are restricted for medical professionals only. The third data set consisted 50 special case reports published in the New England Journal of Medicine (NEJM). After keyword extraction, data were entered into two different DDx generators (IsabelHealth (IH), Memem7 (M7)) to examine differences in target diagnosis recognition and physician-rated usefulness between DDx generators. Results Both DDx generators detected the target diagnosis equally successfully (all cases: M7, 83/170 (49%); IH 90/170 (53%), NEJM: M7, 28/50 (56%); IH, 34/50 (68%); differences n.s.). Differences occurred in AD, where detection of an expert diagnosis was less successful with IH than with M7 (29.7% vs. 54.1%, p = 0.003). In contrast, in CD IH performed significantly better than M7 (73.9% vs. 32.6%, p = 0.021). Congruent identification of target diagnosis occurred in only 46/170 (27.1%) of cases. However, a qualitative analysis of the DDx results revealed useful complements from using the two systems in parallel. Conclusion Both DDx systems IsabelHealth and Memem7 provided substantial help in finding a helpful list of differential diagnoses or identifying the target diagnosis either in standard cases or complicated and rare cases. Our pilot study highlights the need for different levels of complexity and types of real-world medical test cases, as there are significant differences between DDx generators away from traditional case reports. Combining different results from DDx generators seems to be a possible approach for future review and use of the systems.

Background. This study was performed in knowledge of the increasing gap between breast disease treatment in countries with restricted resources and developed countries with increasingly sophisticated examination methods. Methods. The authors present the analysis of a breast disease register consisting of diagnostic cases from Mazar e Sharif and Herat in 2018 and 2019. The study comprises a total of 567 cases, which were presented to experts via telemedicine for final diagnosis. 62 cases (10.9%) were excluded due to inacceptable data or insufficient image quality. These data provided by daily diagnostic classification were used for the built-up of a profile for each frequent breast disease and a breast cancer register. All images and cases were seen by at least 3 independent experts. The diagnoses were made in 60% of cases by cytology of fine needle aspiration and in 40% by histological images. Results. For each entity of breast diseases (e.g., fibroadenoma), a profile of context variables was constructed allowing to assist medical decisions, as “wait and see,” elective surgery or immediate surgical intervention with R0 (complete) resection. These “profiles” could be described for fibroadenoma, mastitis, galactocele, fibrous-cystic disease, and invasive breast cancer. Conclusions. The presented preliminary data set could serve as a cost-effective basis for a North Afghan breast cancer registry, with option to extent to a national model. These preliminary data are transformed in profiles of breast diseases, which are used by the local physicians in charge of breast disease patients. Each new case can be compared by the local treating physician with the profile of all preceded cases with the same diagnosis.

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