Introduction: In this study, we report on our experience using digital pathology to overcome the severe limitations imposed on health care by the Covid-19 outbreak in Northern Italy. Social distancing had a major impact on public transportation, causing it to run with reduced timetables. This resulted in a major challenge for hospital commuters. To limit the presence in our hospital of no more than two pathologists at a time out of four, a web-based digital pathology system (DPS) was employed to work remotely. Subjects and Methods: We used a DPS in which a scanner, a laboratory information system, a storage device, and a web server were interfaced so that tissue slides could be viewed over the Internet by whole-slide imaging (WSI). After a brief internal verification test, the activity on the DPS was recorded, taking track of a set of performance and efficiency indicators. At the end of the study, 405 cases were signed out remotely. Results: Of 693 cases, 58.4% were signed out remotely by WSI, while 8.4% needed to be kept on hold to return to the original microscope slide. In three cases, at least one slide had to be rescanned. In eight cases, one slide was recut. Panel discussion by WSI was necessary in 34 cases, a condition in which all pathologists were asked for their opinion. A consultation with a more experienced colleague was necessary in 17 cases. Conclusions: We show that WSI easily allows pathologists to overcome the problems caused by the severe social distancing measures imposed by the Covid-19 pandemic. Our experience shows that soon there will not be alternatives to digital pathology, given that there is no assurance that other similar outbreaks will not occur.
Summary Objective A common source of concern about digital pathology (DP) is that limited resolution could be a reason for an increased risk of malpractice. A frequent question being raised about this technology is whether it can be used to reliably detect Helicobacter pylori (HP) in gastric biopsies, which can be a significant burden in routine work. The main goal of this work is to show that a reliable diagnosis of HP infection can be made by DP even at low magnification. The secondary goal is to demonstrate that artificial intelligence (AI) algorithms can diagnose HP infections on virtual slides with sufficient accuracy. Methods The method we propose is based on the Warthin-Starry (W-S) silver stain which allows faster detection of HP in virtual slides. A software tool, based on regular expressions, performed a specific search to select 679 biopsies on which a W-S stain was done. From this dataset 185 virtual slides were selected to be assessed by WSI and compared with microscopy slide readings. To determine whether HP infections could be accurately diagnosed with machine learning. AI was used as a service (AIaaS) on a neural network-based web platform trained with 468 images. A test dataset of 210 images was used to assess the classifier performance. Results In 185 gastric biopsies read with DP we recorded only 4 false positives and 4 false negatives with an overall agreement of 95.6%. Compared with microscopy, defined as the “gold standard” for the diagnosis of HP infections, WSI had a sensitivity and specificity of 0.95 and 0.96, respectively. The ROC curve of our AI classifier generated on a testing dataset of 210 images had an AUC of 0.938. Conclusions This study demonstrates that DP and AI can be used to reliably identify HP at 20X resolution.
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