Background: The development of artificial intelligence (AI) in pathology frequently relies on digitally annotated whole slide images (WSI). The creation of these annotations – manually drawn by pathologists in digital slide viewers – is time consuming and expensive. At the same time, pathologists routinely annotate glass slides with a pen to outline cancerous regions, for example, for molecular assessment of the tissue. These pen annotations are currently considered artifacts and excluded from computational modeling. Methods: We propose a novel method to segment and fill hand-drawn pen annotations and convert them into a digital format to make them accessible for computational models. Our method is implemented in Python as an open source, publicly available software tool. Results: Our method is able to extract pen annotations from WSI and save them as annotation masks. On a data set of 319 WSI with pen markers, we validate our algorithm segmenting the annotations with an overall Dice metric of 0.942, Precision of 0.955, and Recall of 0.943. Processing all images takes 15 min in contrast to 5 h manual digital annotation time. Further, the approach is robust against text annotations. Conclusions: We envision that our method can take advantage of already pen-annotated slides in scenarios in which the annotations would be helpful for training computational models. We conclude that, considering the large archives of many pathology departments that are currently being digitized, our method will help to collect large numbers of training samples from those data.

Background: Recently, research data are increasingly shared through social media and other digital platforms. Traditionally, the influence of a scientific article has been assessed by the publishing journal's impact factor (IF) and its citation count. The Altmetric scoring system, a new bibliometric that integrates research “mentions” over digital media platforms, has emerged as a metric of online research distribution. The aim of this study was to explore the relationship of the Altmetric Score with IF and citation number within the pathology literature. Methods: Citation count and Altmetric scores were obtained from the top 10 most-cited articles from the 15 pathology journals with the highest IF for 2013 and 2016. These variables were analyzed and correlated with each other, as well as the age of the publishing journal's Twitter account. Results: Three hundred articles were examined from the two cohorts. The total citation count of the articles decreased from 21,043 (2013) to 14,679 (2016), while the total Altmetric score increased from 830 (2013) to 4066 (2016). In 2013, Altmetric score weakly correlated with citation number (r = 0.284, P < 0.001) but not with journal IF (r = 0.024, P = 0.771). In 2016, there was strong correlation between citation count and Altmetric Score (r = 0.714, P < 0.0001) but not the IF (r = 0.0442, P = 0.591). Twitter was the single most important contributor to the Altmetric score; however, the age of the Twitter account was not associated with citation number nor Altmetric score. Conclusions: In the pathology literature studied, the Altmetric score correlates with article citation count, suggesting that the Altmetric score and conventional bibliometrics can be treated as complementary metrics. Given the trend towards increasing use of social media, additional investigation is warranted to evaluate the evolving role of social media metrics to assess the dissemination and impact of scientific findings in the field of pathology.

Introduction: Hologic is developing a digital cytology platform. An educational website was launched for users to review these digitized Pap test cases. The aim of this study was to analyze data captured from this website. Materials and Methods: ThinPrep^® Pap test slides were scanned at ×40 using a volumetric (14 focal plane) technique. Website cases consisted of an image gallery and whole slide image (WSI). Over a 13 month period data were recorded including diagnoses, time participants spent online, and number of clicks on the gallery and WSI. Results: 51,289 cases were reviewed by 918 reviewers. Cytotechnologists spent less time (M [Median] = 65.0 s) than pathologists (M = 82.2 s) reviewing cases (P < 0.001). Longer times were associated with incorrect diagnoses and cases with organisms. Cytotechnologists matched the reference diagnoses in 85% of cases compared to pathologists who matched in 79.8%. While in 62% of cases reviewers only examined the gallery, they attained the correct diagnosis 92.7% of the time. Pathologists made more clicks on the gallery and WSI than cytotechnologists (P < 0.001). Diagnostic accuracy decreased with increasing clicks. Conclusions: Website participation provided feedback about how cytologists interact with a digital platform when reviewing cases. These data suggest that digital Pap test review when comprised of an image gallery displaying diagnostically relevant objects is quick and easy to interpret. The high diagnostic concordance of digital Pap tests with reference diagnoses can be attributed to high image quality with volumetric scanning, image gallery format, and ability for users to freely navigate the entire digital slide.