Background: Transition to digital pathology usually takes months or years to be completed. We were familiarizing ourselves with digital pathology solutions at the time when the COVID-19 outbreak forced us to embark on an abrupt transition to digital pathology. Objective: The aim of this study was to quantitatively describe how the abrupt transition to digital pathology might affect the quality of diagnoses, model possible causes by probabilistic modeling, and qualitatively gauge the perception of this abrupt transition. Methods: A total of 17 pathologists and residents participated in this study; these participants reviewed 25 additional test cases from the archives and completed a final psychologic survey. For each case, participants performed several different diagnostic tasks, and their results were recorded and compared with the original diagnoses performed using the gold standard method (ie, conventional microscopy). We performed Bayesian data analysis with probabilistic modeling. Results: The overall analysis, comprising 1345 different items, resulted in a 9% (117/1345) error rate in using digital slides. The task of differentiating a neoplastic process from a nonneoplastic one accounted for an error rate of 10.7% (42/392), whereas the distinction of a malignant process from a benign one accounted for an error rate of 4.2% (11/258). Apart from residents, senior pathologists generated most discrepancies (7.9%, 13/164). Our model showed that these differences among career levels persisted even after adjusting for other factors. Conclusions: Our findings are in line with previous findings, emphasizing that the duration of transition (ie, lengthy or abrupt) might not influence the diagnostic performance. Moreover, our findings highlight that senior pathologists may be limited by a digital gap, which may negatively affect their performance with digital pathology. These results can guide the process of digital transition in the field of pathology.

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中文翻译：

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意大利COVID-19爆发期间的数字病理学：调查研究

背景：向数字病理学的转变通常需要数月或数年才能完成。当COVID-19爆发迫使我们着手向数字病理学的突然过渡时，我们已经熟悉了数字病理学解决方案。目的：本研究的目的是定量描述突然向数字病理学过渡如何影响诊断质量，通过概率建模对可能的原因进行建模，并定性评估这种突然过渡的感知。方法：共有17位病理学家和住院医师参加了这项研究。这些参与者检查了档案中的另外25个测试案例，并完成了最终的心理调查。对于每种情况，参与者都执行了几种不同的诊断任务，记录其结果，并与使用金标准方法（即常规显微镜）进行的原始诊断进行比较。我们使用概率模型进行了贝叶斯数据分析。结果：包括1345个不同项目的整体分析导致使用数字幻灯片时9％（117/1345）的错误率。将肿瘤过程与非肿瘤过程相区别的任务导致了10.7％（42/392）的错误率，而将恶性过程与良性过程相区别导致了4.2％（11/258）的错误率。除居民外，高级病理学家之间的差异最大（7.9％，13/164）。我们的模型表明，即使在调整了其他因素之后，职业水平之间的这些差异仍然存在。结论：我们的发现与以前的发现一致，强调过渡的持续时间（即长或突然）可能不会影响诊断性能。此外，我们的研究结果表明，高级病理学家可能会受到数字鸿沟的限制，这可能会对他们在数字病理领域的表现产生负面影响。这些结果可以指导病理学领域的数字转换过程。

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