Digital Pathology During the COVID-19 Outbreak in Italy: Survey Study

Abstract

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.

Keywords: Bayesian data analysis; COVID19; digital pathology; probabilistic modeling.

Figure 1

Error rates among different categories. This dot-bar plot depicts the median (IQR) values of error rates among different categories. The error rates showed the widest IQR among individual pathologists (PID), whereas the least IQR was noted for the career level and the specimen type (biopsy vs surgical).

Figure 2

Raw proportion of errors across (A) career levels and (B) specimen types in performing two important tasks: differentiation between neoplastic and nonneoplastic processes and between malignant and benign tumors.

Figure 3

Prediction of average pathologist performance. Pathologists of intermediate levels of career perform better on average. The graph depicts the posterior predictive distributions for the multilevel model. Solid lines represent posterior mean values; shaded regions represent 89% high-posterior density interval; and dashed lines represent raw data.

Figure 4

Overview of the psychological aspect of the study. This series of graphs summarize the results of the survey conducted among pathologists at different career levels (residents, junior, expert, and senior) to evaluate their attitudes toward, confidence in, and satisfaction with digital pathology solutions.