Performance analysis of automated evaluation of Crithidia luciliae-based indirect immunofluorescence tests in a routine setting - strengths and weaknesses

Computer-aided classification of indirect immunofluorescence patterns on esophagus and split skin for the detection of autoimmune dermatoses

Autoimmune bullous dermatoses (AIBD) are rare diseases that affect human skin and mucous membranes. Clinically, they are characterized by blister formation and/or erosions. Depending on the structures involved and the depth of blister formation, they are grouped into pemphigus diseases, pemphigoid diseases, and dermatitis herpetiformis. Classification of AIBD into their sub-entities is crucial to guide treatment decisions. One of the most sensitive screening methods for initial differentiation of AIBD is the indirect immunofluorescence (IIF) microscopy on tissue sections of monkey esophagus and primate salt-split skin, which are used to detect disease-specific autoantibodies. Interpretation of IIF patterns requires a detailed examination of the image by trained professionals automating this process is a challenging task with these highly complex tissue substrates, but offers the great advantage of an objective result. Here, we present computer-aided classification of esophagus and salt-split skin IIF images. We show how deep networks can be adapted to the specifics and challenges of IIF image analysis by incorporating segmentation of relevant regions into the prediction process, and demonstrate their high accuracy. Using this semi-automatic extension can reduce the workload of professionals when reading tissue sections in IIF testing. Furthermore, these results on highly complex tissue sections show that further integration of semi-automated workflows into the daily workflow of diagnostic laboratories is promising.

Evaluation of the automated indirect immunofluorescence test for anti-dsDNA antibodies

BACKGROUND

Anti-dsDNA antibody is a specific antibody in systemic lupus erythematosus (SLE). Indirect immunofluorescence test (IIFT) is a highly specific method in detecting anti-dsDNA antibody. The application of automated system has gained better consistency than manual operation. This study detected anti-dsDNA antibodies using EUROPattern Computer-aided immunofluorescence microscopy (EPA), and evaluated the performance of the automated system.

METHODS

The sera of 96 patients with suspected SLE and 102 control patients were examined using IIFT. The consistency between the EPA and manual reading was analyzed.

RESULTS

Analysis of 198 samples showed that the overall consistency of the negative/positive results between the EPA and manual reading was 94.95%. Based on the manual reading results, the sensitivity and specificity of EPA were 95.70% and 94.29%, respectively. The analysis of 57 samples with non-specific fluorescence showed that the overall consistency of the negative/positive results was 96.49%. The analysis of the antibody titer of 89 positive samples showed that the consistency between the EPA and manual reading was 97.75%.

CONCLUSION

EPA was consistent with the manual reading with regard to qualitative reading and antibody titer. With low-exposure function, EPA could read samples with non-specific fluorescence. EPA was superior to manual reading in automation and standardization.

Performance analysis of automated evaluation of antinuclear antibody indirect immunofluorescent tests in a routine setting

Purpose

Indirect immunofluorescence (IIF) on the human epithelial cell-line HEp-2 (or derivatives) serves as the gold standard in antinuclear antibody (ANA) screening. IIF, and its evaluation, is a labor-intensive method, making ANA testing a major challenge for present clinical laboratories. Nowadays, several automated ANA pattern recognition systems are on the market. In the current study, the EUROPattern Suite is evaluated for its use in daily practice in a routine setting.

Methods

A total of 1033 consecutive routine samples was used to screen for ANA. Results (positive/negative ANA screening, pattern identification and titer) were compared between software-generated results (EUROPattern) and visual interpretation (observer) of automatically acquired digital images.

Results

Considering the visual interpretation as reference, a relative sensitivity of 99.3% and a relative specificity of 88.9% were obtained for negative and positive discrimination by the software (EPa). A good agreement between visual and software-based interpretation was observed with respect to pattern recognition (mean kappa: for 7 patterns: 0.7). Interestingly, EPa software distinguished more patterns per positive sample than the observer (on average 1.5 and 1.2, respectively). Finally, a concordance of 99.3% was observed within the range of 1 titer step difference between EPa and observer.

Conclusions

The ANA IIF results reported by the EPa software are in very good agreement with the results reported by the observer with respect to being negative/positive, pattern recognition and titer, making automated ANA IIF evaluation an objective and time-efficient tool for routine testing.

Electronic supplementary material

The online version of this article (10.1007/s13317-018-0108-y) contains supplementary material, which is available to authorized users.