Automated fingerprint analysis as a diagnostic tool for the genetic disorder Kabuki syndrome.

PURPOSE

Emerging therapeutic strategies for Kabuki syndrome (KS) make early diagnosis critical. Fingerprint analysis as a diagnostic aid for KS diagnosis could facilitate early diagnosis and expand the current patient base for clinical trials and natural history studies.

METHOD

Fingerprints of 74 individuals with KS, 1 individual with a KS-like phenotype, and 108 controls were collected through a mobile app. KS fingerprint patterns were studied using logistic regression and a convolutional neural network to differentiate KS individuals from controls.

RESULTS

Our analysis identified 2 novel KS metrics (folding finger ridge count and simple pattern), which significantly differentiated KS fingerprints from controls, producing an area under the receiver operating characteristic curve value of 0.82 [0.75; 0.89] and a likelihood ratio of 9.0. This metric showed a sensitivity of 35.6% [23.73%; 47.46%] and a specificity of 96.04% [92.08%; 99.01%]. An independent artificial intelligence convolutional neural network classification-based method validated this finding and yielded comparable results, with a likelihood ratio of 8.7, sensitivity of 76.6%, and specificity of 91.2%.

CONCLUSION

Our findings suggest that automatic fingerprint analysis can have diagnostic use for KS and possible future utility for diagnosing other genetic disorders, enabling greater access to genetic diagnosis in areas with limited availability of genetic testing.