Temporal bone CT-based deep learning models for differential diagnosis of primary ciliary dyskinesia related otitis media and simple otitis media with effusion.

OBJECTIVE

To investigate the diagnostic value of deep learning (DL) in differentiating otitis media (OM) caused by otitis media with effusion (OME) and primary ciliary dyskinesia (PCD), so as to provide reference for early intervention.

METHODS

From January 2010 to January 2021, 31 patients with PCD who had temporal bone computed tomography (TBCT) in the Children's Hospital of Fudan University were retrospectively analyzed. Another 30 age-matched cases of OME with TBCT were collected as the control group. The CT imaging signatures of children were observed. Besides, a variety of DL neural network training models were established based on PyTorch, and the optimal models were trained and selected for PCD screening.

RESULTS

The google net-trained model worked best, with an accuracy of 0.99. Vgg16_bn, vgg19_bn, resnet18, and resnet34; having neural networks with fewer layers, better model effects, with an accuracy rate of 0.86, 0.9, 0.86, and 0.86, respectively. Resnet50 and other neural networks with more layers had relatively poor results.

CONCLUSION

DL-based CT radiomics can accurately distinguish OM caused by OME from that induced by PCD, which can be used for screening the PCD.