Differentiation between normal and abnormal kidneys using ^99mTc-DMSA SPECT with deep learning in paediatric patients

To investigate the feasibility of using deep learning (DL) to differentiate normal from abnormal (or scarred) kidneys using technetium-99m dimercaptosuccinic acid (^99mTc-DMSA) single-photon-emission computed tomography (SPECT) in paediatric patients.

Three hundred and one ^99mTc-DMSA renal SPECT examinations were reviewed retrospectively. The 301 patients were split randomly into 261, 20, and 20 for training, validation, and testing data, respectively. The DL model was trained using three-dimensional (3D) SPECT images, two-dimensional (2D) maximum intensity projections (MIPs), and 2.5-dimensional (2.5D) MIPs (i.e., transverse, sagittal, and coronal views). Each DL model was trained to determine renal SPECT images into either normal or abnormal. Consensus reading results by two nuclear medicine physicians served as the reference standard.

The DL model trained by 2.5D MIPs outperformed that trained by either 3D SPECT images or 2D MIPs. The accuracy, sensitivity, and specificity of the 2.5D model for the differentiation between normal and abnormal kidneys were 92.5%, 90% and 95%, respectively.

The experimental results suggest that DL has the potential to differentiate normal from abnormal kidneys in children using ^99mTc-DMSA SPECT imaging.