Publications by authors named "Shinji Nakazawa"
Article Synopsis
- Researchers developed AI software to automatically measure white matter hyperintensities (WMHs) in head MRIs using thick-slice FLAIR sequences from over 1000 participants in Japan.
- They trained and tested their WMH segmentation model on annotated MRI images, achieving a Dice similarity coefficient (DSC) of 0.820, nearly matching human accuracy.
- The study suggests this model could be useful in clinical settings despite some limitations, with slightly better performance when additional thin-slice data was included.
Purpose: To create and evaluate a machine-learning model for YOLOv3 that can simultaneously perform morphological evaluation and tracking in a short time, which can be adapted to video data under an inverted microscope.
Methods: Japanese patients who underwent intracytoplasmic sperm injection at the Jikei University School of Medicine and Keiai Reproductive and Endosurgical Clinic from January 2019 to March 2020 were included. An AI model that simultaneously performs morphological assessment and tracking was created and its performance was evaluated.