Won-Jin Yi

The CNN method we developed for automatically diagnosing odontogenic cysts and tumors of both jaws on panoramic radiographs using data augmentation showed high sensitivity, specificity, accuracy, and AUC despite the limited number of panoramic images involved.

We developed a system to measure ECG without subject's awareness and derived respiration from the ECG using the wavelet transform. The bed sheet electrodes consisted of three pieces of conductive textiles. Respiratory signals (EDR) were derived by reconstructing the detail signal of the 9th decomposition from the wavelet transform. The respiration periods were calculated by detecting the zero-crossing of the respiratory signals. Correlations between series of respiration periods extracted from E

Several methods enabling independent repositioning of the maxilla have been introduced to reduce intraoperative errors inherent in the intermediate splint. However, the accuracy is still to be improved and a different approach without time-consuming laboratory process is needed, which can allow perioperative modification of unoptimized maxillary position. The purpose of this study is to assess the feasibility and accuracy of a robot arm combined with intraoperative image-guided navigation in ort

Cone-beam CT (CBCT) is widely used in dental clinics but exhibits limitations in assessing soft tissue pathology because of its lack of contrast resolution and low Hounsfield Units (HU) quantification accuracy. We aimed to increase the image quality and HU accuracy of CBCTs while preserving anatomical structures. We generated CT-like images from CBCT images using a patchwise contrastive learning-based GAN model. Our model was trained on unpaired CT and CBCT datasets with the novel combination of