Accuracy of one-step automated orthodontic diagnosis model using a convolutional neural network and lateral cephalogram images with different qualities obtained from nationwide multi-hospitals / 대한치과교정학회지

Objective@#The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals. @*Methods@#Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradientweighted class activation mapping (Grad-CAM). @*Results@#In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis. @*Conclusions@#Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.

Accuracy of artificial intelligence-assisted landmark identification in serial lateral cephalograms of Class III patients who underwent orthodontic treatment and two-jaw orthognathic surgery / 대한치과교정학회지

Objective@#To investigate the pattern of accuracy change in artificial intelligence-assisted landmark identification (LI) using a convolutional neural network (CNN) algorithm in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent twojaw orthognathic surgery. @*Methods@#A total of 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n = 23 per group) for LI. Each C-III patient in the test set had four Lat-cephs: initial (T0), pre-surgery (T1, presence of orthodontic brackets [OBs]), post-surgery (T2, presence of OBs and surgical plates and screws [S-PS]), and debonding (T3, presence of S-PS and fixed retainers [FR]). After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed. @*Results@#The total mean error was 1.17 mm without significant difference among the four timepoints (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points ([T0, T1] vs. [T2, T3]), ANS, A point, and B point showed an increase in error (p < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showeda decrease in error (all p < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups. @*Conclusions@#The CNN model can be used for LI in serial Lat-cephs despite the presence of OB, S-PS, FR, genioplasty, and bone remodeling.

Spinal Accessory Neuropathy Associated With the Tumor Located on the Jugular Foramen

Spinal accessory neuropathy is commonly caused by iatrogenic injury or secondary to trauma or infection. Nevertheless, the tumor related palsy is rare. We present a case of an 18-year-old male patient suffering from paralysis of his right trapezius and sternocleidomastoid muscle. An electrophysiologic diagnostic study confirmed the spinal accessory neuropathy of the proximal segment. In addition, magnetic resonance imaging showed the location of tumor on the jugular foramen. However, the type of the tumor was not confirmed through biopsy because the patient refused surgical procedure. Based on the study, it is hypothesized that the tumor located on the jugular foramen should be considered as a cause of the spinal accessory nerve of the proximal segment.

Ischiofemoral Impingement Syndrome

Ischiofemoral impingement syndrome is known as one of the causes of hip pain due to impingement of ischium and femur, and usually correlated with trauma or operation. We report a rare case of ischiofemoral impingement syndrome that has no history of trauma or surgery. A 48-year-old female patient was referred for 2 months history of the left hip pain, radiating to lower extremity with a hip snapping sensation. She had no history of trauma or surgery at or around the hip joint and femur. The magnetic resonance imaging (MRI) of the lumbar spine showed no abnormality, except diffuse bulging disc without cord compression at the lumbosacral area. Electrophysiologic study was normal, and there were no neurologic abnormalities compatible with the lumbosacral radiculopathy or spinal stenosis. Hip MRI revealed quadratus femoris muscle edema with concurrent narrowing of the ischiofemoral space. The distance of ischiofemoral space and quadratus femoris space were narrow. It was compatible with ischiofemoral impingement syndrome. After treatment with nonsteroidal anti-inflammatory drugs, physical therapy, and exercise program, the patient's pain was relieved and the snapping was improved. To our knowledge, this is the first reported case of a nontraumatic, noniatrogenic ischiofemoral impingement syndrome, and also the first case to be treated by a nonsurgical method in the Republic of Korea.