A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs.

Purpose: The aim of this study was to assess the performance of a deep learning system for permanent tooth germ detection on pediatric panoramic radiographs. Materials and Methods: In total, 4518 anonymized panoramic radiographs of children between 5 and 13 years of age were collected. YOLOv4, a convolutional neural network (CNN)-based object detection model, was used to automatically detect permanent tooth germs. Panoramic images of children processed in LabelImg were trained and tested in the YOLOv4 algorithm. True-positive, false-positive, and false-negative rates were calculated. A confusion matrix was used to evaluate the performance of the model. Results: The YOLOv4 model, which detected permanent tooth germs on pediatric panoramic radiographs, provided an average precision value of 94.16% and an F1 value of 0.90, indicating a high level of significance. The average YOLOv4 inference time was 90 ms. Conclusion: The detection of permanent tooth germs on pediatric panoramic X-rays using a deep learning-based approach may facilitate the early diagnosis of tooth deficiency or supernumerary teeth and help dental practitioners find more accurate treatment options while saving time and effort.

Elevated serum midkine levels in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected patients.

BACKGROUND: The coronavirus disease-2019 (COVID-19) pandemic has caused important health, economic, social, and cultural problems worldwide. Recent findings demonstrate an excessive cytokine release during the disease development, especially in the seriously life-threatening form of COVID-19. Among other chemokines and cytokines that are released in high amounts at the infection site of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), midkine (MK), which is a potent pro-inflammatory growth factor/ cytokine, can be also overexpressed and contribute to the pathophysiological process in patients infected with SARS-CoV-2. MATERIALS AND METHOD: Serum was collected from 87 intensive care unit (ICU) patients that are COVID-19 positive and 50 healthy volunteers in the control group with a negative PCR test and without disease symptoms. Circulating MK concentration was measured by enzyme-linked immunosorbent assay (ELISA). RESULTS: COVID-19 patients had a significantly higher serum MK concentration compared to non-COVID-19 control subjects (1892.8 ± 1615.8 pg/mL versus 680.7 ± 907.6 pg/mL, respectively; P < 0.001). The cut-off MK concentration was 716.7 pg/ mL, with the sensitivity and specificity of 75.9 % and 76.0 %, respectively. The area under the receiver operating characteristic (ROC) curve of MK was = 0.827. Our findings showed that circulating MK levels are significantly increased in SARS-CoV-2 infected patients. CONCLUSION: We suggest that MK is involved in the pathogenesis of COVID-19 and may be a part of hypercytokinaemia. Therefore, MK may serve as a supporting biomarker in the diagnosis of COVID-19, and blocking MK actions or its targets may attenuate the inflammatory process and the severity of the disease.