Achalasia is an esophageal motility disorder that is commonly misdiagnosed initially as gastroesophageal reflux disease. Patients with achalasia often complain of dysphagia with solids and liquids but may focus on regurgitation as the primary symptom, leading to initial misdiagnosis. Diagnostic tests for achalasia include esophageal motility testing, esophagogastroduodenoscopy and barium swallow. These tests play a complimentary role in establishing the diagnosis of suspected achalasia. High-resolution manometry has now identified three subtypes of achalasia, with therapeutic implications. Pneumatic dilation and surgical myotomy are the only definitive treatment options for patients with achalasia who can undergo surgery. Botulinum toxin injection into the lower esophageal sphincter should be reserved for those who cannot undergo definitive therapy. Close follow-up is paramount because many patients will have a recurrence of symptoms and require repeat treatment.
Botulinum Toxins/administration & dosage ; Deglutition Disorders/etiology ; Diagnostic Errors ; Endoscopy, Digestive System ; Esophageal Achalasia/*diagnosis/etiology/physiopathology/therapy ; Esophageal Sphincter, Lower ; Esophagus/physiopathology/surgery ; Gastroesophageal Reflux/diagnosis ; Humans ; Injections, Subcutaneous ; Manometry ; Neurotransmitter Agents/administration & dosage ; Recurrence

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.