Evaluation of an artificial intelligence system for the diagnosis of apical periodontitis on digital panoramic images.

Aims: The aim of the present study was to evaluate the effectiveness of an artificial intelligence (AI) system in the detection of roots with apical periodontitis (AP) on digital panoramic radiographs. Materials and Methods: Three hundred and six panoramic radiographs containing 400 roots with AP (an equal number for both jaws) were used to test the diagnostic performance of an AI system. Panoramic radiographs of the patients were selected with the terms 'apical lesion' and 'apical periodontitis' from the archive and then with the agreement of two oral and maxillofacial radiologists. The radiologists also carried out the grouping and determination of the lesion borders. A deep learning (DL) model was built and the diagnostic performance of the model was evaluated by using recall, precision, and F measure. Results: The recall, precision, and F-measure scores were 0.98, 0.56, and 0.71, respectively. While the number of roots with AP detected correctly in the mandible was 169 of 200 roots, it was only 56 of 200 roots in the maxilla. Only four roots without AP were incorrectly identified as those with AP. Conclusions: The DL method developed for the automatic detection of AP on digital panoramic radiographs showed high recall, precision, and F measure values for the mandible, but low values for the maxilla, especially for the widened periodontal ligament (PL)/uncertain AP.

Characteristic imaging findings of duodenal perforation.

Two patients were admitted to emergency room with complaint of abdominal pain with abrupt onset and retroperitoneal free air was detected in pararenal area on CT. In this paper, characteristic CT findings of patients diagnosed with duodenal perforation are presented under the light of literature data.