Detection of the pathological exposure of pulp using an artificial intelligence tool: a multicentric study over periapical radiographs.

BACKGROUND: Introducing artificial intelligence (AI) into the medical field proved beneficial in automating tasks and streamlining the practitioners' lives. Hence, this study was conducted to design and evaluate an AI tool called Make Sure Caries Detector and Classifier (MSc) for detecting pathological exposure of pulp on digital periapical radiographs and to compare its performance with dentists. METHODS: This study was a diagnostic, multi-centric study, with 3461 digital periapical radiographs from three countries and seven centers. MSc was built using Yolov5-x model, and it was used for exposed and unexposed pulp detection. The dataset was split into a train, validate, and test dataset; the ratio was 8-1-1 to prevent overfitting. 345 images with 752 labels were randomly allocated to test MSc. The performance metrics used to test MSc performance included mean average precision (mAP), precision, F1 score, recall, and area under receiver operating characteristic curve (AUC). The metrics used to compare the performance with that of 10 certified dentists were: right diagnosis exposed (RDE), right diagnosis not exposed (RDNE), false diagnosis exposed (FDE), false diagnosis not exposed (FDNE), missed diagnosis (MD), and over diagnosis (OD). RESULTS: MSc achieved a performance of more than 90% in all metrics examined: an average precision of 0.928, recall of 0.918, F1-score of 0.922, and AUC of 0.956 (P<.05). The results showed a higher mean of 1.94 for all right (correct) diagnosis parameters in MSc group, while a higher mean of 0.64 for all wrong diagnosis parameters in the dentists group (P<.05). CONCLUSIONS: The designed MSc tool proved itself reliable in the detection and differentiating between exposed and unexposed pulp in the internally validated model. It also showed a better performance for the detection of exposed and unexposed pulp when compared to the 10 dentists' consensus.

Melioidosis: Case reports of confirmed Burkholderia pseudomallei in Saudi Arabia.

Melioidosis is an infectious disease of tropical climates. The disease is caused by the bacterium Burkholderia pseudomallei. Most cases are diagnosed in southeast Asia and northern Australia. Some imported cases diagnosed in returning tourists, soldiers, and immigrants from endemic areas. It caught much attention since the Centers for Disease Control and Prevention (CDC) designated B. pseudomallei as an agent for biological warfare and terrorism. We describe two cases of a 26-year-old Saudi woman who had fulminant sepsis soon after returning from Thailand & a 48-year-old woman with a long history of fever. B. pseudomallei was isolated from both patients blood cultures, and they had different consequences. A confirmed case of melioidosis was not reported before in Saudi Arabia.