Deep neural network approaches for detecting gastric polyps in endoscopic images.

Gastrointestinal endoscopy is the primary method used for the diagnosis and treatment of gastric polyps. The early detection and removal of polyps is vitally important in preventing cancer development. Many studies indicate that a high workload can contribute to misdiagnosing gastric polyps, even for experienced physicians. In this study, we aimed to establish a deep learning-based computer-aided diagnosis system for automatic gastric polyp detection. A private gastric polyp dataset was generated for this purpose consisting of 2195 endoscopic images and 3031 polyp labels. Retrospective gastrointestinal endoscopy data from the Karadeniz Technical University, Farabi Hospital, were used in the study. YOLOv4, CenterNet, EfficientNet, Cross Stage ResNext50-SPP, YOLOv3, YOLOv3-SPP, Single Shot Detection, and Faster Regional CNN deep learning models were implemented and assessed to determine the most efficient model for precancerous gastric polyp detection. The dataset was split 70% and 30% for training and testing all the implemented models. YOLOv4 was determined to be the most accurate model, with an 87.95% mean average precision. We also evaluated all the deep learning models using a public gastric polyp dataset as the test data. The results show that YOLOv4 has significant potential applicability in detecting gastric polyps and can be used effectively in gastrointestinal CAD systems. Gastric Polyp Detection Process using Deep Learning with Private Dataset.

The effect of low-level laser therapy on the success rate of inferior alveolar nerve blocks in mandibular molars with symptomatic irreversible pulpitis: A randomized clinical trial.

AIM: To evaluate the effect of low-level laser therapy (LLLT) on the success rate of inferior alveolar nerve blocks (IANB) in mandibular molar teeth with symptomatic irreversible pulpitis (SIP). METHODOLOGY: Eighty-eight patients who were diagnosed with SIP were randomly divided into two groups: the group in which only IANB was applied and the group in which IANB + LLLT was applied. IANB was applied to patients in the control group with 4% articaine. LLLT was applied to the patients in the experimental group in addition to IANB. The pain experienced during the operation was evaluated using a visual analog scale. If the patients reported moderate or severe pain during the treatment, the IANB was defined as unsuccessful. Pearson's chi-square test was used to analyse anaesthetic success rates. RESULTS: Whilst the anaesthesia success rate was 34% in the group where only IANB was applied, it was 57% in the group in which LLLT was applied in addition to IANB. There was a significant difference between the groups (p = .032). CONCLUSIONS: The application of LLLT to support IANB in mandibular molar teeth with SIP increased the success of anaesthesia. However, it was insufficient for a complete pulpal anaesthesia.