Analysis of Missed Diagnosis of Gastric Lipomas by CT

Evaluation of children's oral diagnosis and treatment using imaging examination using AI based Internet of Things

BACKGROUND

Although cone beam computed tomography (CBCT) plays an important role in the diagnosis and treatment of oral diseases, its image segmentation method needs to be further improved, and there are still objections about the clinical application effect of general anesthesia (GA) on children's dental fear (CDF).

OBJECTIVE

This study aimed to investigate the application value of CBCT based on intelligent computer segmentation model in oral diagnosis and treatment of children in the context of biomedical signals, and to analyze the alleviating effect of GA on CDF.

METHODS

Based on the regional level set (CV) algorithm, the local binary fitting (LBF) model was introduced to optimize it, and the tooth CBCT image segmentation model CV-LBF was established to compare the segmentation accuracy (SA), maximum symmetric surface distance (MSSD), average symmetric surface distance (ASSD), over segmentation rate (OR), and under segmentation rate (UR) between these model and other algorithms. 82 children with CDF were divided into general anesthesia group (GAG) (n= 38) and controls (n= 44) according to the voluntary principle of their families. Children in GAG were treated with GA and controls with protective fixed intervention. Children's fear survey schedule-dental subscale (CFSS-DS) and Venham scores were counted before intervention in the two groups. CFSS-DS scores were recorded at 2 hours after intervention and after recovery in children in GAG. CFSS-DS and Venham scores were performed in all children 1 week after surgery.

RESULTS

The results showed that the S⁢A value of CV-LBF algorithm was higher than that of region growing algorithm (P< 0.05). OR, UR, MSSD, and ASSD values of CV-LBF algorithm were evidently lower than those of other algorithms (P< 0.05). CFSS-DS scores were lower in GAG than in controls 2 hours after intervention and at return visits after 1 week of intervention (P< 0.001), and Venham scores were lower in GAG than in controls after intervention (P< 0.001). After intervention, the proportion of children with Venham grade 0, 1, 2, and 3 was obviously higher in GAG than in controls (P< 0.001), while the proportion of children with Venham grade 4 and 5 was clearly higher in controls than in GAG (P< 0.001).

CONCLUSION

The results revealed that the computer intelligent segmentation model CV-LBF has potential application value in CBCT image segmentation of children's teeth, and GA can effectively alleviate anxiety of children with CDF and can be used as biomedical signals.

Diagnostic Value of CT Window Technique for Primary Omentum Infarction

Objective

The diagnostic value of CT window width technique in primary omentum infarction was evaluated by this study.

Methods

The abdominal CT data of 32 patients with clinically diagnosed abdominal omentum infarction were retrospectively selected and analyzed. The fixed window position was 50 HU, and the window width was 135 HU, 250 HU (abdomen), 350 HU (mediastinum), and 500 HU, respectively. The detection rate of lesions was analyzed and compared.

Results

Window widths of 135 HU, 250 HU (abdomen), 350 HU (mediastinum), and 500 HU have a detection rate of 12.5% (4 cases), 62.5% (20 cases), 100% (32 cases), 100% (32 cases) for abdominal omental lesions, respectively. However, 500 HU showed worse abdominal bowel and parenchymal organs than 350 HU.

Conclusion

According to the comprehensive image quality, the ideal window width for diagnosis of primary omentum infarction is 350HU (mediastinal) window width.