Opportunistic use of chest low-dose computed tomography (LDCT) imaging for low bone mineral density and osteoporosis screening: cutoff thresholds for the attenuation values of the lower thoracic and upper lumbar vertebrae.

Background: Osteoporosis remains substantially underdiagnosed and undertreated worldwide. Chest low-dose computed tomography (LDCT) may provide a valuable and popular opportunity for osteoporosis screening. This study sought to evaluate the feasibility of the screening of low bone mineral density (BMD) and osteoporosis with mean attenuation values of the lower thoracic compared to upper lumbar vertebrae. The cutoff thresholds of the mean attenuation values in Hounsfield units (HU) were derived to facilitate implementation of opportunistic screening using chest LDCT. Methods: The participants aged 30 years or older who underwent chest LDCT and quantitative computed tomography (QCT) examinations from August 2018 to October 2020 in our hospital were consecutively included in this retrospective study. A region of interest (ROI) was placed in the trabecular bone of each vertebral body to measure the HU values. The correlations of mean HU values of lower thoracic (T11-T12) and upper lumbar (L1-L2) vertebrae with age and lumbar BMD obtained with QCT were performed using the Pearson correlation coefficient, respectively. The area under the curve (AUC) of the receiver operator characteristic (ROC) curve was generated to determine the cutoff thresholds for distinguishing low BMD from normal and osteoporosis from non-osteoporosis. Results: A total of 1,112 participants were included in the final study cohort (743 men and 369 women, mean age 58.2±8.9 years; range, 32-88 years). The mean HU values of T11-T12 and L1-L2 were significantly different among 3 QCT-defined BMD categories of osteoporosis, osteopenia, and normal (P<0.001). The differences in HU values between T11-T12 and L1-L2 in each category of bone status were statistically significant (P<0.001). The mean HU values of T11-T12 (r=-0.453, P<0.001) and L1-L2 (r=-0.498, P<0.001) had negative correlations with age. Positive correlations were observed between the mean HU values of T11-T12 (r=0.872, P<0.001) and L1-L2 (r=0.899, P<0.001) with BMD. The optimal cutoff thresholds for distinguishing low BMD from normal were average T11-T12 ≤157 HU [AUC =0.941, 95% confidence interval (CI): 0.925-0.954, P<0.001] and L1-L2 ≤138 HU (AUC =0.950, 95% CI: 0.935-0.962, P<0.001), as well as distinguishing osteoporosis from non-osteoporosis were average T11-T12 ≤125 HU (AUC =0.960, 95% CI: 0.947-0.971, P<0.001) and L1-L2 ≤107 HU (AUC =0.961, 95% CI: 0.948-0.972, P<0.001). There was no significant difference between the AUC values of T11-T12 and L1-L2 for low BMD (P=0.07) and osteoporosis (P=0.92) screening. Conclusions: We have conducted a study on low BMD and osteoporosis screening using mean attenuation values of lower thoracic and upper lumbar vertebrae. Assessment of mean attenuation values of T11-T12 and L1-L2 can be used interchangeably for low BMD and osteoporosis screening using chest LDCT, and their cutoff thresholds were established.

Assessment of Helicobacter pylori infection by deep learning based on endoscopic videos in real time.

BACKGROUND AND AIMS: Endoscopic assessment of Helicobacter pylori infection is a simple and effective method. Here, we aimed to develop a deep learning-based system named Intelligent Detection Endoscopic Assistant-Helicobacter pylori (IDEA-HP) to assess H. pylori infection by using endoscopic videos in real time. METHODS: Endoscopic data were retrospectively obtained from Zhejiang Cancer Hospital (ZJCH) for the development, validation, and testing of the system. Stored videos from ZJCH were used for assessing and comparing the performance of IDEA-HP with that of endoscopists. Prospective consecutive patients undergoing esophagogastroduodenoscopy were enrolled to assess the applicability of clinical practice. The urea breath test was used as the gold standard for diagnosing H. pylori infection. RESULTS: In 100 videos, IDEA-HP achieved a similar overall accuracy of assessing H. pylori infection to that of experts (84.0% vs. 83.6% [P = 0.729]). Nevertheless, the diagnostic accuracy (84.0% vs. 74.0% [P<0.001]) and sensitivity (82.0% vs. 67.2% [P<0.001]) of IDEA-HP were significantly higher than those of the beginners. In 191 prospective consecutive patients, IDEA-HP achieved accuracy, sensitivity, and specificity of 85.3% (95% CI: 79.0%-89.3%), 83.3% (95% CI: 72.8%-90.5%), and 85.8% (95% CI: 77.7%-91.4%), respectively. CONCLUSIONS: Our results show that IDEA-HP has great potential for assisting endoscopists in assessing H. pylori infection status during actual clinical work.

Intelligent detection endoscopic assistant: An artificial intelligence-based system for monitoring blind spots during esophagogastroduodenoscopy in real-time.

BACKGROUND: Observation of the entire stomach during esophagogastroduodenoscopy (EGD) is important; however, there is a lack of effective evaluation tools. AIMS: To develop an artificial intelligence (AI)-assisted EGD system able to automatically monitor blind spots in real-time. METHODS: An AI-based system, called the Intelligent Detection Endoscopic Assistant (IDEA), was developed using a deep convolutional neural network (DCNN) and long short-term memory (LSTM). The performance of IDEA for recognition of gastric sites in images and videos was evaluated. Primary outcomes included diagnostic accuracy, sensitivity, and specificity. RESULTS: A total of 170,297 images and 5779 endoscopic videos were collected to develop the system. As the test group, 3100 EGD images were acquired to evaluate the performance of DCNN in recognition of gastric sites in images. The sensitivity, specificity, and accuracy of DCNN were determined as 97.18%,99.91%, and 99.83%, respectively. To assess the performance of IDEA in recognition of gastric sites in EGD videos, 129 videos were used as the test group. The sensitivity, specificity, and accuracy of IDEA were 96.29%,93.32%, and 95.30%, respectively. CONCLUSIONS: IDEA achieved high accuracy for recognition of gastric sites in real-time. The system can be applied as a powerful assistant tool for monitoring blind spots during EGD.