Artificial intelligence-assisted system for the assessment of Forrest classification of peptic ulcer bleeding: a multicenter diagnostic study.

BACKGROUND: Inaccurate Forrest classification may significantly affect clinical outcomes, especially in high risk patients. Therefore, this study aimed to develop a real-time deep convolutional neural network (DCNN) system to assess the Forrest classification of peptic ulcer bleeding (PUB). METHODS: A training dataset (3868 endoscopic images) and an internal validation dataset (834 images) were retrospectively collected from the 900th Hospital, Fuzhou, China. In addition, 521 images collected from four other hospitals were used for external validation. Finally, 46 endoscopic videos were prospectively collected to assess the real-time diagnostic performance of the DCNN system, whose diagnostic performance was also prospectively compared with that of three senior and three junior endoscopists. RESULTS: The DCNN system had a satisfactory diagnostic performance in the assessment of Forrest classification, with an accuracy of 91.2% (95%CI 89.5%-92.6%) and a macro-average area under the receiver operating characteristic curve of 0.80 in the validation dataset. Moreover, the DCNN system could judge suspicious regions automatically using Forrest classification in real-time videos, with an accuracy of 92.0% (95%CI 80.8%-97.8%). The DCNN system showed more accurate and stable diagnostic performance than endoscopists in the prospective clinical comparison test. This system helped to slightly improve the diagnostic performance of senior endoscopists and considerably enhance that of junior endoscopists. CONCLUSION: The DCNN system for the assessment of the Forrest classification of PUB showed satisfactory diagnostic performance, which was slightly superior to that of senior endoscopists. It could therefore effectively assist junior endoscopists in making such diagnoses during gastroscopy.

Artificial intelligence-aided diagnostic imaging: A state-of-the-art technique in precancerous screening.

BACKGROUND AND AIM: Chromoendoscopy with the use of indigo carmine (IC) dye is a crucial endoscopic technique to identify gastrointestinal neoplasms. However, its performance is limited by the endoscopist's skill, and no standards are available for lesion identification. Thus, we developed an artificial intelligence (AI) model to replace chromoendoscopy. METHODS: This pilot study assessed the feasibility of our novel AI model in the conversion of white-light images (WLI) into virtual IC-dyed images based on a generative adversarial network. The predictions of our AI model were evaluated against the assessments of five endoscopic experts who were blinded to the purpose of this study with a staining quality rating from 1 (unacceptable) to 4 (excellent). RESULTS: The AI model successfully transformed the WLI of polyps with different morphologies and different types of lesions in the gastrointestinal tract into virtual IC-dyed images. The quality ratings of the real IC-dyed and AI images did not significantly differ concerning surface structure (AI vs IC: 3.08 vs 3.00), lesion border (3.04 vs 2.98), and overall contrast (3.14 vs 3.02) from 10 sets of images (10 AI images and 10 real IC-dyed images). Although the score depended significantly on the evaluator, the staining methods (AI or real IC) and evaluators had no significant interaction (P > 0.05) with each other. CONCLUSION: Our results demonstrated the feasibility of employing AI model's virtual IC staining, increasing the possibility of being employed in daily practice. This novel technology may facilitate gastrointestinal lesion identification in the future.

Artificial intelligence for diagnosing microvessels of precancerous lesions and superficial esophageal squamous cell carcinomas: a multicenter study.

BACKGROUND: Intrapapillary capillary loop (IPCL) is an important factor for predicting invasion depth of esophageal squamous cell carcinoma (ESCC). The invasion depth is closely related to the selection of treatment strategy. However, diagnosis of IPCLs is complicated and subject to interobserver variability. This study aimed to develop an artificial intelligence (AI) system to predict IPCLs subtypes of precancerous lesions and superficial ESCC. METHODS: Images of magnifying endoscopy with narrow band imaging from three hospitals were collected retrospectively. IPCLs subtypes were annotated on images by expert endoscopists according to Japanese Endoscopic Society classification. The performance of the AI system was evaluated using internal and external validation datasets (IVD and EVD) and compared with that of the 11 endoscopists. RESULTS: A total of 7094 images from 685 patients were used to train and validate the AI system. The combined accuracy of the AI system for diagnosing IPCLs subtypes in IVD and EVD was 91.3% and 89.8%, respectively. The AI system achieved better performance than endoscopists in predicting IPCLs subtypes and invasion depth. The ability of junior endoscopists to diagnose IPCLs subtypes (combined accuracy: 84.7% vs 78.2%, P < 0.0001) and invasion depth (combined accuracy: 74.4% vs 67.9%, P < 0.0001) were significantly improved with AI system assistance. Although there was no significant differences, the performance of senior endoscopists was slightly elevated. CONCLUSIONS: The proposed AI system could improve the diagnostic ability of endoscopists to predict IPCLs classification of precancerous lesions and superficial ESCC.

Cancer Research Advance in CKLF-like MARVEL Transmembrane Domain Containing Member Family (Review).

CKLF-like MARVEL transmembrane domain-containing family (CMTM) is a novel family of genes first reported at international level by Peking University Human Disease Gene Research Center. The gene products are between chemokines and the transmembrane-4 superfamily. Loaceted in several human chromosomes, CMTMs, which are unregulated in kinds of tumors, are potential tumor suppressor genes consisting of CKLF and CMTM1 to CMTM8. CMTMs play important roles in immune, male reproductive and hematopoietic systems. Also, it has been approved that CMTM family has strong connection with diseases of autoimmunity, haematopoietic system and haematopoietic system. The in-depth study in recent years found the close relation between CMTMs and umorigenesis, tumor development and metastasis. CMTM family has a significant clinical value in diagnosis and treatment to the diseases linking to tumor and immune system.

[Effects of temperature and heating time on armco iron spectral emissivity in atmospheric environment].

The spectral emissivity of pure iron at 1.55 µm was investigated systematically by using our self-designed reflective experimental apparatus based on the Kirchhoff's law, and the influences of temperature and heating time on the spectral emissivity of pure iron were also discussed. The experimental data showed that the spectral emissivity of pure iron increased with temperature rising and its peak value and valley value appeared at certain temperatures. By analyzing the emissivity model of metal with oxidation layer, the variation of the spectral emissivity of pure iron was illustrated. The influence of heating time on the spectral emissivity was different at different temperature. The research results will further enrich pure iron spectral emissivity data, and provide the experimental basis for its application in atmospheric environment.

Follistatin-like 1 suppresses sensory afferent transmission by activating Na+,K+-ATPase.

Excitatory synaptic transmission is modulated by inhibitory neurotransmitters and neuromodulators. We found that the synaptic transmission of somatic sensory afferents can be rapidly regulated by a presynaptically secreted protein, follistatin-like 1 (FSTL1), which serves as a direct activator of Na(+),K(+)-ATPase (NKA). The FSTL1 protein is highly expressed in small-diameter neurons of the dorsal root ganglion (DRG). It is transported to axon terminals via small translucent vesicles and secreted in both spontaneous and depolarization-induced manners. Biochemical assays showed that FSTL1 binds to the α1 subunit of NKA and elevates NKA activity. Extracellular FSTL1 induced membrane hyperpolarization in cultured cells and inhibited afferent synaptic transmission in spinal cord slices by activating NKA. Genetic deletion of FSTL1 in small DRG neurons of mice resulted in enhanced afferent synaptic transmission and sensory hypersensitivity, which could be reduced by intrathecally applied FSTL1 protein. Thus, FSTL1-dependent activation of NKA regulates the threshold of somatic sensation.