Chronic atrophic gastritis detection with a convolutional neural network considering stomach regions

Clinical efficacy of moluodan in the treatment of chronic atrophic gastritis: A protocol for systematic review and meta-analysis

BACKGROUND

Chronic atrophic gastritis (CAG) is an important stage of precancerous lesions of gastric cancer, and also a key period of drug intervention. However, there is still a lack of drugs to maintain the treatment of CAG until the advent of moluodan.

OBJECTIVE

This study was conducted to assess the clinical efficacy of moluodan in the treatment of CAG by meta-analysis and trial sequential analysis.

METHODS

China National Knowledge Infrastructure, China Biology Medicine, VIP, Wanfang, Embase, PubMed, the Cochrane Library, and Web of Science databases were searched, all with the time limit from database establishment to July 2022. The published randomized controlled trials of moluodan for CAG were conducted for meta-analysis and trial sequential analysis.

RESULTS

7 studies with a total sample size of 1143 cases were included. Compared to folic acid/vitamins, moluodan alone significantly increased the effective rate of pathological detection (relative risk [RR] = 1.73, 95% confidence interval [95%CI] = [1.48,2.02], P < .00001), and moluodan in combination with folic acid/vitamins significantly increased the effective rates of pathological detection (RR = 1.37, 95%CI = [1.23,1.52], P < .00001), gastroscopy (RR = 1.37, 95%CI = [1.18,1.60], P < .0001) and symptoms (RR = 1.25, 95%CI = [1.13,1.38], P < .0001). Harbord regression showed no publication bias (P = .22). Quality of evidence evaluation demonstrated moderate quality of evidence for all indicators.

CONCLUSIONS

Moluodan can improve the effective rates of pathological examination, gastroscopy and symptoms in patients with CAG, and play a role in slowing down the disease progression and reducing clinical symptoms. It may be a potential drug for the treatment of CAG and has the value of further exploration.

A deep learning method to assist with chronic atrophic gastritis diagnosis using white light images

BACKGROUND

Chronic atrophic gastritis is a common preneoplastic condition of the stomach with a low detection rate during endoscopy.

AIMS

This study aimed to develop two deep learning models to improve the diagnostic rate.

METHODS

We collected 10,593 images from 4005 patients including 2280 patients with chronic atrophic gastritis and 1725 patients with chronic non-atrophic gastritis from two tertiary hospitals. Two deep learning models were developed to detect chronic atrophic gastritis using ResNet50. The detection ability of the deep learning model was compared with that of three expert endoscopists.

RESULTS

In the external test set, the diagnostic accuracy of model 1 for detecting gastric antrum atrophy was 0.890. The identification accuracies for the severity of gastric antrum atrophy were 0.773 and 0.590 in the internal and external test sets, respectively. In the other two external sets, the detection accuracies of model 2 for chronic atrophic gastritis were 0.854 and 0.916, respectively. Deep learning model 1's ability to identify gastric antrum atrophy was comparable to that of human experts.

CONCLUSION

Deep-learning-based models can detect chronic atrophic gastritis with good performance, which may greatly reduce the burden on endoscopists, relieve patient suffering, and improve the disease's detection rate in primary hospitals.

Experimental Research on the Antitumor Effect of Human Gastric Cancer Cells Transplanted in Nude Mice Based on Deep Learning Combined with Spleen-Invigorating Chinese Medicine

Gastric cancer is still the fifth most common malignant tumor in the world and has the fourth highest mortality rate in the world. Gastric cancer is difficult to treat because of its unobvious onset, low resection rate, and rapid deterioration. Therefore, humans have been working hard to combat gastric cancer. At present, the most commonly used treatment method is radiotherapy. However, this method will damage the normal tissues of the irradiated area while treating malignant tumor cells. It not only has side effects of damage to the patient's skin and mucous membranes but also needs high-rate radiotherapy and has high cost for chemotherapy. In order to solve these problems, it is necessary to find new treatment methods. This article proposes the use of Chinese medicine to invigorate the spleen to inhibit human gastric cancer cells. This article combines modern machine learning technology with traditional Chinese medicine and combines traditional Chinese medicine physiotherapy with Western medicine nude mouse transplantation experiments. The treatment of tumors in Chinese medicine is based on the theory of Chinese medicine and has different characteristics. Western medicine has the advantage of permanently injuring patients. The process of the experiment is to transplant human-derived gastric cancer cells into nude mice. After grouping treatments and obtaining comparative data, deep learning techniques are used to analyze the properties of Chinese medicines for strengthening the spleen and to compare the properties of Chinese medicines for strengthening the spleen. The experimental results showed that the tumor inhibition rate of mice using fluorouracil was 18%, the tumor inhibition rate of mice using low-dose Chinese medicine was 16%, and the tumor inhibition rate of mice using high-dose Chinese medicine reached 52%. 80 days after the experiment, the survival rate of mice using high-dose Chinese medicine is 100% higher than that of mice without treatment.

Clinical efficacy of Weisu granule combined with Weifuchun tablet in the treatment of chronic atrophic gastritis and its effect on serum G-17, PG I and PG II levels

OBJECTIVE

The aim of this prospective study was to explore the clinical efficacy of Weisu granules combined with Weifuchun tablets in the treatment of chronic atrophic gastritis and its effect on serum gastrin-17 (G-17), pepsinogen I (PG I), and II (PG II) levels.

METHODS

Totally, 120 patients with chronic atrophic gastritis admitted to our hospital from February 2019 to February 2020 were enrolled and randomized into a control group (n=60) treated with Weifuchun tablets, and a experimental group given Weisu granules. Serum G-17, PG I, and PG II levels, inflammatory factor levels, TCM syndrome scores, gastric mucosa pathological scores, and clinical efficacy were compared between the two groups. Gastric tissue changes were observed using gastroscopy and HE staining.

RESULTS

After treatment, the levels of serum G-17, PG I, and PG II of the experimental group were significantly better than those of the control group (P<0.001). The levels of inflammatory factors, traditional Chinese medicine (TCM) syndrome scores, and gastric mucosal pathology scores of the experimental group were significantly lower than those of the control group (P<0.001). The overall response rate of the experimental group was significantly higher than that of the control group (P<0.05). The experimental group showed a lower HP positive result and a higher HP negative conversion ratio than the control group (all P<0.05). HE staining results revealed that after treatment, the number of glands was basically restored to the level of normal gastric mucosa, and the improvement of inflammatory cell infiltration in the experimental group was significantly better than that in the control group.

CONCLUSION

Weisu granule combined with Weifuchun tablets can ameliorate serum G-17, PG I, and PG II levels in patients with chronic atrophic gastritis, relieve inflammatory responses and clinical symptoms, and improve the treatment effect, which is worth promoting in clinical practice.

CLINICAL TRIAL REGISTRATION

Chinese Registry of Clinical Trials.

TRIAL REGISTRATION NUMBER

ChiCTR200002548416. Trial URL: http://www.chictr.org.cn/showproj.aspx?proj=26516901.

Artificial intelligence in the diagnosis of gastric precancerous conditions by image-enhanced endoscopy: a multicenter, diagnostic study (with video)

BACKGROUND AND AIMS

Gastric precancerous conditions, including gastric atrophy (GA) and intestinal metaplasia (IM), play an important role in the development of gastric cancer. Image-enhanced endoscopy (IEE) shows great potential in diagnosing gastric precancerous conditions and adenocarcinoma. In this study, a deep convolutional neural network system, named ENDOANGEL, was constructed to detect gastric precancerous conditions by IEE.

METHODS

Endoscopic images were retrospectively obtained from 5 hospitals in China for the development, validation, and internal and external test of the system. Prospective consecutive patients receiving IEE were enrolled from January 13, 2020 to October 29, 2020 in Renmin Hospital of Wuhan University to assess in real time the applicability of the proposed computer-aided detection (CADe) system in clinical practice, and the performance of CADe was compared with that of endoscopists.

RESULTS

Six thousand two hundred fifty endoscopic images from 760 patients and 98 video clips from 77 individuals undergoing IEE were enrolled in this study. The diagnostic accuracy of GA was .901 (95% confidence interval [CI], .883-.917) in the internal test set, .864 (95% CI, .842-.884) in the multicenter external test set, and .878 (95% CI, .796-.935) in the prospective video test set. The diagnostic accuracy of IM was .908 (95% CI, .889-.924) in the internal test set, .859 (95% CI, .837-.880) in the multicenter external test set, and .898 (95% CI, .820-.950) in the prospective video test set. CADe achieved similar diagnostic accuracy to that of the experts for detecting GA (.869 [95% CI, .790-.927] vs .846 [95% CI, .808-.879], P = .396) and IM (.888 [95% CI, .812-.941] vs .820 [95% CI, .780-.855], P = .117) and was superior to that of nonexperts for GA (.750 [95% CI, .711-.786], P = .008) and IM (.736 [95% CI, .697-.773], P = .028).

CONCLUSIONS

CADe achieved high diagnostic accuracy in gastric precancerous conditions, which was similar to that of experts and superior to that of nonexperts. Thus, CADe provides possibilities for a wide application in assisting in the diagnosis of gastric precancerous conditions.

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.

Application of artificial intelligence to endoscopy on common gastrointestinal benign diseases

Artificial intelligence (AI) has been widely involved in every aspect of healthcare in the preclinical stage. In the digestive system, AI has been trained to assist auxiliary examinations including histopathology, endoscopy, ultrasonography, computerized tomography, and magnetic resonance imaging in detection, diagnosis, classification, differentiation, prognosis, and quality control. In the field of endoscopy, the application of AI, such as automatic detection, diagnosis, classification, and invasion depth, in early gastrointestinal (GI) cancers has received wide attention. There is a paucity of studies of AI application on common GI benign diseases based on endoscopy. In the review, we provide an overview of AI applications to endoscopy on common GI benign diseases including in the esophagus, stomach, intestine, and colon. It indicates that AI will gradually become an indispensable part of normal endoscopic detection and diagnosis of common GI benign diseases as clinical data, algorithms, and other related work are constantly repeated and improved.