Using deep learning and explainable artificial intelligence to assess the severity of gastroesophageal reflux disease according to the Los Angeles Classification System

Efficacy of Shugan Hewei formula combined with rabeprazole in refractory gastroesophageal reflux disease: randomized, double-blind, placebo-controlled trial

OBJECTIVES

To assess the efficacy of the Chinese herbal medication Shugan Hewei formula (SHF) combined with rabeprazole in patients with refractory gastroesophageal reflux disease (rGERD).

METHOD

A total of 264 participants were randomly assigned to the treatment group (n = 132) receiving SHF granules (20 mg) combined with rabeprazole (10 mg) and the control group (n = 132) receiving placebo SHF granules (20 mg) combined with rabeprazole (20 mg). Both groups undergo 8 weeks of treatment and 2 weeks of follow-up.

RESULTS

The treatment group showed higher total clinical symptom efficacy and lower total symptom scores compared to the control group. The treatment group was superior to the control group in reducing rGERD major symptom scores, including heartburn, retrosternal pain, regurgitation and belching, and acid regurgitation. Additionally, treatment group (Z = 8.169, P < 0.001) and control group (Z = 9.800, P < 0.001) treatments were all significantly attenuated esophageal inflammation, demonstrating comparable efficacy. Patients with esophagitis grade A decreased from 40.34% to 17.23%, and those with grade B decreased from 11.76% to 3.78% in the treatment group. The results of the SF-36 scale showed that combination therapy was more effective in improving role limitations due to physical health, vitality, general health, total somato-physical health, and psychiatric mental health.

CONCLUSION

Our study reveals that the combined treatment of SHF with rabeprazole is more efficacious in managing patients with rGERD when contrasted with sole rabeprazole treatment.

Efficacy of different endoscopic treatments for gastroesophageal reflux disease: a systematic review and network meta-analysis

BACKGROUND

There are no direct comparisons across different endoscopic therapies for gastroesophageal reflux disease (GERD). This study aimed to evaluate the relative effects of different endoscopic therapies in GERD.

METHODS

Five databases were searched until August 2023 for randomized controlled trials (RCTs) that compared the efficacy of endoscopic band ligation (EBL), Stretta, endoscopic fundoplication (transoral incisionless fundoplication [TIF], endoscopic full-thickness plication [EFTP], and EndoCinch plication procedure [EndoCinch, CR BARD, Billerica, Mass., USA]), or proton pump inhibitors (PPIs)/sham procedure for GERD. Bayesian network meta-analysis was performed.

RESULTS

A total of 19 trials comprising 1181 patients were included. EBL (mean difference [MD], -7.75; 95% credible interval [CrI], -13.90 to -1.44), Stretta (MD, -9.86; 95% CrI, -19.05 to -0.58), and TIF (MD, -12.58; 95% CrI, -20.23 to -4.91) all significantly improved patients' health-related quality of life score with equivalent efficacy compared with PPIs. TIF and EBL achieved equivalent efficacy in reducing PPIs utility (risk ratio [RR], 0.66; 95% CrI, 0.40-1.05) and both were significantly superior to other endoscopic interventions (Stretta, EFTP, and EndoCinch). Besides, EBL and TIF also could significantly decrease the esophagitis incidence compared with PPIs (EBL [RR, 0.34; 95% CrI, 0.22-0.48] and TIF [RR, 0.38; 95% CrI, 0.15-0.88]). In terms of lower esophageal sphincter (LES) pressure, only TIF could significantly increase the LES pressure (MD, 6.53; 95% CrI, 3.65-9.40) to PPIs. In contrast, TIF was inferior to PPIs in decreasing esophageal acid exposure (MD, 2.57; 95% CrI, 0.77-4.36).

CONCLUSION

Combining the evidence, EBL and TIF may have comparable efficacy and both might be superior to Stretta, EFTP, or EndoCinch in GERD treatment.

Role of Artificial Intelligence in the Diagnosis of Gastroesophageal Reflux Disease

Gastroesophageal reflux disease (GERD) is a disorder that usually presents with heartburn. GERD is diagnosed clinically, but most patients are misdiagnosed due to atypical presentations. The increased use of artificial intelligence (AI) in healthcare has provided multiple ways of diagnosing and treating patients accurately. In this review, multiple studies in which AI models were used to diagnose GERD are discussed. According to the studies, using AI models helped to diagnose GERD in patients accurately. AI, although considered one of the most potent emerging aspects of medicine with its accuracy in patient diagnosis, presents limitations of its own, which explains why healthcare providers may hesitate to use AI in patient care. The challenges and limitations should be addressed before AI is fully incorporated into the healthcare system.

The performance of artificial intelligence large language model-linked chatbots in surgical decision-making for gastroesophageal reflux disease

BACKGROUND

Large language model (LLM)-linked chatbots may be an efficient source of clinical recommendations for healthcare providers and patients. This study evaluated the performance of LLM-linked chatbots in providing recommendations for the surgical management of gastroesophageal reflux disease (GERD).

METHODS

Nine patient cases were created based on key questions addressed by the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) guidelines for the surgical treatment of GERD. ChatGPT-3.5, ChatGPT-4, Copilot, Google Bard, and Perplexity AI were queried on November 16th, 2023, for recommendations regarding the surgical management of GERD. Accurate chatbot performance was defined as the number of responses aligning with SAGES guideline recommendations. Outcomes were reported with counts and percentages.

RESULTS

Surgeons were given accurate recommendations for the surgical management of GERD in an adult patient for 5/7 (71.4%) KQs by ChatGPT-4, 3/7 (42.9%) KQs by Copilot, 6/7 (85.7%) KQs by Google Bard, and 3/7 (42.9%) KQs by Perplexity according to the SAGES guidelines. Patients were given accurate recommendations for 3/5 (60.0%) KQs by ChatGPT-4, 2/5 (40.0%) KQs by Copilot, 4/5 (80.0%) KQs by Google Bard, and 1/5 (20.0%) KQs by Perplexity, respectively. In a pediatric patient, surgeons were given accurate recommendations for 2/3 (66.7%) KQs by ChatGPT-4, 3/3 (100.0%) KQs by Copilot, 3/3 (100.0%) KQs by Google Bard, and 2/3 (66.7%) KQs by Perplexity. Patients were given appropriate guidance for 2/2 (100.0%) KQs by ChatGPT-4, 2/2 (100.0%) KQs by Copilot, 1/2 (50.0%) KQs by Google Bard, and 1/2 (50.0%) KQs by Perplexity.

CONCLUSIONS

Gastrointestinal surgeons, gastroenterologists, and patients should recognize both the promise and pitfalls of LLM's when utilized for advice on surgical management of GERD. Additional training of LLM's using evidence-based health information is needed.

The performance of artificial intelligence large language model-linked chatbots in surgical decision-making for gastroesophageal reflux disease

BACKGROUND

Large language model (LLM)-linked chatbots may be an efficient source of clinical recommendations for healthcare providers and patients. This study evaluated the performance of LLM-linked chatbots in providing recommendations for the surgical management of gastroesophageal reflux disease (GERD).

METHODS

Nine patient cases were created based on key questions addressed by the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) guidelines for the surgical treatment of GERD. ChatGPT-3.5, ChatGPT-4, Copilot, Google Bard, and Perplexity AI were queried on November 16th, 2023, for recommendations regarding the surgical management of GERD. Accurate chatbot performance was defined as the number of responses aligning with SAGES guideline recommendations. Outcomes were reported with counts and percentages.

RESULTS

Surgeons were given accurate recommendations for the surgical management of GERD in an adult patient for 5/7 (71.4%) KQs by ChatGPT-4, 3/7 (42.9%) KQs by Copilot, 6/7 (85.7%) KQs by Google Bard, and 3/7 (42.9%) KQs by Perplexity according to the SAGES guidelines. Patients were given accurate recommendations for 3/5 (60.0%) KQs by ChatGPT-4, 2/5 (40.0%) KQs by Copilot, 4/5 (80.0%) KQs by Google Bard, and 1/5 (20.0%) KQs by Perplexity, respectively. In a pediatric patient, surgeons were given accurate recommendations for 2/3 (66.7%) KQs by ChatGPT-4, 3/3 (100.0%) KQs by Copilot, 3/3 (100.0%) KQs by Google Bard, and 2/3 (66.7%) KQs by Perplexity. Patients were given appropriate guidance for 2/2 (100.0%) KQs by ChatGPT-4, 2/2 (100.0%) KQs by Copilot, 1/2 (50.0%) KQs by Google Bard, and 1/2 (50.0%) KQs by Perplexity.

CONCLUSIONS

Gastrointestinal surgeons, gastroenterologists, and patients should recognize both the promise and pitfalls of LLM's when utilized for advice on surgical management of GERD. Additional training of LLM's using evidence-based health information is needed.

Using deep learning to assess the function of gastroesophageal flap valve according to the Hill classification system

BACKGROUND

The endoscopic Hill classification of the gastroesophageal flap valve (GEFV) is of great importance for understanding the functional status of the esophagogastric junction (EGJ). Deep learning (DL) methods have been extensively employed in the area of digestive endoscopy. To improve the efficiency and accuracy of the endoscopist's Hill classification and assist in incorporating it into routine endoscopy reports and GERD assessment examinations, this study first employed DL to establish a four-category model based on the Hill classification.

MATERIALS AND METHODS

A dataset consisting of 3256 GEFV endoscopic images has been constructed for training and evaluation. Furthermore, a new attention mechanism module has been provided to improve the performance of the DL model. Combined with the attention mechanism module, numerous experiments were conducted on the GEFV endoscopic image dataset, and 12 mainstream DL models were tested and evaluated. The classification accuracy of the DL model and endoscopists with different experience levels was compared.

RESULTS

12 mainstream backbone networks were trained and tested, and four outstanding feature extraction backbone networks (ResNet-50, VGG-16, VGG-19, and Xception) were selected for further DL model development. The ResNet-50 showed the best Hill classification performance; its area under the curve (AUC) reached 0.989, and the classification accuracy (93.39%) was significantly higher than that of junior (74.83%) and senior (78.00%) endoscopists.

CONCLUSIONS

The DL model combined with the attention mechanism module in this paper demonstrated outstanding classification performance based on the Hill grading and has great potential for improving the accuracy of the Hill classification by endoscopists.

Computer-Based Intelligent Solutions for the Diagnosis of Gastroesophageal Reflux Disease Phenotypes and Chicago Classification 3.0

Gastroesophageal reflux disease (GERD) is a multidisciplinary disease; therefore, when treating GERD, a large amount of data needs to be monitored and managed.The aim of our study was to develop a novel automation and decision support system for GERD, primarily to automatically determine GERD and its Chicago Classification 3.0 (CC 3.0) phenotypes. However, phenotyping is prone to errors and is not a strategy widely known by physicians, yet it is very important in patient treatment. In our study, the GERD phenotype algorithm was tested on a dataset with 2052 patients and the CC 3.0 algorithm was tested on a dataset with 133 patients. Based on these two algorithms, a system was developed with an artificial intelligence model for distinguishing four phenotypes per patient. When a physician makes a wrong phenotyping decision, the system warns them and provides the correct phenotype. An accuracy of 100% was obtained for both GERD phenotyping and CC 3.0 in these tests. Finally, since the transition to using this developed system in 2017, the annual number of cured patients, around 400 before, has increased to 800. Automatic phenotyping provides convenience in patient care, diagnosis, and treatment management. Thus, the developed system can substantially improve the performance of physicians.