Is diagnostic and therapeutic endoscopy currently appropriate?: Suggestions for improvement

Axon, Anthony

doi:10.1016/j.bpg.2008.07.003

Cited by 7 publications

(6 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, endoscopic diagnosis is subjective and operator dependent and varies greatly with experience, reducing the detection rates of gastrointestinal cancer and precursor lesions. Endoscopy are semi-invasive procedures that often cause discomfort and fear in patients, thus affecting early GIC examinations [50,51]. Therefore, as an indispensable diagnostic technique for early screening of gastrointestinal diseases, improving the comfort and accuracy of endoscopy is the main research direction in the future.…”

Section: Gastrointestinal Endoscopymentioning

confidence: 99%

Application of nanotechnology in the early diagnosis and comprehensive treatment of gastrointestinal cancer

Deng

Jiang

et al. 2022

J Nanobiotechnol

View full text Add to dashboard Cite

Gastrointestinal cancer (GIC) is a common malignant tumour of the digestive system that seriously threatens human health. Due to the unique organ structure of the gastrointestinal tract, endoscopic and MRI diagnoses of GIC in the clinic share the problem of low sensitivity. The ineffectiveness of drugs and high recurrence rates in surgical and drug therapies are the main factors that impact the curative effect in GIC patients. Therefore, there is an urgent need to improve diagnostic accuracies and treatment efficiencies. Nanotechnology is widely used in the diagnosis and treatment of GIC by virtue of its unique size advantages and extensive modifiability. In the diagnosis and treatment of clinical GIC, surface-enhanced Raman scattering (SERS) nanoparticles, electrochemical nanobiosensors and magnetic nanoparticles, intraoperative imaging nanoparticles, drug delivery systems and other multifunctional nanoparticles have successfully improved the diagnosis and treatment of GIC. It is important to further improve the coordinated development of nanotechnology and GIC diagnosis and treatment. Herein, starting from the clinical diagnosis and treatment of GIC, this review summarizes which nanotechnologies have been applied in clinical diagnosis and treatment of GIC in recent years, and which cannot be applied in clinical practice. We also point out which challenges must be overcome by nanotechnology in the development of the clinical diagnosis and treatment of GIC and discuss how to quickly and safely combine the latest nanotechnology developed in the laboratory with clinical applications. Finally, we hope that this review can provide valuable reference information for researchers who are conducting cross-research on GIC and nanotechnology.

show abstract

Section: Gastrointestinal Endoscopymentioning

confidence: 99%

Application of nanotechnology in the early diagnosis and comprehensive treatment of gastrointestinal cancer

Deng

Jiang

et al. 2022

J Nanobiotechnol

View full text Add to dashboard Cite

show abstract

“…Cognitively, EGC lesions are difficult to recognize because the mucosa changes are often very subtle, requiring endoscopists to have thorough knowledge and rich experiences. [4,7] Here, we developed ENDOANGEL, a real-time artificial intelligence system assists in detecting EGC without blind spots to specifically address these two problems. In this multi-center study, for monitoring blind spots, we validated its effectiveness and robustness on improving EGD quality;…”

Section: Discussionmentioning

confidence: 99%

“…Technically, complete observation is an essential prerequisite for detecting EGC; however, although protocols for mapping the entire stomach have been widely proposed, they are often not followed closely in clinical practice. Cognitively, EGC lesions are difficult to recognize because the mucosal changes are often very subtle, requiring endoscopists to have thorough knowledge and extensive experience [4,7]. In the current study, we developed ENDOANGEL, a real-time AI assistance system for the detection of EGC, with no blind spots, to specifically address these two problems.…”

Section: Discussionmentioning

confidence: 99%

“…Lianlian Wu, 1,2,3# MD, Xinqi He, 1,2,3# MD, Mei Liu 4 MD Prof, Huaping Xie 4 MD Prof, Ping An, 1,2,3 MD, Jun Zhang, 1,2,3 MD, Heng Zhang 5 MD Prof, Yaowei Ai, 6 MD Prof, Qiaoyun Tong 7 MD Prof, Mingwen Guo, 6 MD Prof, Manling Huang 5 MD, Cunjin Ge, 7 MD, Zhi Yang, 7 MD, Jingping Yuan, 8 MD, Jun Liu 1,3 MM, Wei Zhou 1,2,3 MD, Xiaoda Jiang 1,2,3 MD, Xu Huang 1,2,3 MM, Ganggang Mu 1,2,3 MD, Xinyue Wan 1,2,3 MD, Yanxia Li 1,2,3 MD, Hongguang Wang 9 MD Prof, Yonggui Wang 10 PhD, Hongfeng Zhang 11 MD, Di Chen, 1,2,3 MD, Dexin Gong, 1,2,3 MD, Jing Wang, 1,2,3 MM, Li Huang, 1,2,3 MM, Jia Li 1,2,3 MM, Liwen Yao, 1,2,3 Yijie Zhu, 1,2,3 MD, Honggang Yu, 1,2,3* MD Prof Figure S1). Model 2 achieved a superior performance at the threshold of 0.90 with a sensitivity of 88.06% and a specificity of 65.26%.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…[1,2] White light imaging (WLI) endoscopy is a standard protocol for examining gastric lesions, however, the performance of endoscopists varies greatly, leading to a miss rate of 20-40% for early gastric cancer (EGC). [3] Endoscopy diagnosis is subjective, operator-dependent, and varies widely in experience [4], which reduces the detection rate of EGC and precursor lesions [5]. There is an urgent need for improving endoscopy quality and reliability.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of the effects of an artificial intelligence system on endoscopy quality and preliminary testing of its performance in detecting early gastric cancer: a randomized controlled trial

et al. 2021

View full text Add to dashboard Cite

Background and study aims: Qualified esophagogastroduodenoscopy (EGD) is a prerequisite for detecting upper gastrointestinal lesions especially early gastric cancer (EGC). Our previous report showed that artificial intelligence system could monitor blind spots during EGD. Here, we updated the system to a new one (named ENDOANGEL), verified its effectiveness on improving endoscopy quality and pre-tested its performance on detecting EGC in a multi-center randomized controlled trial. Patients and methods: ENDOANGEL was developed using deep convolutional neural networks and deep reinforcement learning. Patients undergoing EGD examination in 5 hospitals were randomly assigned to ENDOANGEL-assisted (EA) group or normal control (NC) group. The primary outcome was the number of blind spots. The second outcome includes performance of ENDOANGEL on predicting EGC in clinical setting. Results: 1,050 patients were recruited and randomized. 498 and 504 patients in EA and NC groups were respectively analyzed. Compared with NC, the number of blind spots was less (5.382±4.315 vs. 9.821±4.978, p<0.001) and the inspection time was prolonged (5.400±3.821 min vs. 4.379±3.907 min, p<0.001) in EA group. In the 498 patients from EA group, 196 gastric lesions with pathological results were identified. ENDOANGEL correctly predicted all 3 EGC (1 mucosal carcinoma and 2 high-grade neoplasia) and 2 advanced gastric cancer, with a per-lesion accuracy of 84.69%, sensitivity of 100% and specificity of 84.29% for detecting GC. Conclusions: The results of the multi-center study confirmed that ENDOANGEL is an effective and robust system to improve the quality of EGD and has the potential to detect EGC in real time.

show abstract

A survey of feature extraction and fusion of deep learning for detection of abnormalities in video endoscopy of gastrointestinal-tract

et al. 2019

View full text Add to dashboard Cite

Is diagnostic and therapeutic endoscopy currently appropriate?: Suggestions for improvement

Cited by 7 publications

References 20 publications

Application of nanotechnology in the early diagnosis and comprehensive treatment of gastrointestinal cancer

Application of nanotechnology in the early diagnosis and comprehensive treatment of gastrointestinal cancer

Evaluation of the effects of an artificial intelligence system on endoscopy quality and preliminary testing of its performance in detecting early gastric cancer: a randomized controlled trial

A survey of feature extraction and fusion of deep learning for detection of abnormalities in video endoscopy of gastrointestinal-tract

Contact Info

Product

Resources

About