Evaluation of computer aided detection during colonoscopy among Veterans:Randomized clinical trial

BACKGROUND There has been significant interest in use of computer aided detection(CADe)devices in colonoscopy to improve polyp detection and reduce miss rate.AIM To investigate the use of CADe amongst veterans.METHODS Between September 2020 and December 2021,we performed a randomized controlled trial to evaluate the impact of CADe.Patients at Veterans Affairs Palo Alto Health Care System presenting for screening or low-risk surveillance were randomized to colonoscopy performed with or without CADe.Primary outcomes of interest included adenoma detection rate(ADR),adenomas per colonoscopy(APC),and adenomas per extraction.In addition,we measured serrated polyps per colonoscopy,non-adenomatous,non-serrated polyps per colonoscopy,serrated polyp detection rate,and procedural time.RESULTS A total of 244 patients were enrolled(124 with CADe),with similar patient characteristics(age,sex,body mass index,indication)between the two groups.Use of CADe was found to have decreased number of adenomas(1.79 vs 2.53,P=0.030)per colonoscopy compared to without CADe.There was no significant difference in number of serrated polyps or non-adenomatous non-serrated polyps per colonoscopy between the two groups.Overall,use of CADe was found to have lower ADR(68.5%vs 80.0%,P=0.041)compared to without use of CADe.Serrated polyp detection rate was lower with CADe(3.2%vs 7.5%)compared to without CADe,but this was not statistically significant(P=0.137).There was no significant difference in withdrawal and procedure times between the two groups or in detection of adenomas per extraction(71.4%vs 73.1%,P=0.613).No adverse events were identified.CONCLUSION While several randomized controlled trials have demonstrated improved ADR and APC with use of CADe,in this RCT performed at a center with high ADR,use of CADe was found to have decreased APC and ADR.Further studies are needed to understand the true impact of CADe on performance quality among endoscopists as well as determine criteria for endoscopists to consider when choosing to adopt CADe in their practices.

Computer aided diabetic retinopathy detection based on ophthalmic photography: a systematic review and Meta-analysis

AIM: To ensure the diagnostic value of computer aided techniques in diabetic retinopathy(DR) detection based on ophthalmic photography(OP). METHODS: PubM ed, EMBASE, Ei village, IEEE Xplore and Cochrane Library database were searched systematically for literatures about computer aided detection(CAD) in DR detection. The methodological quality of included studies was appraised by the Quality Assessment Tool for Diagnostic Accuracy Studies(QUADAS-2). Meta-Di Sc was utilized and a random effects model was plotted to summarize data from those included studies. Summary receiver operating characteristic curves were selected to estimate the overall test performance. Subgroup analysis was used to identify the efficiency of CAD in detecting DR, exudates(EXs), microaneurysms(MAs) as well as hemorrhages(HMs), and neovascularizations(NVs). Publication bias was analyzed using STATA. RESULTS: Fourteen articles were finally included in this Meta-analysis after literature review. Pooled sensitivity and specificity were 90%(95%CI, 85%-94%) and 90%(95%CI, 80%-96%) respectively for CAD in DR detection. With regard to CAD in EXs detecting, pooled sensitivity, specificity were 89%(95%CI, 88%-90%) and99%(95%CI, 99%-99%) respectively. In aspect of MAs and HMs detection, pooled sensitivity and specificity of CAD were 42%(95%CI, 41%-44%) and 93%(95%CI, 93%-93%) respectively. Besides, pooled sensitivity and specificity were 94%(95%CI, 89%-97%) and 87%(95%CI, 83%-90%) respectively for CAD in NVs detection. No potential publication bias was observed. CONCLUSION: CAD demonstrates overall high diagnostic accuracy for detecting DR and pathological lesions based on OP. Further prospective clinical trials are needed to prove such effect.

Artificial intelligence in polyp detection-where are we and where are we headed?

The goal of artificial intelligence in colonoscopy is to improve adenoma detection rate and reduce interval colorectal cancer.Artificial intelligence in polyp detection during colonoscopy has evolved tremendously over the last decade mainly due to the implementation of neural networks.Computer aided detection(CADe)utilizing neural networks allows real time detection of polyps and adenomas.Current CADe systems are built in single centers by multidisciplinary teams and have only been utilized in limited clinical research studies.We review the most recent prospective randomized controlled trials here.These randomized control trials,both non-blinded and blinded,demonstrated increase in adenoma and polyp detection rates when endoscopists used CADe systems vs standard high definition colonoscopes.Increase of polyps and adenomas detected were mainly small and sessile in nature.CADe systems were found to be safe with little added time to the overall procedure.Results are promising as more CADe have shown to have ability to increase accuracy and improve quality of colonoscopy.Overall limitations included selection bias as all trials built and utilized different CADe developed at their own institutions,non-blinded arms,and question of external validity.

The diagnostic rules of peripheral lung cancer preliminary study based on data mining technique

Objective： To discuss the clinical and imaging diagnostic rules of peripheral lung cancer by data mining technique, and to explore new ideas in the diagnosis of peripheral lung cancer, and to obtain early-stage technology and knowledge support of computer-aided detecting （CAD）. Methods： 58 cases of peripheral lung cancer confirmed by clinical pathology were collected. The data were imported into the database after the standardization of the clinical and CT findings attributes were identified. The data was studied comparatively based on Association Rules （AR） of the knowledge discovery process and the Rough Set （RS） reduction algorithm and Genetic Algorithm（GA） of the generic data analysis tool （ROSETTA）, respectively. Results： The genetic classification algorithm of ROSETTA generates 5 000 or so diagnosis rules. The RS reduction algorithm of Johnson＇s Algorithm generates 51 diagnosis rules and the AR algorithm generates 123 diagnosis rules. Three data mining methods basically consider gender, age, cough, location, lobulation sign, shape, ground-glass density attributes as the main basis for the diagnosis of peripheral lung cancer. Conclusion： These diagnosis rules for peripheral lung cancer with three data mining technology is same as clinical diagnostic rules, and these rules also can be used to build the knowledge base of expert system. This study demonstrated the potential values of data mining technology in clinical imaging diagnosis and differential diagnosis.