Ultrasound Color Pattern of Colonic Hamartomatous Polyps.

Colonic hamartomatous polyps are clinically benign tumors. Colonic hamartomas are polypoid lesions that are rare in adults and most commonly encountered in infants and children. We report an unusual case of giant colonic hamartomatous polyps that were found incidentally during a medical workup for acute lower gastrointestinal bleeding in a 26-year-old woman. We present the color Doppler ultrasound, computed tomography scan, and endoscopic pattern of colonic hamartomatous polyps.

Deep learning-based endoscopic anatomy classification: an accelerated approach for data preparation and model validation.

BACKGROUND: Photodocumentation during endoscopy procedures is one of the indicators for endoscopy performance quality; however, this indicator is difficult to measure and audit in the endoscopy unit. Emerging artificial intelligence technology may solve this problem, which requires a large amount of material for model development. We developed a deep learning-based endoscopic anatomy classification system through convolutional neural networks with an accelerated data preparation approach. PATIENTS AND METHODS: We retrospectively collected 8,041 images from esophagogastroduodenoscopy (EGD) procedures and labeled them using two experts for nine anatomical locations of the upper gastrointestinal tract. A base model for EGD image multiclass classification was first developed, and an additional 6,091 images were enrolled and classified by the base model. A total of 5,963 images were manually confirmed and added to develop the subsequent enhanced model. Additional internal and external endoscopy image datasets were used to test the model performance. RESULTS: The base model achieved total accuracy of 96.29%. For the enhanced model, the total accuracy was 96.64%. The overall accuracy improved with the enhanced model compared with the base model for the internal test dataset without narrowband images (93.05% vs. 91.25%, p < 0.01) or with narrowband images (92.74% vs. 90.46%, p < 0.01). The total accuracy was 92.56% of the enhanced model on the external test dataset. CONCLUSIONS: We constructed a deep learning-based model with an accelerated approach that can be used for quality control in endoscopy units. The model was also validated with both internal and external datasets with high accuracy.

Comparison Between Same-Day and Split-Dose Preparations with Sodium Picosulfate/Magnesium Citrate: A Randomized Noninferiority Study.

BACKGROUND: Sodium picosulfate/magnesium citrate (SPMC) is a small-volume bowel cleansing agent with similar efficacy to and better tolerability than polyethylene glycol. However, we found no data on which SPMC preparation (same-day vs. split-dose) provides better bowel cleansing efficacy for afternoon colonoscopy. AIMS: To compare bowel cleansing efficacy of different timing of the regimen. METHODS: This randomized, single-center, endoscopist-blinded, noninferior study compared same-day and split-dose SPMC preparations for afternoon colonoscopy in 101 and 96 patients, respectively. We also included a prospective observation group of 100 patients receiving morning colonoscopy to compare bowel preparation between morning and afternoon colonoscopies. Bowel cleansing efficacy was then evaluated by the Aronchick Scale, Ottawa Bowel Preparation Scale (OBPS), Boston Bowel Preparation Scale (BBPS), and the Bubble Scale. RESULTS: Same-day and split-dose preparations were similar in efficacy in all four scales. In the Aronchick Scale, the success rate (excellent and good cleanliness) was higher in same-day preparation than in split-dose preparation (100% vs. 92.8%). The same-day preparation also obtained a better OBPS score (1.4 vs. 2.1), but BBPS showed no difference between such groups (7.7 vs. 7.4). CONCLUSION: Same-day preparation with SPMC is not inferior to split-dose preparation for afternoon colonoscopy.

Development and validation of a deep learning-based algorithm for colonoscopy quality assessment.

BACKGROUND: Quality indicators should be assessed and monitored to improve colonoscopy quality in clinical practice. Endoscopists must enter relevant information in the endoscopy reporting system to facilitate data collection, which may be inaccurate. The current study aimed to develop a full deep learning-based algorithm to identify and analyze intra-procedural colonoscopy quality indicators based on endoscopy images obtained during the procedure. METHODS: A deep learning system for classifying colonoscopy images for quality assurance purposes was developed and its performance was assessed with an independent dataset. The system was utilized to analyze captured images and results were compared with those of real-world reports. RESULTS: In total, 10,417 images from the hospital endoscopy database and 3157 from Hyper-Kvasir open dataset were utilized to develop the quality assurance algorithm. The overall accuracy of the algorithm was 96.72% and that of the independent test dataset was 94.71%. Moreover, 761 real-world reports and colonoscopy images were analyzed. The accuracy of electronic reports about cecal intubation rate was 99.34% and that of the algorithm was 98.95%. The agreement rate for the assessment of polypectomy rates using the electronic reports and the algorithm was 0.87 (95% confidence interval 0.83-0.90). A good correlation was found between the withdrawal time calculated using the algorithm and that entered by the physician (correlation coefficient r = 0.959, p < 0.0001). CONCLUSION: We proposed a novel deep learning-based algorithm that used colonoscopy images for quality assurance purposes. This model can be used to automatically assess intra-procedural colonoscopy quality indicators in clinical practice.

Upper endoscopy photodocumentation quality evaluation with novel deep learning system.

OBJECTIVES: Visualization and photodocumentation during endoscopy procedures are suggested to be one indicator for endoscopy performance quality. However, this indicator is difficult to measure and audit manually in clinical practice. Artificial intelligence (AI) is an emerging technology that may solve this problem. METHODS: A deep learning model with an accuracy of 96.64% was developed from 15,305 images for upper endoscopy anatomy classification in the unit. Endoscopy images for asymptomatic patients receiving screening endoscopy were evaluated with this model to assess the completeness of photodocumentation rate. RESULTS: A total of 15,723 images from 472 upper endoscopies performed by 12 endoscopists were enrolled. The complete photodocumentation rate from the pharynx to the duodenum was 53.8% and from the esophagus to the duodenum was 78.0% in this study. Endoscopists with a higher adenoma detection rate had a higher complete examination rate from the pharynx to duodenum (60.0% vs. 38.7%, P < 0.0001) and from esophagus to duodenum (83.0% vs. 65.7%, P < 0.0001) compared with endoscopists with lower adenoma detection rate. The pharynx, gastric angle, gastric retroflex view, gastric antrum, and the first portion of duodenum are likely to be missed by endoscopists with lower adenoma detection rates. CONCLUSIONS: We report the use of a deep learning model to audit endoscopy photodocumentation quality in our unit. Endoscopists with better performance in colonoscopy had a better performance for this quality indicator. The use of such an AI system may help the endoscopy unit audit endoscopy performance.

Elimination of Hepatitis C Virus in a Dialysis Population: A Collaborative Care Model in Taiwan.

RATIONALE & OBJECTIVE: Hemodialysis facilities are high-risk environments for the spread of hepatitis C virus (HCV). Eliminating HCV from all dialysis facilities in a community may be achieved more effectively under a collaborative care model. STUDY DESIGN: Quality improvement study of multidisciplinary collaborative care teams including nephrologists, gastroenterologists, and public health practitioners. SETTING & PARTICIPANTS: All dialysis patients in Changhua County, Taiwan were treated using an interdisciplinary collaborative care model implemented within a broader Changhua-Integrated Program to Stop HCV Infection (CHIPS-C). QUALITY IMPROVEMENT ACTIVITIES: Provision of an HCV care cascade to fill 3 gaps, including screening and testing, diagnosis, and universal direct-acting antiviral (DAA) treatment implemented by collaborating teams of dialysis practitioners and gastroenterologists working under auspices of Changhua Public Health Bureau. OUTCOME: Outcome measures included quality indicators pertaining to 6 steps in HCV care ranging from HCV screening to treatment completion to cure. ANALYTICAL APPROACH: A descriptive analysis. RESULTS: A total of 3,657 patients from 31 dialysis facilities were enrolled. All patients completed HCV screening. The DAA treatment initiation rate and completion rate were 88.9% and 94.0%, respectively. The collaborative care model achieved a cure rate of 166 (96.0%) of 173 patients. No virologic failure occurred. The cumulative treatment ratios for patients with chronic HCV infection increased from 5.3% before interferon-based therapy (2017) to 25.6% after restricted provision of DAA (2017-2018), and then to 89.1% after universal access to DAA (2019). LIMITATIONS: Unclear impact of this collaborative care program on incident dialysis patients entering dialysis facilities each year and on patients with earlier stages of chronic kidney disease. CONCLUSIONS: A collaborative care model in Taiwan increased the rates of diagnosis and treatment for HCV in dialysis facilities to levels near those established by the World Health Organization.

Retrieval of lost patients in the system for hepatitis C microelimination: a single-center retrospective study.

BACKGROUND: Hepatitis C virus (HCV) is one of the major causes of chronic liver disease, cirrhosis, and liver cancer. Most of the infected people have no clinical symptoms. The current strategy for HCV elimination includes test and treatment. In this study, we aimed to evaluate the campaign for retrieving patients who were lost to follow-up, for subsequent re-evaluation. METHODS: From January 2020 to October 2020, patients who had prior tests for positive anti-HCV antibody in 2010-2018 in our hospital were enrolled for our patient callback campaign. Patients who had unknown HCV RNA status or no documented successful antiviral therapy history were selected for anti-HCV therapy re-evaluation. To facilitate patient referral in the hospital, we developed an electronic reminding system and called the candidate patients via telephone during the study period. RESULTS: Through the hospital electronic system, 3783 patients with positive anti-HCV antibody documentation were identified. Among them, 1446 (38.22%) had tested negative for HCV RNA or had anti-HCV therapy, thereby excluded. Of the 2337 eligible patients, 1472 (62.99%) were successfully contacted and called back during the study period for subsequent HCV RNA testing and therapy. We found that 42.19% of the patients had positive HCV RNA and 88% received subsequent anti-HCV therapy. CONCLUSIONS: A significant number of patients with positive HCV serology were lost for HCV confirmatory test or therapy in the hospital. Therefore, this targeted HCV callback approach in the hospital is feasible and effective in achieving microelimination.

Evaluation of dose-efficacy of sorafenib and effect of transarterial chemoembolization in hepatocellular carcinoma patients: a retrospective study.

BACKGROUND: Transarterial chemoembolization (TACE) and sorafenib are the therapeutic standard for intermediate and advanced stage hepatocellular carcinoma (HCC) patients respectively. High costs with adverse events (AE) of sorafenib might limit sorafenib dosage, further affecting therapeutic response. To attain greatest benefit, we evaluated the efficacy of different doses and effect of TACE during and after sorafenib discontinuation in patients representing Child-Pugh Classification Class A with venous or extra-hepatic invasion. METHODS: A total 156 patients met the criteria and were divided into Groups I (n = 52) accepting 800 mg/day; II (n = 58) accepting 800 mg/day and reduced to 400 mg/day owing to AE; and III (n = 46) accepting 400 mg/day. TACE was performed during and after sorafenib discontinuation and therapeutic response bimonthly to four-monthly was rated thereafter. RESULTS: Median duration of sorafenib treatment and patients' survival were 4.00 ± 0.45 and 7.50 ± 1.44 months in all cases; 2.50 ± 0.90 and 5.00 ± 1.10 months in Group I; 5.50 ± 1.27 and 16.50 ± 1.86 months in Group II; 4.00 ± 0.94 and 6.50 ± 2.49 months in Group III. Group II presented the best response and survival benefit (p = 0.010 and p = 0.011 respectively). Child-Pugh Classification score 5 (Hazard Ratio = 0.492, p = 0.049), absent AE (3.423, p = 0.015), tumor numbers ≤ 3 (0.313, p = 0.009), sorafenib duration ≤ 1 cycle (3.694, p = 0.004), and absent TACE (3.197, p = 0.008) significantly correlated with patient survival. TACE benefit appeared in separate and total cases during (p = 0.002, p = 0.595, p = 0.074, p = 0.002 respectively) and after discontinuation of sorafenib administration (p = 0.001, p = 0.034, p = 0.647, p = 0.001 respectively). CONCLUSIONS: Low-dosage sorafenib not only appeared tolerable and lowered economic pressure but also provided satisfactory results. TACE benefited patient's survival during and after sorafenib discontinuation.