A Comparison between Chicago Classification Versions 3.0 and 4.0 and Their Impact on Manometric Diagnoses in Esophageal High-Resolution Manometry Cases.

High-resolution manometry (HRM) facilitates the detailed evaluation of esophageal motility. In December 2020, Chicago classification (CC) version 4.0 introduced modifications to improve consistency and accuracy. We conducted this study to compare the differences in the interpretations of HRM examinations between CC 3.0 and 4.0. Consecutive HRM records at a Taiwan tertiary medical center, including wet swallows and MRS performed in both supine and sitting positions from October 2019 to May 2021, were retrospectively reviewed and analyzed using both CC versions 3.0 and 4.0. A total of 105 patients were enrolled, and 102 patients completed the exam, while three could not tolerate HRM sitting up. Refractory gastroesophageal reflux disease (GERD) symptoms (n = 65, 63.7%) and dysphagia (n = 37, 36.3%) were the main indications. A total of 18 patients (17.6%) were reclassified to new diagnoses using CC 4.0. Of the 11 patients initially diagnosed with absent contractility, 3 (27.3%) were reclassified as having Type 1 achalasia. Of the 18 patients initially diagnosed with IEM, 6 (33.3%) were reclassified as normal. The incidence of diagnosis changes was similar in both the dysphagia and refractory GERD symptoms groups (21.6% versus 15.3%, p = 0.43). The use of CC 4.0 led to changes in the diagnoses of esophageal motility disease, irrespective of examination indications. Early adoption improves the accuracy of diagnoses and affects patient management.

Prescription system to calculate precise doses of Chinese herbal medicine to avoid toxic effects.

Background and objectives: Traditional Chinese Medicine (TCM) is a therapeutic system which has been practiced for thousands of years. Although for much of its history the decoction of medicinal herbs was the most common method of consuming the herbal treatments, TCM prescriptions are now primarily prepared using concentrated Chinese herbal extracts (CCHE) in powder or granular form. However, determining the precise dose of each single Chinese herbal constituent within a prescription creates a challenge in clinical practice due to the potential risk of toxicity. To alleviate this, we invented the Chinese Intelligence Prescription System (CIPS) to calculate the exact dose of each single herb within an individual prescription. Methods: In this study, we applied CIPS in a real-world setting to analyze clinical prescriptions collected and prepared at the TCM Pharmacy of China Medical University Hospital (CMUH). Results: Our investigation revealed that 3% of all prescriptions filled in a 1-month period contained inexact dosages, suggesting that more than 170,000 prescriptions filled in Taiwan in a given month may contain potentially toxic components. We further analyzed the data to determine the excess dosages and outline the possible associated side effects. Conclusions: In conclusion, CIPS offers TCM practitioners the ability to prepare exact Chinese herbal medicine (CHM) prescriptions in order to avoid toxic effects, thereby ensuring patient safety.

Chinese Intelligence Prescription System improves prescription accuracy while decreasing labor and drug costs.

BACKGROUND AND AIM: The traditional method of taking Chinese Medicine involves creating a decoction by cooking medicinal Chinese herbs. However, this method has become less popular, being replaced by the more convenient method of consuming concentrated Chinese herbal extracts, which creates challenges related to the complexity of stacking multiple formulas. METHODS: We developed the Chinese Intelligence Prescription System (CIPS) to simplify the prescription process. In this study, we used data from our institutions pharmacy to calculate the number of reductions, average dispensing time, and resulting cost savings. RESULTS: The mean number of prescriptions was reduced from 8.19 ± 3.65 to 7.37 ± 3.34 ([Formula: see text]). The reduction in the number of prescriptions directly resulted in decreased dispensing time, reducing it from 1.79 ± 0.25 to 1.63 ± 0.66 min ([Formula: see text]). The reduced dispensing time totaled 3.75 h per month per pharmacist, equivalent to an annual labor cost savings of $15,488 NTD per pharmacist. In addition, drug loss was reduced during the prescription process, with a mean savings of $4,517 NTD per year. The combined savings adds up to a not insignificant $20,005 NTD per year per pharmacist. When taking all TCM clinics/hospitals in Taiwan into account, the total annual savings would be $77 million NTD. CONCLUSION: CIPS assists clinicians and pharmacists to formulate precise prescriptions in a clinical setting to simplify the dispensing process while reducing medical resource waste and labor costs.

Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy.

Background: Adequate bowel cleansing is important for colonoscopy performance evaluation. Current bowel cleansing evaluation scales are subjective, with a wide variation in consistency among physicians and low reported rates of accuracy. We aim to use machine learning to develop a fully automatic segmentation method for the objective evaluation of the adequacy of colon preparation. Methods: Colonoscopy videos were retrieved from a video data cohort and transferred to qualified images, which were randomly divided into training, validation, and verification datasets. The fecal residue was manually segmented. A deep learning model based on the U-Net convolutional network architecture was developed to perform automatic segmentation. The performance of the automatic segmentation was evaluated on the overlap area with the manual segmentation. Results: A total of 10,118 qualified images from 119 videos were obtained. The model averaged 0.3634 s to segmentate one image automatically. The models produced a strong high-overlap area with manual segmentation, with 94.7% ± 0.67% of that area predicted by our AI model, which correlated well with the area measured manually (r = 0.915, p < 0.001). The AI system can be applied in real-time qualitatively and quantitatively. Conclusions: We established a fully automatic segmentation method to rapidly and accurately mark the fecal residue-coated mucosa for the objective evaluation of colon preparation.

A novel machine learning-based algorithm to identify and classify lesions and anatomical landmarks in colonoscopy images.

OBJECTIVES: Computer-aided diagnosis (CAD)-based artificial intelligence (AI) has been shown to be highly accurate for detecting and characterizing colon polyps. However, the application of AI to identify normal colon landmarks and differentiate multiple colon diseases has not yet been established. We aimed to develop a convolutional neural network (CNN)-based algorithm (GUTAID) to recognize different colon lesions and anatomical landmarks. METHODS: Colonoscopic images were obtained to train and validate the AI classifiers. An independent dataset was collected for verification. The architecture of GUTAID contains two major sub-models: the Normal, Polyp, Diverticulum, Cecum and CAncer (NPDCCA) and Narrow-Band Imaging for Adenomatous/Hyperplastic polyps (NBI-AH) models. The development of GUTAID was based on the 16-layer Visual Geometry Group (VGG16) architecture and implemented on Google Cloud Platform. RESULTS: In total, 7838 colonoscopy images were used for developing and validating the AI model. An additional 1273 images were independently applied to verify the GUTAID. The accuracy for GUTAID in detecting various colon lesions/landmarks is 93.3% for polyps, 93.9% for diverticula, 91.7% for cecum, 97.5% for cancer, and 83.5% for adenomatous/hyperplastic polyps. CONCLUSIONS: A CNN-based algorithm (GUTAID) to identify colonic abnormalities and landmarks was successfully established with high accuracy. This GUTAID system can further characterize polyps for optical diagnosis. We demonstrated that AI classification methodology is feasible to identify multiple and different colon diseases.

Electron transport in a GaPSb film.

We have performed transport measurements on a gallium phosphide antimonide (GaPSb) film grown on GaAs. At low temperatures (T), transport is governed by three-dimensional Mott variable range hopping (VRH) due to strong localization. Therefore, electron-electron interactions are not significant in GaPSb. With increasing T, the coexistence of VRH conduction and the activated behavior with a gap of 20 meV is found. The fact that the measured gap is comparable to the thermal broadening at room temperature (approximately 25 meV) demonstrates that electrons can be thermally activated in an intrinsic GaPSb film. Moreover, the observed carrier density dependence on temperature also supports the coexistence of VRH and the activated behavior. It is shown that the carriers are delocalized either with increasing temperature or magnetic field in GaPSb. Our new experimental results provide important information regarding GaPSb which may well lay the foundation for possible GaPSb-based device applications such as in high-electron-mobility transistor and heterojunction bipolar transistors.