Nondestructive measurement technique for substandard amoxicillin based on thermal approach

In this study, we introduce a new nondestructive measurement technique based on a thermal approach for the determination of substandard amoxicillin. The quality control of amoxicillin is critical for patient safety, and one of the essential parameters for its evaluation is the content of the active ingredient. Traditional methods for assessing amoxicillin content are defined by their time-consuming nature, reliance on skilled personnel, and frequent necessity for specific reagents. The proposed device aims to provide a rapid and low-cost alternative that can accurately measure the amoxicillin content without damaging the sample. The method validation results indicate coefficient of determination (R2) exceeding 0.99, with percent recoveries falling within the range of 98.70–103.40%. The calculated values for limit of detection and limit of quantitation were determined to be 28.11 and 85.17 mg/L, respectively. Our experiments employed amoxicillin samples with predetermined concentrations, all of which were below the standard quality. It was observed that the proposed analytical device effectively quantifies the amoxicillin content in aqueous solutions. Each measurement took no more than 10 min, underscoring the efficiency of the analysis process. The experiments were validated through independent testing at the Government Pharmaceutical Organization in Thailand and the department of engineering science in Oxford, which provides strong evidence for the effectiveness and robustness of the technique. Overall, this study demonstrates the feasibility of using a thermal approach for the nondestructive measurement of substandard amoxicillin

Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network

Surface electromyography (sEMG) is a non-invasive and straightforward way to allow the user to actively control the prosthesis. However, results reported by previous studies on using sEMG for hand and wrist movement classification vary by a large margin, due to several factors including but not limited to the number of classes and the acquisition protocol. The objective of this paper is to investigate the deep neural network approach on the classification of 41 hand and wrist movements based on the sEMG signal. The proposed models were trained and evaluated using the publicly available database from the Ninapro project, one of the largest public sEMG databases for advanced hand myoelectric prosthetics. Two datasets, DB5 with a low-cost 16 channels and 200 Hz sampling rate setup and DB7 with 12 channels and 2 kHz sampling rate setup, were used for this study. Our approach achieved an overall accuracy of 93.87 ± 1.49 and 91.69 ± 4.68% with a balanced accuracy of 84.00 ± 3.40 and 84.66 ± 4.78% for DB5 and DB7, respectively. We also observed a performance gain when considering only a subset of the movements, namely the six main hand movements based on six prehensile patterns from the Southampton Hand Assessment Procedure (SHAP), a clinically validated hand functional assessment protocol. Classification on only the SHAP movements in DB5 attained an overall accuracy of 98.82 ± 0.58% with a balanced accuracy of 94.48 ± 2.55%. With the same set of movements, our model also achieved an overall accuracy of 99.00% with a balanced accuracy of 91.27% on data from one of the amputee participants in DB7. These results suggest that with more data on the amputee subjects, our proposal could be a promising approach for controlling versatile prosthetic hands with a wide range of predefined hand and wrist movements

Computer Integrated Endoscopic Simulator with Nonlinear Distortion Correction System

The minimally invasive surgery or endoscopic surgery is the performance of surgery through a small incision with the aid of special medical equipment called a flexible endoscope. The advantage of this technique over open surgery is that there is significantly less operative trauma, resulting in less pain and shorter recovery time. Side effects of the surgery, such as the risk of infection, also reduce. Despite the tremendous benefits, surgeons require considerable practice and time to become competent in endoscopy. Traditionally, the procedure has been taught at the expense of patient comfort and safety as residents have performed the surgery under the supervision of physicians. Patients who undergo the endoscopies performed by students, particularly early in the training period, have been more likely to suffer more discomfort and prolonged procedures. Therefore, using the simulator becomes a promising alternative for endoscopic training process. Our approach is to integrate the computer system with a realistic mechanical model to create a computer-based simulator for upper endoscopy training. The simulator will cover the basics of flexible endoscopy and teach the student the skills required to perform the upper endoscopy. The mechanical training model that simulates a human upper gastrointestinal tract, including pathologies such as ulcers and polyps, will be built and integrated with computer software that will both help and evaluate the student. Due to the optical system of an endoscope, a barrel-type spatial distortion of the image is obtained which results in an inconsistent measurement of object size and distance. The distortion correction system, which includes automatic calibration and expansion coefficients calculation, offers a better perception of size and distance from the endoscopic images to the trainee. With the completion of the distortion correction system, an evaluation system including object recognition can be implemented with high accuracy. Finally, the results of visual observation and numerical analysis are discussed. A recommendation for further study is enclosed.</p

Computer Integrated Endoscopic Simulator for Training in Esophagogastroduodenoscopy

We propose a computer integrated endoscopic simulator for training in upper endoscopy as a low-cost alternative to the traditional training methods and virtual reality simulators. The use of a real endoscope in conjunction with our simulator and computer system in an actual operating room setup makes the training environment similar to a real procedure. Endoscopic surgery is the performance of surgery through a small incision with the aid of special medical equipment called a flexible endoscope. The advantage of this technique over open surgery is that there is significantly less operative trauma, resulting in less pain and a shorter recovery time. Side effects of the surgery, such as the risk of infection, are also reduced. While endoscopy procedure has tremendous benefits, surgeons require considerable practice and time to develop competency. Traditionally, the procedure has been taught at the expense of patient comfort and safety, in other words, gastroenterology training fellows have performed the surgery under the supervision of physicians. Patients who undergo the endoscopies performed by fellows, particularly early in the training period, have been more likely to suffer more discomfort and prolonged procedures. In this study, we introduce a new type of simulator which combines the use of mechanical model and computer system as an additional or low-cost alternative for training in upper endoscopy. Our approach is to integrate a computer system with a realistic mechanical model to create a computer-based simulator for upper endoscopy training. The simulator will cover the basics of flexible endoscopy and teach a trainee the skills required to perform upper endoscopy. The mechanical training model with a sensor system that simulates a human upper gastrointestinal tract, including pathologies such as ulcers and polyps, will be built and integrated with computer software. The software offers the following functions: provides help to the trainee, provides curriculum-required learning tasks, and assesses the performance and diagnostic skills. Due to the optical nature of an endoscopic lens, the obtained image suffers from a barrel-type spatial distortion, which results in an inconsistent measurement of object size and distance. Our distortion correction system with automatic calibration, based on least squares estimation, offers a better perception of size and distance from the endoscopic images. In order to examine the endoscopic maneuvering skills of the trainee, the automatic evaluation system is created. The system uses images from the exam procedure to verify the trainee skills. We use Support Vector Machine to classify endoscopic images of different regions in upper gastrointestinal tract. The experimental results on the distortion correction and image classification are reported. Simulator validation survey result from gastroenterology surgeons and fellows is included in this dissertation. A recommendation for further study is also enclosed.</p

Classification of endoscopie images using support vector machines

This paper presents an application of support vector machines (SVMs) to mu I ti class problem in endoscopie image classification. Many studies have reported that SVMs have met with success in the texture classification problem. As an endoscopie image poses rich information expressed by texture features, we therefore investigate the potential of SVMs in this task. Strategy for multiclass problem based on an ensemble of binary classifiers is also implemented since the traditional SVMs algorithm deals with single label classification problems. The proposed scheme demonstrated an excellent classification result for multiclass problem in endoscopie image classification. We also show how a distortion correction helps further improve the results