Recent advancements in non-invasive wearable electrochemical biosensors for biomarker analysis - A review.

A biomarker is a molecular indicator that can be used to identify the presence or severity of a disease. It may be produced due to biochemical or molecular changes in normal biological processes. In some cases, the presence of a biomarker itself is an indication of the disease, while in other cases, the elevated or depleted level of a particular protein or chemical substance aids in identifying a disease. Biomarkers indicate the progression of the disease in response to therapeutic interventions. Identifying these biomarkers can assist in diagnosing the disease early and providing proper therapeutic treatment. In recent years, wearable electrochemical (EC) biosensors have emerged as an important tool for early detection due to their excellent selectivity, low cost, ease of fabrication, and improved sensitivity. There are several challenges in developing a fully integrated wearable sensor, such as device miniaturization, high power consumption, incorporation of a power source, and maintaining the integrity and durability of the biomarker for long-term continuous monitoring. This review covers the recent advancements in the fabrication techniques involved in device development, the types of sensing platforms utilized, different materials used, challenges, and future developments in the field of wearable biosensors.

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques.

Presently, close to two million patients globally succumb to gastrointestinal reflux diseases (GERD). Video endoscopy represents cutting-edge technology in medical imaging, facilitating the diagnosis of various gastrointestinal ailments including stomach ulcers, bleeding, and polyps. However, the abundance of images produced by medical video endoscopy necessitates significant time for doctors to analyze them thoroughly, posing a challenge for manual diagnosis. This challenge has spurred research into computer-aided techniques aimed at diagnosing the plethora of generated images swiftly and accurately. The novelty of the proposed methodology lies in the development of a system tailored for the diagnosis of gastrointestinal diseases. The proposed work used an object detection method called Yolov5 for identifying abnormal region of interest and Deep LabV3+ for segmentation of abnormal regions in GERD. Further, the features are extracted from the segmented image and given as an input to the seven different machine learning classifiers and custom deep neural network model for multi-stage classification of GERD. The DeepLabV3+ attains an excellent segmentation accuracy of 95.2% and an F1 score of 93.3%. The custom dense neural network obtained a classification accuracy of 90.5%. Among the seven different machine learning classifiers, support vector machine (SVM) outperformed with classification accuracy of 87% compared to all other class outperformed combination of object detection, deep learning-based segmentation and machine learning classification enables the timely identification and surveillance of problems associated with GERD for healthcare providers.