Exploring ChatGPT's Potential for Consultation, Recommendations and Report Diagnosis: Gastric Cancer and Gastroscopy Reports’ Case

Artificial intelligence (AI) has shown its effectiveness in helping clinical users meet evolving challenges. Recently, ChatGPT, a newly launched AI chatbot with exceptional text comprehension capabilities, has triggered a global wave of AI popularization and application in seeking answers through human‒machine dialogues. Gastric cancer, as a globally prevalent disease, has a five-year survival rate of up to 90% when detected early and treated promptly. This research aims to explore ChatGPT's potential in disseminating gastric cancer knowledge, providing consultation recommendations, and interpreting endoscopy reports. Through experimentation, the GPT-4 model of ChatGPT achieved an appropriateness of 91.3% and a consistency of 95.7% in a gastric cancer knowledge test. Furthermore, GPT-4 has demonstrated considerable potential in consultation recommendations and endoscopy report analysis

Mining Oncology Data: Knowledge Discovery in Clinical Performance of Cancer Patients

Our goal in this research is twofold: to develop clinical performance databases of cancer patients, and to conduct data mining and machine learning studies on collected patient records. We use these studies to develop models for predicting cancer patient medical outcomes. The clinical database is developed in conjunction with surgeons and oncologists at UMass Memorial Hospital. Aspects of the database design and representation of patient narrative are discussed here. Current predictive model design in medical literature is dominated by linear and logistic regression techniques. We seek to show that novel machine learning methods can perform as well or better than these traditional techniques. Our machine learning focus for this thesis is on pancreatic cancer patients. Classification and regression prediction targets include patient survival, wellbeing scores, and disease characteristics. Information research in oncology is often constrained by type variation, missing attributes, high dimensionality, skewed class distribution, and small data sets. We compensate for these difficulties using preprocessing, meta-learning, and other algorithmic methods during data analysis. The predictive accuracy and regression error of various machine learning models are presented as results, as are t-tests comparing these to the accuracy of traditional regression methods. In most cases, it is shown that the novel machine learning prediction methods offer comparable or superior performance. We conclude with an analysis of results and discussion of future research possibilities

Identifying individuals at-risk of developing oesophageal adenocarcinoma through symptom, risk factor and salivary biomarker analysis

Background: Oesophageal adenocarcinoma (OAC) carries a grave prognosis. Existing early detection strategies are flawed predominately because of reliance upon symptoms known to occur late when the disease is often incurable. Detection of individuals with Barrett’s Oesophagus (BO), a known pre-malignant condition, is problematic and the vast majority will not develop OAC. Aim: To explore novel methods of identifying patients with or at risk of OAC through machine learning (ML) techniques and biomarker identification. Materials and Methods: Initial work utilised novel ML on two existing patient symptom and risk factor questionnaire datasets. Additionally, targeted expression analysis was performed to establish whether transcriptomic biomarkers were present in blood and saliva of affected patients. Optimal RNA extraction techniques and saliva collection strategies for sufficient quality and quantity RNA were determined. Whole mRNA sequencing was performed on patient salivary RNA to identify biomarkers for future assessment. Epigenetic analysis was performed on salivary DNA to identify biomarkers. ML techniques analysed these data to derive a risk prediction tool. Results: ML techniques on questionnaire data produced satisfactory sensitivity (90%), but accuracy not appropriate for population screening (AUC 0.77). Blood and saliva extraction and collection methods were established and samples found to contain biomarkers. Targeted transcriptomic expression analysis demonstrated 12 / 22 tested genes were significantly aberrantly expressed in patients. 5 genes, combined with 6 questionnaire data-points, identified those with or at risk of OAC 93% sensitivity, AUC 0.88. Whole mRNA sequencing identified a further 134 genes implicated in OAC pathogenesis requiring future testing. Epigenetic analysis found 25 differentially methylated regions, when combined, identified those with or at risk of OAC to 99.9% accuracy. 5 Conclusion: Utilisation of salivary biomarkers is a potentially effective means to identify individuals with or at risk of OAC. Further work exploring transcriptomic and epigenetic data established in this thesis should be performed

International scientific and practical conference CUTTING EDGE-SCIENCE

International scientific and practical conference CUTTING EDGE-SCIENC

Colon histology slide classification with deep-learning framework using individual and fused features

Cancer occurrence rates are gradually rising in the population, which reasons a heavy diagnostic burden globally. The rate of colorectal (bowel) cancer (CC) is gradually rising, and is currently listed as the third most common cancer globally. Therefore, early screening and treatments with a recommended clinical protocol are necessary to trat cancer. The proposed research aim of this paper to develop a Deep-Learning Framework (DLF) to classify the colon histology slides into normal/cancer classes using deep-learning-based features. The stages of the framework include the following: (ⅰ) Image collection, resizing, and pre-processing; (ⅱ) Deep-Features (DF) extraction with a chosen scheme; (ⅲ) Binary classification with a 5-fold cross-validation; and (ⅳ) Verification of the clinical significance. This work classifies the considered image database using the follwing: (ⅰ) Individual DF, (ⅱ) Fused DF, and (ⅲ) Ensemble DF. The achieved results are separately verified using binary classifiers. The proposed work considered 4000 (2000 normal and 2000 cancer) histology slides for the examination. The result of this research confirms that the fused DF helps to achieve a detection accuracy of 99% with the K-Nearest Neighbor (KNN) classifier. In contrast, the individual and ensemble DF provide classification accuracies of 93.25 and 97.25%, respectively

Artificial intelligence in gastroenterology: a state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett's esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.Cellular mechanisms in basic and clinical gastroenterology and hepatolog

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward.

The recent development in the areas of deep learning and deep convolutional neural networks has significantly progressed and advanced the field of computer vision (CV) and image analysis and understanding. Complex tasks such as classifying and segmenting medical images and localising and recognising objects of interest have become much less challenging. This progress has the potential of accelerating research and deployment of multitudes of medical applications that utilise CV. However, in reality, there are limited practical examples being physically deployed into front-line health facilities. In this paper, we examine the current state of the art in CV as applied to the medical domain. We discuss the main challenges in CV and intelligent data-driven medical applications and suggest future directions to accelerate research, development, and deployment of CV applications in health practices. First, we critically review existing literature in the CV domain that addresses complex vision tasks, including: medical image classification; shape and object recognition from images; and medical segmentation. Second, we present an in-depth discussion of the various challenges that are considered barriers to accelerating research, development, and deployment of intelligent CV methods in real-life medical applications and hospitals. Finally, we conclude by discussing future directions

Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews

[EN] Background: Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people & rsquo;s health. It is necessary to assess the current status on the application of AI towards the improvement of people's health in the domains defined by WHO's Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges. Objective: To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people's health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality. Methods: A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO's PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist. Results: The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N=98) followed by Health Emergencies (N=16) and Better Health and Wellbeing (N=15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7%, N=28). The reviews featured analytics primarily over both public and private data sources (67.44%, N=87). The most used type of data was medical imaging (31.8%, N=41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4%, N=56), in which Support Vector Machine method was predominant (20.9%, N=27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4%, N=47). (...)Martinez-Millana, A.; Saez-Saez, A.; Tornero-Costa, R.; Azzopardi-Muscat, N.; Traver Salcedo, V.; Novillo-Ortiz, D. (2022). Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. International Journal of Medical Informatics. 166:1-12. https://doi.org/10.1016/j.ijmedinf.2022.10485511216