SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
Browse
2 results
Search Results
Item Embargo AI-Enabled Bioresponsive Clinical Decision Support Systems for Chronic Pain: User-Centered Approach(Saudi Digital Library, 0025) Alrefaei, Doaa; Soussan, DjamasbiThe advancement of eye-tracking technologies has enabled the development of systems capable of detecting attention and cognitive states objectively and in real time. Biometric technologies that capture psychological measures, such as eye movements (EMs), have allowed user experience (UX) research to expand toward building smart bioresponsive tools. One area that may benefit from these advancements is chronic pain, where self-report methods are often limited in capturing the complex phenomenon of chronic pain experience in both research and practice. This has established a need for objective biomarkers that can support pain assessment. Pain literature suggests the use of EMs as potential biomarkers, as they reflect pain-related attentional patterns. This dissertation adopts a bioresponsive, UX research approach to explore the efficacy of using EMs to detect pain experience in individuals with and without chronic pain. A proof-of-concept AI tool was developed to detect chronic pain using only EMs from individuals with and without chronic pain, achieving an accuracy of 81%, thereby demonstrating the robustness of EMs as a potential biomarker for pain. To successfully evolve this proof of concept into a fully developed and effective Clinical Decision Support System (CDSS) for chronic pain treatment and management, it is essential to understand the needs of the healthcare professionals who will use the system. As a first step, traditional UX research methods were employed to conduct interviews with healthcare professionals involved in the treatment and management of chronic pain. Based on this research, six user personas, four representing doctors and two representing nurses, were developed to serve as a foundational guideline for the design of an initial CDSS prototype. The findings of this dissertation contribute to both UX research and pain science by presenting a comprehensive methodology for using eye movements (EMs) as input signals to an AI tool capable of detecting differences in attentional patterns toward pain-related stimuli. It also contributes to clinical practice by outlining design guidelines for developing an initial prototype of such an AI-based CDSS, grounded in the needs and workflows of healthcare professionals.17 0Item Restricted Field Cancerization and Microbiome Effects on Lung Cancer: A Source of Early Detection Biomarkers to Improve Patients’ Outcome(George Mason University, 2024-08-16) Alhammad, Rayan; Luchini, AlessandraLung cancer results in more deaths than any other cancer in the United States and worldwide, with non-small cell lung cancer (NSCLC) accounting for most cases. Diagnosis typically involves chest imaging, molecular testing, and biopsy. However, most patients are diagnosed at advanced stages, with only a 6% chance of a 5-year survival rate. In contrast, early-stage diagnosis and treatment can result in a favorable prognosis, with a high 5-year survival rate of 70-90%. The concept of tumor field cancerization describes a phenomenon where exposure to carcinogens can cause histologic changes in large areas of tissue, creating a field of pre-malignant cells that can eventually develop into tumors. Additionally, microbiota dysbiosis might influence tumor development. Studies have identified several commensal bacteria present in the lower airway tracts, such as Streptococcus, Prevotella, and Veillonella. The high mortality rate of lung cancer is often attributed to i) its late-stage diagnosis, ii) aggressive nature given its ability to metastasize early in the disease process complicating treatment and reducing survival rates, and iii) significant therapeutic challenges despite current treatments such as surgery, chemotherapy, radiation therapy, targeted therapy and immunotherapy. Despite advancements, the survival rate for advanced lung cancer remains low. To address this challenge, our research focuses on identifying risk protein biomarkers that are associated with the earliest molecular changes indicative of an ongoing tumorigenic process, thus offering significant potential for early intervention. Our study investigates the phenotypic molecular changes in the bronchial tree of NSCLC patients in light of the field cancerization theory and correlates these findings with blood biomarkers to support the future development of a non-invasive risk assessment test. Using enhanced liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomic analysis and two independent cohorts of lung cancer patients (N=18, and N=263) with matched plasma and bronchial tree tissue specimens, we identified a set of 6 and 13 candidate risk plasma biomarkers with tissue origin. Additionally, we explored the microbiome proteome composition in NSCLC patient tissue and plasma to support future characterization of its potential role in cancer development. Risk biomarkers will enable the evaluation of individuals at high risk, guiding necessary lifestyle adjustments and facilitating the development of personalized prevention plans and therapies.52 0