Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
Browse
3 results
Search Results
Item Restricted Assessing artificial intelligence MRI autocontouring in Raystation and the AutoConfidence uncertainty model for brain radiotherapy(The University of Leeds, 2024-10) Alzahrani, Nouf; Henry, Ann; Nix, Michael; Murray, Louise; Al-qaisieh, BasharAbstract: Background: In radiotherapy, deep learning autosegmentation (DL-AS) and automation of quality assurance (QA) have the potential to efficiently standardize and enhance the quality of contours. Aim: To assess the performance of DL-AS in delineating organs-at-risk (OARs) in brain RT using the RayStation Treatment Planning System. Secondly, to build and test a novel artificial intelligence QA model called AutoConfidence (ACo). Methods: Retrospective MRI and CT cases were randomly selected for training and testing. DL-AS models were evaluated from geometric and dosimetric perspectives, focusing on the impact of pre-training editing. The ACo model was evaluated using two sources of autosegmentation: internal autosegmentations (IAS) produced from the ACo generator and two external DL-AS with different qualities (high and low quality) produced from RayStation models. Results: The edited DL-AS models generated more segmentations than the unedited models. Editing pituitary, orbits, optic nerves, lenses, and optic chiasm on MRI before training significantly improved at least one geometry metric. MRI-based DL-AS performed worse than CT-based in delineating the lacrimal gland, whereas the CT-based performed worse in delineating the optic chiasm. Except for the right orbit, when delineated using MRI models, the dosimetric statistical analysis revealed no superior model in terms of the dosimetric accuracy between the MR and CT DL-AS models. The number of patients where the clinical significance threshold was exceeded was higher for the optic chiasm D1% than for other OARs, for all models. ACo had excellent performance on both internal and external segmentations across all OARs (except lenses). Mathews Correlation Coefficient was higher on IAS and low-quality external segmentations than high-quality ones. Conclusion: MRI DL-AS in RT may improve consistency, quality, and efficiency but requires careful editing of training contours. ACo was a reliable predictor of uncertainty and errors on DL-AS, demonstrating its potential as an independent, reference-free QA tool.9 0Item Restricted Knowledge, Use, and Confidence in Artificial Intelligence Applications Among Orthodontists in the UK and Ireland(The University of Edinburgh, 2024) Sabbagh, Abdulrahman; McGuinness, NiallBackground: Artificial intelligence (AI) has been applied in orthodontics using different applications, including cephalometric tracing, remote and initial assessment, remote monitoring of treatment progress, and extraction decision-making. This study aims to assess knowledge, usage, confidence, and future interest in AI applications amongst orthodontists in the United Kingdom and Ireland. Materials and Methods: A cross-sectional study was conducted amongst orthodontists in the United Kingdom and Ireland. A self-reported questionnaire was used. Data was collected on participant demographics, as well as knowledge, usage, confidence, and future use of different AI applications. Pearson chi-square tests were used to assess if demographics, region of work, sector of work, and years of experience influenced responses. Results: A total of 331 responses were received. There was a general awareness that AI can be used in orthodontics in 80.4% of respondents. In addition, the overall mean knowledge, usage, and confidence levels of the examined AI applications were 51.3%, 16.6% and 18.7% respectively. Knowledge, usage, and confidence levels for specific AI applications differed, with the greatest familiarity, usage, and confidence observed in AI applications for cephalometric tracing and remote monitoring. Alternatively, the lowest awareness, usage, and confidence were attributed to AI applications that assisted in identifying the need for extractions. Additionally, most orthodontists (81%) consider AI to be beneficial for future use and the majority (96.7%) were open to learning about it. Statistically Significant associations (P >0.05) were discovered between knowledge, usage, and confidence in various AI applications and between multiple factors including healthcare sectors, practice regions, and gender. Conclusion: This study revealed differing levels of knowledge, usage, and confidence in various AI applications among practitioners in the UK and Ireland. The findings suggest a knowledge-implementation gap that might be beneficial to be targeted by educational means to increase the adoption of AI technology in the orthodontic practice.73 0Item Restricted Hijab Meets Style | Incorporating service design to simplify the shopping experience for Saudi Hijabi GenZ’s when abroad(University of the Arts in London, 2024-02-16) Sharaf Aldeen, Wejdan; Barber, SamThe realm of fashion expands beyond surface trends and visual appeal, serving as a convergence point for culture, identity, and individual beliefs (Davis, 2013). For Muslim women, the selection of clothing holds deep meaning, going beyond mere style to embody a balanced fusion of modesty and religious dedication (Bernier, 2022). In contemporary Islam, women wear the hijab, a veil covering their hair, and fashionably modest attire covering the chest, legs, and arms. (Rahman et al., 2016, p. 218). The growing modest fashion industry is driven by fashion-conscious Muslim consumers combining a symbol of adherence to Islamic principles with a modern interpretation of modesty that allows for self-expression within contemporary fashion trends (The Cooper Hewitt, 2021). The Global Islamic Economic 2019/20 study projects that Muslim consumer spending on clothing and shoes increased in 2018, reaching 283$ dollars and is projected to grow in 2024, reaching 402$ billion (Dinar Standard, 2019). Despite the growth and demand for modest wear, Madeeha Najeeb (2019) confronts the lack of availability for Hijabi clothing options in mainstream retail and emphasises the neglect of a substantial market gap. This challenges Muslims worldwide to undergo a time-consuming process of matching pieces from different local and international brands to create full hijabi wear, including the headscarf (Hassan & Harun, 2016). This dilemma frequently results in the repetition of outfits and discontent with their wardrobe selections, impacting their self-esteem and confidence level. Today, Gen Z Saudi Muslim women emerge as dominant consumers who prioritise fashionable clothing and seek recognition, diversity, and inclusivity from fast fashion brands. According to a study by Brand Genetics, the Saudi Gen Z generation is characterised by a «more liberal, risk-taking, entrepreneurial mindset» (Alexandra, 2021). Saudi Arabia stands as a major market in the domain of modest clothing. (Herrmann, 2022). Under the Saudi Vision 2030 initiative, significant legal reforms have taken place, marking progress in the status of women in Saudi Arabia by granting them the freedom to choose their clothing over the traditional Abaya and enabling them to travel abroad without strict guardianship (Sadek, 2019). Contemporary Saudi Gen Z women are empowered and expanding their horizons through travel while simultaneously aspiring to uphold their stylish religious identity. Thus, addressing this issue is crucial to empower Saudi Muslim women and help them access diverse Hijabi fashion choices, boost their confidence in navigating diverse foreign cultures abroad, foster a sense of belonging and inclusion, and simplify their shopping experience.42 0