Saudi Cultural Missions Theses & Dissertations
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Item Embargo Comparative Study of the Performance of an Artificial Intelligence Platform in Detecting Periapical Radiolucencies Across Different Imaging Modalities(Saudi Digital Library, 2025) Allihaibi, Marwa; Koller, Garrit; Mannocci, FrancescoAim: This thesis aimed to evaluate the diagnostic accuracy of a commercial artificial intelligence (AI) platform in detecting periapical radiolucencies (PARLs) across different imaging modalities. The evaluation included preoperative assessment of teeth requiring primary endodontic treatment with comparison against dental professionals, radiographic healing assessment at follow-up, and assessment of teeth referred for apical microsurgery. Methods: Five retrospective diagnostic accuracy studies were conducted to evaluate the commercial AI platform Diagnocat (versions 1.0 and 2.0) for PARL detection across multiple imaging modalities. The studies utilised radiographic data from patients treated at Guy's and St Thomas' NHS Foundation Trust between 2012-2023. The study sample included: (1) 339 teeth indicated for primary root canal treatment, assessed on periapical radiographs (PARs) and compared with two experienced endodontists; (2) 376 teeth assessed at minimum one-year follow-up on PARs for radiographic healing outcomes, compared with two endodontists; (3) 134 molars evaluated on cone-beam computed tomography (CBCT) for preoperative and postoperative assessment; (4) 177 posterior teeth requiring primary endodontic treatment, assessed on PARs and compared with eleven general dental practitioners (GDPs); and (5) 116 anterior teeth referred for apical microsurgery, evaluated on both PARs and CBCT. Reference standards varied by study design: CBCT for PAR validation, expert consensus for CBCT assessment, and histopathology for cases referred for apical microsurgery. Statistical analyses included calculation of sensitivity, specificity and accuracy with 95% confidence intervals. McNemar's test assessed diagnostic performance differences. Subgroup analyses examined performance across anatomical variables. Results: Across five retrospective studies, Diagnocat demonstrated significant performance variability dependent on imaging modality, anatomical location, and treatment status. On PARs, for non-root-filled teeth requiring primary root canal treatment, sensitivity was 47.9% and specificity 95.4%, indicating reliable exclusion of disease but missing over half of actual lesions. In root-filled teeth assessed at one-year follow-up, sensitivity increased to 67.3% while specificity decreased to 82.3%, suggesting altered diagnostic thresholds based on treatment status. Performance on CBCT scans of molars showed marked improvement, achieving 93.9% sensitivity and 65.2% specificity in preoperative cases, and 88.6% sensitivity and 63.3% specificity in follow-up cases. While three-dimensional (3D) imaging substantially enhanced sensitivity for posterior teeth, it was accompanied by reduced specificity, indicating potential for overdiagnosis. Anatomical analysis revealed consistent underperformance in maxillary teeth and specific roots on PARs, limitations that were largely resolved on CBCT for posterior teeth. In contrast, anterior teeth demonstrated persistently poor performance regardless of imaging modality, achieving only 63.8% sensitivity on PARs and 57.5% on CBCT despite histopathological confirmation of periapical pathology. Cross-modality consistency was poor, with only 43.8% of lesions detected on both imaging modalities. Compared to clinicians, Diagnocat showed lower sensitivity (47.9% vs 65.3%) but comparable specificity (95.4% vs 97.7%) when assessed against endodontists in non-root-filled teeth. In root-filled teeth, this pattern reversed, with the AI achieving higher sensitivity (67.3% vs 49.3%) but lower specificity (82.3% vs 92.5%). When compared with GDPs, Diagnocat demonstrated lower sensitivity (44.9% vs 80.8%) but markedly superior specificity (94.3% vs 47.5%). Re-evaluation with version 2.0 showed no improvement in PARL detection across 1,308 PARs and 268 CBCT scans. Conclusion: This thesis demonstrated that multiple factors critically determine AI diagnostic accuracy for PARL detection, including imaging modality, anatomical location, and treatment status, thus highlighting fundamental limitations in training data representation and model development. AI platforms require comprehensive training on datasets with balanced anatomical representation and the incorporation of three-dimensional imaging before being considered for reliable implementation in endodontic diagnosis.15 0Item Restricted The Accuracy of Diagnosing Salivary Gland Diseases by Artificial Intelligence: Systematic Review(Saudi Digital Library, 2025) Aljohani, Wejdan; Seoudi, Noha1.1 Purpose Artificial intelligence (AI) is increasingly applied in the diagnosis of salivary gland diseases, particularly Sjögren’s syndrome (SS) and salivary gland tumours (SGTs). This review aimed to evaluate the diagnostic performance of AI models in these two disease categories and identify converging patterns, limitations, and research gaps. 1.2 Method A systematic literature search was conducted in PubMed, Scopus, and Google Scholar over the past two decades (2005-2025) using predefined inclusion and exclusion criteria. Data extraction captured study design, input modality, AI model type, performance metrics (sensitivity, specificity, accuracy, AUC). Quality analysis was performed using JBI tool. Results were stratified by disease group (SS vs SGTs) and AI model type (Machine learning vs Deep learning). 1.3 Results A total of 19 studies were included from the 221 initially retrieved. Most of the included studies were assessed as moderate risk of bias, with only three low-risk and one high-risk. In SS studies , ML models showed excellent performance when applied to structured data. Logistic Regression emerged as the best-performing architecture, achieving accuracies up to 94% with AUC values ranging from 0.88 to 0.96. DL models on histopathology ranged from weak performance in baseline Residual CNNs (ResNet) (50% accuracy) to excellent outcomes with custom architectures such as CTG-PAM (100% across sensitivity, specificity, and accuracy). In SGTs, ML models on imaging inputs showed moderate ability, with Logistic Regression achieving 78–84% accuracy (AUC up to 0.91) and ultrasound reporting lower sensitivity but good specificity. DL approaches outperformed ML, particularly hybrid CNN–Transformers on MRI (85% accuracy, AUC 0.96; Liu et al., 2023) and Vision Transformers on ultrasound (87% accuracy, AUC 0.93; He et al., 2025). CNNs were more variable: Inception showed consistent results (73–85% accuracy, AUC up to 0.91), while ResNet and Densely Connected CNN (DenseNet) performance fluctuated widely even within the same input modality. 1.4 Conclusion AI demonstrates high potential in salivary gland disease diagnosis, with structured data input and custom-made models and advanced DL architectures yielding the most promising results. However, heterogeneity in input modalities and model design limits comparability, underscoring the need for standardised, multicentre validation.6 0Item Restricted The use and evaluation of integrated diagnostics in haematological malignancy(Manchester, 2020-09-30) Altowairqi, Ahlam; Byers, RichardBackground: Haematologic malignancies are one of the most challenging diagnostic fields. Many discrepancies have been reported in diagnoses of this disease over the last few years that affect patient outcomes. Such discrepancies have promoted the introduction of guidelines and classifications to improve identification and predict an optimal therapy approach. The Specialist Integrated Haematological Malignancy Diagnostic Services (SIHMDS), which was introduced in the UK as a special service for haematologic malignancies, to provide a second review, promote multiple techniques for proper diagnosis and prognosis and to choose the best treatment approach. Objectives: This study aims to identify the effectiveness of diagnosing lymphoma using SIHMDS in Manchester by comparing this service to local in MRI and other hospitals diagnoses. Also, it aims to identify the role of interpretation and diagnostic techniques in accurate lymphoma diagnosis. Methods: A total of 2,014 cases were collected from January 2019 to December 2019 from HODS system in SIHMDS. Among these, 295 cases were lymphoma cases and their details were taken from an integrated report. The primary diagnoses of these cases were taken to compare and identify discrepancies. Results: From 295 cases, the discrepancy was 10% in the total cases diagnosed, while the discrepancy rate was lower in MRI (7.4%), and higher in other hospitals by (29%). The role and need of the second review and multiple techniques were confirmed to improve the diagnoses in the special service. Also, needle core biopsy was shown to be useful in lymphoma diagnosis. Conclusion: Introducing special diagnostic service in haematologic malignancies is crucial due to the great benefits of second review and multiple advance techniques. New advances in this field will further improve the diagnosis sensitivity and further enhance patient outcomes.20 0Item Restricted Design and Development of Lateral Flow Test for Early Screening of Pancreatic Cancer(University College London (UCL), 2022) Sharief, Rimaz; Patra, Hirak; Acedo, Pilar; Chivu, Alex; Sampedro, AndresPancreatic cancer currently showing one of the lowest survival rates in the UK (~5%). Despite the huge efforts in improving the diagnosis using existing screening methods, an early stage detection remains a challenge. Developing a rapid, point-of-care lateral flow test (LFT) for patients is an unmet clinical goal that can accelerate the screening in clinical settings. The focus of my project is to design & develop a LFT based on carbohydrate/cancer antigen (CA 19-9) levels in the blood. My aim is to investigate and increase the sensitivity of the LFT by indicating high-low levels of CA 19-9 for regular screening and monitoring. This project involved 4 distinct stages: Optimizing the appropriate golden nanoparticles (Au NP) for their functionalization with different concentrations of PEG and MSA to get the best immunoassay. Different characterization methods including UV-Vis and DLS will be conducted to MSA and PEG Au NPs to observe the stability of Au NPs at different concentrations. Consequently, freeze-drying procedure will be conducted to ensure that PEG and MSA AuNPs sensitivity and reactivity is retained when in dried state and rehydration/rewetting. All MSA AuNP aggregated after resuspension. While 50 uM, and 100 uM PEG retained stability after resuspension. 50 uM PEG was selected for the functionalization of 22 nm AuNP (AuNP1) for further Anti-CA 19-9 monoclonal antibody covalent conjugation. Characterisation methods including UV-VIS, DLS, gel electrophoresis (agarose and ) indicated successful EDC/NHS conjugation. To assess the binding efficiency of Ab-AuNP on membrane strip; Supernatant was collected from cholangiocarcinoma cell lines and pancreatic cell lines at 80% confluency. The expression of CA 19-9 was evident in PDX-185 only. No test line were evident; during suspension of SFM PD-185 supernatant and Ab-AuNP1 on immobalised mAb. Hence, the Fabrication of the LFT Lastly was not achieved as Ab concentration optmisation during conjugation process is required to achieve the optimum sensitivity and the limit of detection for the LFT.19 0Item Restricted Diagnosis of Oral and maxillofacial cysts using artificial intelligence: a literature review(University of Manchester, 2024) Almohawis, Alhaitham; Yong, SinAbstract Oral and maxillofacial cysts are cavities that can pose significant risks if not detected and treated promptly. Many of these cysts are asymptomatic, often going unnoticed until complications arise. The introduction of artificial intelligence (AI) presents a promising opportunity for early detection and management of these cysts. Aim: To explore current studies on the use of artificial intelligence in diagnosing oral and maxillofacial cysts. Objectives: To examine the existing literature in this field, assess the accuracy, effectiveness, and limitations of AI models, and identify challenges in implementing AI in clinical practice. Methods: This literature review followed a systematic approach, identifying 223 studies from PUBMED and SCOPUS databases between 1975 and 2024. After applying inclusion and exclusion criteria, 26 retrospective cohort studies were included in the final analysis. A risk of bias assessment was conducted using the ROBINS I tool. Results: The investigation revealed that AI models consistently demonstrate high accuracy in detecting oral cysts in both radiographs and digital histopathology. The ROBINS I tool indicated a moderate risk of bias in most of the included studies. Notable limitations include limited datasets, variable data quality, and a lack of explainability in AI models results. Conclusion: AI models have shown considerable effectiveness and speed in detecting both simple and complex cysts. However, to fully leverage AI's potential in clinical settings, further rigorous studies are needed to evaluate its risks, benefits, and feasibility, ensuring compliance with governmental health regulations on AI.19 0Item Restricted Comprehensive Patient-Specific Prediction Models for Diagnosis and Prognosis of Temporoman-dibular Joint Osteoarthritis(Saudi Digital Library, 2023) Alturkestani, Najla; Cevidanes, LuciaOsteoarthritis is the most common degenerative joint disease, affecting 15% of the global popula-tion. Osteoarthritis in temporomandibular joint (TMJ OA) can cause chronic pain, facial deformi-ty, joint dysfunction, impacting the quality of life. Unlike weight-bearing joints, TMJ OA primar-ily affects individuals between the ages of 20 and 40 and can also appear in adolescents. Current standards for diagnosing TMJ OA rely on clinical and imaging criteria. However, these criteria have limited efficacy in detecting early-stage TMJ OA, posing challenges to timely inter-vention and mitigation of irreversible tissue damage. Hence, it becomes imperative to identify additional objective diagnostic criteria. In addition, determining which patients are at increased risk of disease progression is critical for making informed clinical decisions and designing more effective and individualized treatments. Radiomics is a newly established field propelled by advancements in computational power. It extracts quantitative imaging features from radiological images, aiming to identify subtle tissue variations and reduce subjectivity in image interpretation. Beyond radiomics, metabolic abnor-malities in joint tissues serve as early indicators of osteoarthritis. Although there has been pro-gress in studying osteoarthritis biomarkers, they have not yet been clinically established. Evaluat-ing multiple markers may reveal their intricate interrelations and fully harness their potential. With the advent of powerful machine learning (ML) methods, analysis of complex multisource data became feasible. Nevertheless, applying feature selection methods is crucial to eliminate re-dundant and irrelevant data, improving the output accuracy. Unlike knee osteoarthritis, which has been extensively studied using ML models, TMJ OA remains an underexplored area. Therefore, we aimed to 1) Develop a reliable prediction tool for TMJ OA progression and identify the con-tributing factors during a 2–3-year follow-up period, 2) Develop a comprehensive prediction tool tailored for TMJ OA diagnosis and use explainable methods to identify key factors driving diag-nosis, and 3) Investigate the feasibility of privileged learning in addressing missing data when diagnosing TMJ OA. We successfully developed an open-source tool which combined 18 feature selection and ML methods. This allowed for the prediction of disease progression with an accuracy=0.87, area un-der the ROC curve (AUC)=0.72, and an F1 score=0.82. Using the interpretable SHAP analysis method, we identified the strongest predictors for TMJ OA progression. These included: clinical (headache, lower back pain, restless sleep), quantitative imaging (condyle high-grey-level-run-emphasis (HGLRE), articular fossa GL-non-uniformity, and long-run-low-GLRE, joint space), and biological markers in saliva (Osteoprotegerin, Angiogenin, VEGF, and MMP-7) and serum samples (ENA-78). Utilizing clinical, CBCT imaging, and biological data from 162 prospectively recruited subjects, we evaluated 77 ML methods. Random forest demonstrated the best diagnostic performance, achieving AUC=0.90, accuracy=0.79, precision=0.80, and F1=0.80. The integration of clinical, imaging, and biological markers enhanced TMJ OA diagnosis. The top contributing features were clinical (headache, restless sleep, mouth opening, muscle soreness), objective quantitative imag-ing (condyle Cluster-Prominence, HGLRE, SRHGLRE, Trabecular Thickness), and biological markers in saliva (TGFB-1, TRANCE, TIMP-1, PAI-1, VECadherin, CXCL-16) and serum (An-giogenin, PAI-1, VEGF, TRANCE, TIMP-1, BDNF, VECadherin). Lastly, we developed the KRVFL+ diagnostic tool, which can be used when only clinical and imaging data are available. It achieved an AUC, specificity, and precision of 0.81, 0.79, and 0.77, respectively. Collectively, these efforts emphasize the immense potential of multi-source data and ML applica-tions in presenting solutions for predicting TMJ OA progression and diagnosis, with potential implications for timely interventions and a transformative impact on TMJ OA healthcare deliv-ery.33 0