SACM - United Kingdom
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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 New Market Entry Strategy(University of Strathclyde, 2024-07-04) Bakhsh, Anas; Bakhsh, Muaaz; Morrison, IainInfinityBlu Dental Clinic, a successful dental care provider from Scotland, commissioned this strategic consultancy engagement to plan its expansion into the London market. They want to target wealthy neighbourhoods that lack high-end dental care options. Our in-depth study gave data-driven insights on the best locations, services, office design, amenities and marketing strategies to achieve lasting profits and stand out from competitors. We recommend a luxury boutique feel with lavish, hotel-like decor, spa services, and custom personal options to impress demanding clients continually. We also advise specialised dental, cosmetic, and advanced treatments tailored to attract wealthy beauty-focused customers. Additional offerings, such as cafe and pharmacy can further increase income. An integrated marketing plan focused on one-on-one care, cutting-edge technologies and modern style is outlined to inform locals of InfinityBlu's unique features. If our advice on location, office environment, services and messaging is followed diligently, we project steady profits within 10 years. The full analysis has more details on launching and positioning for success23 0