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Title: Dermato-informatic approaches to understanding and improving lesional diagnostic expertise in cutaneous oncology
Author: Aldridge, Roger Benjamin Lochore
ISNI:       0000 0004 7230 0504
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2018
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Cutaneous malignancies represent a quarter of all new cancer diagnoses in the UK. The key to reducing the tumours’ associated mortality and morbidity is early diagnosis and treatment. Prompt diagnosis remains predominately a clinical skill, but relatively little investigation of the cognitive psychology underpinning expertise in this domain has been undertaken. This thesis aims to improve understanding of these processes and investigate how lesional diagnostic expertise might be enhanced. A large database of diagnostically tagged images was captured specifically for this project. A series of separate studies were undertaken to give insight into how lesional diagnosis occurs and how it can be improved. The studies highlighted that non-analytical pattern recognition (NAPR) is likely to predominate in distinguishing malignant and non-malignant skin lesions and that the widely-promoted rules advocating analytical pattern recognition (APR) are not effective for discriminating melanoma from benign pigmented lesions. The keystone to promoting the development of NAPR and thus diagnostic expertise would seem to be increasing a novice’s personal library of examples with relevant feedback. Studies demonstrated that current undergraduate exposure was variable but universally sparse, so simulation by way of diagnostically tagged images was developed which showed accuracy could be improved by increased exposure. This improvement occurred in both a content specific and dose responsive manner. These studies also highlighted that the learning curves for skin lesions are not uniform. Further studies demonstrated that the choice of images had implications on the development of diagnostic expertise; suggesting it was important that these images represent clinical practice rather than “classic” examples traditionally advocated for teaching purposes. In addition, studies highlighted the potential benefit of the 3D models developed during this project. Building on the idea that a personal catalogue of relevant referent images was crucial to enhanced diagnostic accuracy, prototype software was developed to exteriorise the experts’ library of examples; in the tests described novices utilising the software delivered superior accuracy than medical students on the completion of their undergraduate teaching. In summation, the work described shows that by utilising dermato-informatic approaches lesional diagnostic competence can be improved significantly.
Supervisor: Rees, Jonathan Sponsor: Wellcome Trust
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: skin cancer ; early detection ; skin lesions ; diagnosis ; imaging techniques ; dermato-informatics