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1. DiaGame: Serious and personalized game for self-management of diabetes

Author

Ferreira de Carvalho, D.F., Kaymak, U., Van Gorp, P., van Riel, Natal A.W., Hilbers, P., de Vries, R., Haak, H., van Pul, C., van Poppel, P., Reinders, E., Cruts, E., Information Systems IE&IS, EAISI Health, EAISI Foundational, JADS Den Bosch (TU/e), Systems Biology and Metabolic Disease, Eindhoven MedTech Innovation Center, Computational Biology, and Biomedical Engineering

Subjects
SDG 3 - Good Health and Well-being
Abstract

Diabetes is a well-known serious condition which its severity can be considerably reduced by a well-educated lifestyle. Learning how to adjust a patient’s daily behavior taking into account diet, exercise, or stress has a major part on diabetes education. When applied to healthcare, patient data can bring a step towards individualized patient treatments, as – through Artificial Intelligence (AI)/Machine Learning (ML) techniques – it can unfold a complex medical condition by considering not only biomedical, but lifestyle data. In DiaGame, the goal is to create a personalized layer over an educational digital game for diabetes patients, to make patients self-aware of their condition through a more familiar (customized) scenario based on their own data. Understanding, representing, and improving patients' knowledge through a data-driven approach is a step in the direction of a better disease management, and the game here acts as a tool supporting this objective. The project integrates: e-DES: The Eindhoven Diabetes Education Simulator, SugarVita, a game based on the former model, and GameBus, a data-integration application - developed at Eindhoven University of Technology (TU/e) - able to encourage the data gathering process involving patients. In-sync with patients data, the aforementioned tools will be tied to AI/ML techniques in order to generate a data-driven solution able to represent diabetic patients (concerning his/her condition and routine/reality) in a personalized educational game.

Published
2022

2. Interobserver agreement on line-field confocal optical coherence tomography image markers in keratinocyte carcinomas and precursor lesions.

Author

Jacobsen K, Ortner VK, Wenande E, Fredman G, Untracht GR, Wolswijk T, Cruts E, Mosterd K, Nielsen K, Philipsen PA, Wiegell SR, and Haedersdal M

Subjects
Humans, Microscopy, Confocal methods, Precancerous Conditions diagnostic imaging, Precancerous Conditions pathology, Female, Male, Aged, Middle Aged, Skin Neoplasms diagnostic imaging, Skin Neoplasms pathology, Skin Neoplasms diagnosis, Tomography, Optical Coherence methods, Observer Variation, Carcinoma, Basal Cell diagnostic imaging, Carcinoma, Basal Cell pathology, Carcinoma, Basal Cell diagnosis, Carcinoma, Squamous Cell diagnostic imaging, Carcinoma, Squamous Cell pathology, Keratinocytes pathology, Keratosis, Actinic diagnostic imaging, Keratosis, Actinic pathology, Keratosis, Actinic diagnosis
Abstract

Line-field confocal optical coherence tomography (LC-OCT) is a new technology for skin cancer diagnostics. However, the interobserver agreement (IOA) of known image markers of keratinocyte carcinomas (KC), including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), as well as precursors, SCC in situ (CIS) and actinic keratosis (AK), remains unexplored. This study determined IOA on the presence or absence of 10 key LC-OCT image markers of KC and precursors, among evaluators new to LC-OCT with different levels of dermatologic imaging experience. Secondly, the frequency and association between reported image markers and lesion types, was determined. Six evaluators blinded to histopathologic diagnoses, assessed 75 LC-OCT images of KC (21 SCC; 21 BCC), CIS (12), and AK (21). For each image, evaluators independently reported the presence or absence of 10 predefined key image markers of KCs and precursors described in an LC-OCT literature review. Evaluators were stratified by experience-level as experienced (3) or novices (3) based on previous OCT and reflectance confocal microscopy usage. IOA was tested for all groups, using Conger's kappa coefficient (κ). The frequency of reported image marker and their association with lesion-types, were calculated as proportions and odds ratios (OR), respectively. Overall IOA was highest for the image markers lobules (κ = 0.68, 95% confidence interval (CI) 0.57;0.78) and clefting (κ = 0.63, CI 0.52;0.74), typically seen in BCC (94%;OR 143.2 and 158.7, respectively, p < 0.001), followed by severe dysplasia (κ = 0.42, CI 0.31;0.53), observed primarily in CIS (79%;OR 7.1, p < 0.001). The remaining seven image-markers had lower IOA (κ = 0.06-0.32) and were more evenly observed across lesion types. The lowest IOA was noted for a well-defined (κ = 0.07, CI 0;0.15) and interrupted dermal-epidermal junction (DEJ) (κ = 0.06, CI -0.002;0.13). IOA was higher for all image markers among experienced evaluators versus novices. This study shows varying IOA for 10 key image markers of KC and precursors in LC-OCT images among evaluators new to the technology. IOA was highest for the assessments of lobules, clefting, and severe dysplasia while lowest for the assessment of the DEJ integrity., (© 2024. The Author(s).)

Published
2024
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