Awareness of Skin Self-Assessment as an Early Detection Tool for Skin Cancer

The vast majority of skin cancer deaths are from melanoma, with about 1.04 million cases annually. Early detection of the same can be immensely helpful in order to try to cure it. But most of the diagnosis procedures are either extremely expensive or not available to a vast majority, as these centers are concentrated in urban regions only. Thus, there is a need for an application that can perform a quick, efficient, and low-cost diagnosis. Our solution proposes to build a server less mobile application on the AWS cloud that takes the images of potential skin tumors and classifies it as either Malignant or Benign. The classification would be carried out using a trained Convolution Neural Network model and Transfer learning (Inception v3). Several experiments will be performed based on Morphology and Color of the tumor to identify ideal parameters.

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Interest of a Joint Use of Two Diagnostic Tools of Burnout: Comparison between the Oldenburg Burnout Inventory and the Early Detection Tool of Burnout Completed by Physicians

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph181910544 ◽

2021 ◽

Vol 18 (19) ◽

pp. 10544

Author(s):

Céline Leclercq ◽

Lutgart Braeckman ◽

Pierre Firket ◽

Audrey Babic ◽

Isabelle Hansez

Keyword(s):

Early Detection ◽

Diagnostic Accuracy ◽

General Practitioners ◽

The Self ◽

Added Value ◽

Diagnostic Tools ◽

Occupational Physicians ◽

Detection Tool ◽

Interview Guide ◽

Oldenburg Burnout Inventory

Most research on burnout is based on self-reported questionnaires. Nevertheless, as far as the clinical judgement is concerned, a lack of consensus about burnout diagnosis constitutes a risk of misdiagnosis. Hence, this study aims to assess the added value of a joint use of two tools and compare their diagnostic accuracy: (1) the early detection tool of burnout, a structured interview guide, and (2) the Oldenburg burnout inventory, a self-reported questionnaire. The interview guide was tested in 2019 by general practitioners and occupational physicians among 123 Belgian patients, who also completed the self-reported questionnaire. A receiver operating characteristic curve analysis allowed the identification of a cut-off score for the self-reported questionnaire. Diagnostic accuracy was then contrasted by a McNemar chi-squared test. The interview guide has a significantly higher sensitivity (0.76) than the self-reported questionnaire (0.70), even by comparing the self-reported questionnaires with the interviews of general practitioners and occupational physicians separately. However, both tools have a similar specificity (respectively, 0.60–0.67), except for the occupational physicians’ interviews, where the specificity (0.68) was significantly lower than the self-reported questionnaire (0.70). In conclusion, the early detection tool of burnout is more sensitive than the Oldenburg burnout inventory, but seems less specific. However, by crossing diagnoses reported by patients and by physicians, they both seem useful to support burnout diagnosis.

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