Improving the prevention and diagnosis of melanoma on a national scale: A comparative study of performance in the United Kingdom and Australia

Papachristou, Ioannis; Bosanquet, Nick

doi:10.1057/s41271-019-00187-0

Cited by 18 publications

(10 citation statements)

References 26 publications

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“…(1) Twenty-two scientific papers [8,[21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40];…”

Section: Preliminary Considerations On the Resultsmentioning

confidence: 99%

“…The results highlighted both that the parameters related to user satisfaction were high and that these Apps based on AI solutions were promising and have potential for all the actors within the health domain. However, it should be noted that all the other 21 studies [8,[21][22][23][24][25][26][27][28][29][32][33][34][35][36][37][38][39][40][41] unanimously consider the application in mHealth of TD and AI as a promising future.…”

Section: The Applications In Mhealthmentioning

confidence: 99%

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The Artificial Intelligence in Teledermatology: A Narrative Review on Opportunities, Perspectives, and Bottlenecks

Giansanti

2023

IJERPH

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Artificial intelligence (AI) is recently seeing significant advances in teledermatology (TD), also thanks to the developments that have taken place during the COVID-19 pandemic. In the last two years, there was an important development of studies that focused on opportunities, perspectives, and problems in this field. The topic is very important because the telemedicine and AI applied to dermatology have the opportunity to improve both the quality of healthcare for citizens and the workflow of healthcare professionals. This study conducted an overview on the opportunities, the perspectives, and the problems related to the integration of TD with AI. The methodology of this review, following a standardized checklist, was based on: (I) a search of PubMed and Scopus and (II) an eligibility assessment, using parameters with five levels of score. The outcome highlighted that applications of this integration have been identified in various skin pathologies and in quality control, both in eHealth and mHealth. Many of these applications are based on Apps used by citizens in mHealth for self-care with new opportunities but also open questions. A generalized enthusiasm has been registered regarding the opportunities and general perspectives on improving the quality of care, optimizing the healthcare processes, minimizing costs, reducing the stress in the healthcare facilities, and in making citizens, now at the center, more satisfied. However, critical issues have emerged related to: (a) the need to improve the process of diffusion of the Apps in the hands of citizens, with better design, validation, standardization, and cybersecurity; (b) the need for better attention paid to medico-legal and ethical issues; and (c) the need for the stabilization of international and national regulations. Targeted agreement initiatives, such as position statements, guidelines, and/or consensus initiatives, are needed to ensure a better result for all, along with the design of both specific plans and shared workflows.

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“…(1) Twenty-two scientific papers [8,[21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40];…”

Section: Preliminary Considerations On the Resultsmentioning

confidence: 99%

Section: The Applications In Mhealthmentioning

confidence: 99%

The Artificial Intelligence in Teledermatology: A Narrative Review on Opportunities, Perspectives, and Bottlenecks

Giansanti

2023

IJERPH

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“…31 This is the first paper, to our knowledge, to capture the views of a diverse group of expert Australian stakeholders on skin cancer prevention. Previously, direct comparisons have been made with Australia on the prevention and diagnosis of melanoma 32 and policy framing of sun-safety. 33 There are a number of potential study limitations.…”

Section: Discussionmentioning

confidence: 99%

What can Aotearoa New Zealand learn from the Australian Sunsmart Story? A qualitative study

McNoe

Gage

Signal

2022

Australian and New Zealand Journal of Public Health

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Objective: To explore the views of stakeholders in Australia concerning skin cancer primary prevention and identify successful strategies used that may be translatable to other jurisdictions. Methods:In-depth stakeholder interviews with experts engaged in skin cancer prevention advocacy and action in Australia.Results: A number of important facilitators were identified including: the use of good scientific evidence (including economic), strong leadership, legislation and strategic documents, engaging the media particularly with the use of personal stories and garnering public support. A number of barriers were also identified including: a lack of funding (particularly nationally), variation by state, apathy and the long latency of skin cancer. Conclusions:Advocates identified a number of key strategies that were used to gain momentum in achieving Australia's comprehensive Sunsmart program. These included: strong leadership, legislation including that banning solaria and workplace health and safety legislation, a critical mass of key advocates from a range of disciplines including clinicians and patients, and the advantageous use of media to drive change. Implications for public health:Australia demonstrates what can be achieved when skin cancer prevention is taken seriously. The challenge for other nations is to apply the lessons learnt in Australia to our own jurisdictions.

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“…Hu et al (2019) applied a random forest (RF) model to identify seismic landslides in Jiuzhaigou and achieved an accuracy of 82.72%. Deep learning, an important branch of machine learning, has made numerous achievements in the eld of computer vision, particularly with convolutional neural networks (CNNs) (He et al 2017;Ren et al 2015;Papachristou et al 2020), which demonstrate strong feature extraction and learning capabilities. However, current research predominantly applies CNN models to image processing (Chauhan et al 2018;Sun et al 2020;Zhang et al 2017), with few reports on their application in non-image processing studies.…”

Section: Introductionmentioning

confidence: 99%

Construction and Application of Intelligent Landslide Warning System Based on Fusion of Multiple Source Data

Liu,

Xie,

Tang

et al. 2024

Preprint

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Deformation development of landslides is a complex process that usually requires the deployment of various monitoring devices on a slope to obtain data from multiple sources for effective analysis. Only through this process can an accurate judgment be made regarding the entire evolutionary trend of slope deformation. Monitoring data obtained through multiple sensors have a certain degree of correlation, randomness, and redundancy. The effective processing of these data is crucial for monitoring and providing warnings. Given that machine learning is adept at uncovering implicit patterns from heterogeneous data sources, it is well suited for predicting landslide deformation with multi-factor monitoring. The sample dataset forms the foundation for training the models, and the quality and quantity of the dataset directly affect its accuracy and generalization ability. However, significant deformation in landslide bodies is relatively rare, leading to an imbalance in the collected sample dataset. To address this issue, this study proposed the genetic algorithm improved multi-classification-genetic-synthetic minority oversampling technique (SMOTE)-algorithm (GAMCGSA). Building on the multi-classification-genetic-SMOTE-algorithm (MCGSA), it integrated genetic algorithms to determine the optimal sampling rate. Based on this rate, new samples were generated, avoiding the creation of a large number of synthetic samples and effectively addressing the issue of sample imbalance. Subsequently, a convolutional neural network (CNN) was employed to process non-image data from multiple sources, resulting in the development of an intelligent landslide warning model. According to the test results, the F1 score of this model reached 84.2% with an accuracy of 90.8%, surpassing those of traditional machine learning models. Based on the developed warning model and utilizing popular development frameworks, geographic information systems, and database technologies, an intelligent landslide monitoring warning system was constructed. This system integrates intelligent landslide monitoring and warning services, and provides scientific and reliable technical support for landslide disaster prevention and reduction.

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Improving the prevention and diagnosis of melanoma on a national scale: A comparative study of performance in the United Kingdom and Australia

Cited by 18 publications

References 26 publications

The Artificial Intelligence in Teledermatology: A Narrative Review on Opportunities, Perspectives, and Bottlenecks

The Artificial Intelligence in Teledermatology: A Narrative Review on Opportunities, Perspectives, and Bottlenecks

What can Aotearoa New Zealand learn from the Australian Sunsmart Story? A qualitative study

Construction and Application of Intelligent Landslide Warning System Based on Fusion of Multiple Source Data

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