scholarly journals Mobile Artificial Intelligence Applications for Skin Cancer Diagnostics: Preferences and Concerns of Digital Natives (Preprint)

10.2196/22909 ◽  
2020 ◽  
Author(s):  
Sarah Haggenmüller ◽  
Eva Krieghoff-Henning ◽  
Tanja Jutzi ◽  
Nicole Trapp ◽  
Lennard Kiehl ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Skin Cancer ◽  
Digital Natives ◽  
Cancer Diagnostics

scholarly journals Mobile Artificial Intelligence Applications for Skin Cancer Diagnostics: Preferences and Concerns of Digital Natives (Preprint)

2020 ◽  
Author(s):  
Sarah Haggenmüller ◽  
Eva Krieghoff-Henning ◽  
Tanja Jutzi ◽  
Nicole Trapp ◽  
Lennard Kiehl ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Mobile Applications ◽  
Positive Attitude ◽  
Age Groups ◽  
Digital Natives ◽  
Cancer Diagnostics ◽  
Skin Cancer Detection

BACKGROUND Artificial Intelligence (AI) has shown potential to improve diagnostics of various diseases and especially early skin cancer detection. What is missing is the bridge from AI technology to clear application scenarios in clinical practice as well as added value for patients. Translation of AI-based diagnostic tools can only be successful if they are accepted by potential users. Young adults as digital natives may offer the greatest potential for successful implementation into clinical practice, while at the same time representing the future generation of skin cancer screening participants. OBJECTIVE We conducted an anonymous online survey to examine how and to what extent individuals are willing to accept AI-based mobile applications for skin cancer diagnostics. In this way, we evaluated the preferences as well as the relative influence of concerns with a special focus on younger age groups. METHODS We recruited respondents below 35 years of age through the social media channels Facebook, LinkedIn and Xing. Descriptive analysis and statistical tests were performed to evaluate participants’ attitudes towards mobile applications for skin examination. An adaptive choice-based conjoint (ACBC) was integrated to assess respondents’ preferences. Potential concerns were evaluated using maximum difference scaling (MaxDiff). RESULTS 728 respondents were included in the analysis. About 66.5% expressed a positive attitude towards the use of AI-based applications. In particular participants residing in big cities or small towns and individuals that were familiar with the use of health or tracking apps were significantly more open towards mobile diagnostic systems. Hierarchical Bayes estimation (HB) of the preferences of participants with positive attitude (n=484) revealed that the use of mobile applications as an assistance system was preferred. Respondents ruled out app versions with an accuracy of 65 percent or less, applications using data storage without encryption as well as systems that did not deliver background information about the decision-making. However, participants did not mind their data being used anonymously for research purposes, nor did they object to the inclusion of clinical patient information into the decision-making process. MaxDiff analysis for the negative-minded participant group (n=244) outlined that data security, insufficient trust in the app, as well as the lack of personal interaction represented the dominant concerns with respect to app use. CONCLUSIONS The majority of potential future users below 35 years of age was ready to accept AI-based diagnostic solutions for early skin cancer detection. However, for translation into clinical practice, participants’ demand for increased transparency and explainability of AI-based tools seems to be critical. Altogether, digital natives expressed similar preferences and concerns when compared to results obtained by previous studies that included other age groups.

scholarly journals Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective

2020 ◽  
Vol 7 ◽  
Author(s):  
Tanja B. Jutzi ◽  
Eva I. Krieghoff-Henning ◽  
Tim Holland-Letz ◽  
Jochen Sven Utikal ◽  
Axel Hauschild ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Skin Cancer ◽  
Cancer Diagnostics ◽  
Patients Perspective

Artificial Intelligence for Skin Cancer Detection: A Scoping Review (Preprint)

2020 ◽  
Author(s):  
Abdulrahman Takiddin ◽  
Jens Schneider ◽  
Yin Yang ◽  
Alaa Abd-Alrazaq ◽  
Mowafa Househ
Keyword(s):  
Artificial Intelligence ◽  
Skin Cancer ◽  
Scoping Review ◽  
Performance Metrics ◽  
Data Extraction ◽  
Characteristic Curve ◽  
Evaluation Metrics ◽  
Cancer Type ◽  
Computer Algorithms ◽  
Scoping Reviews

BACKGROUND Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, Artificial Intelligence (AI) tools are being used, including shallow and deep machine learning-based techniques that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. OBJECTIVE The aim of this study is to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examines the reliability of the selected papers by studying the correlation between the dataset size and number of diagnostic classes with the performance metrics used to evaluate the models. METHODS We conducted a systematic search for articles using IEEE Xplore, ACM DL, and Ovid MEDLINE databases following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. The study included in this scoping review had to fulfill several selection criteria; to be specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were conducted by two reviewers independently. Extracted data were synthesized narratively, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. RESULTS We retrieved 906 papers from the 3 databases, but 53 studies were eligible for this review. While shallow techniques were used in 14 studies, deep techniques were utilized in 39 studies. The studies used accuracy (n=43/53), the area under receiver operating characteristic curve (n=5/53), sensitivity (n=3/53), and F1-score (n=2/53) to assess the proposed models. Studies that use smaller datasets and fewer diagnostic classes tend to have higher reported accuracy scores. CONCLUSIONS The adaptation of AI in the medical field facilitates the diagnosis process of skin cancer. However, the reliability of most AI tools is questionable since small datasets or low numbers of diagnostic classes are used. In addition, a direct comparison between methods is hindered by a varied use of different evaluation metrics and image types.

scholarly journals Non-invasive skin sampling of tryptophan/kynurenine ratio in vitro towards a skin cancer biomarker

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Skaidre Jankovskaja ◽  
Johan Engblom ◽  
Melinda Rezeli ◽  
György Marko-Varga ◽  
Tautgirdas Ruzgas ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Relative Increase ◽  
Cancer Biomarker ◽  
Cancer Diagnostics ◽  
Sampling Time ◽  
Skin Permeability ◽  
Experimental Conditions ◽  
Non Invasive

AbstractThe tryptophan to kynurenine ratio (Trp/Kyn) has been proposed as a cancer biomarker. Non-invasive topical sampling of Trp/Kyn can therefore serve as a promising concept for skin cancer diagnostics. By performing in vitro pig skin permeability studies, we conclude that non-invasive topical sampling of Trp and Kyn is feasible. We explore the influence of different experimental conditions, which are relevant for the clinical in vivo setting, such as pH variations, sampling time, and microbial degradation of Trp and Kyn. The permeabilities of Trp and Kyn are overall similar. However, the permeated Trp/Kyn ratio is generally higher than unity due to endogenous Trp, which should be taken into account to obtain a non-biased Trp/Kyn ratio accurately reflecting systemic concentrations. Additionally, prolonged sampling time is associated with bacterial Trp and Kyn degradation and should be considered in a clinical setting. Finally, the experimental results are supported by the four permeation pathways model, predicting that the hydrophilic Trp and Kyn molecules mainly permeate through lipid defects (i.e., the porous pathway). However, the hydrophobic indole ring of Trp is suggested to result in a small but noticeable relative increase of Trp diffusion via pathways across the SC lipid lamellae, while the shunt pathway is proposed to slightly favor permeation of Kyn relative to Trp.

Perceptions of the use of Artificial Intelligence in the diagnosis of skin cancer: An outpatient survey

2021 ◽  
Author(s):  
K. Lim ◽  
G. Neal‐Smith ◽  
C. Mitchell ◽  
J. Xerri ◽  
P. Chuanromanee
Keyword(s):  
Artificial Intelligence ◽  
Skin Cancer

scholarly journals Cloud based mobile solution for early detection of Skin Cancer using Artificial Intelligence

2021 ◽  
pp. 312-324
Author(s):  
Pawan Sonawane ◽  
Sahel Shardhul ◽  
Raju Mendhe
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Skin Cancer ◽  
Early Detection ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Mobile Application ◽  
Low Cost ◽  
Skin Tumors

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.

scholarly journals The role of artificial intelligence in cancer diagnostics - a review

2021 ◽  
Vol 11 (9) ◽  
pp. 113-122
Author(s):  
Paweł Stanicki ◽  
Katarzyna Nowakowska ◽  
Michał Piwoński ◽  
Klaudia Żak ◽  
Sylwiusz Niedobylski ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Standardized Test ◽  
Magnetic Resonance Images ◽  
Cancer Diagnostics ◽  
Data Sets ◽  
Sensitive Information ◽  
Biopsy Material ◽  
Computer Data ◽  
Tumor Diagnostics ◽  
Abnormal Cells

Introduction and purposeArtificial intelligence (AI) is more advanced than ever and finds more and more new applications. Attempts are being made to use computer data analysis in medicine. The aim of this study is to summarize the knowledge on the use of AI in the diagnosis of breast, prostate, skin and colorectal cancer with particular emphasis on the applications and effectiveness of AI in making diagnoses. A brief description of the state of knowledgeThe most frequently used form of artificial intelligence in diagnostics are algorithms that analyze databases and recognize patterns. They can capture the features of samples characteristic of tumors, such as abnormal cells in the biopsy material or the alarming size and color of the skin lesion. Additionally, AI is capable of analyzing magnetic resonance images, radiographs, and other standardized test results. In most cases, AI is more effective than clinicians, sometimes as effective as they are, and almost never less effective. As a rule, the most accurate and adequate diagnosis can be obtained by joining the forces of AI and medical specialists. Working with learning algorithms requires the use of very extensive data sets. Every effort should be made to protect sensitive information from patients' medical history. ConclusionsThe results of research on the effectiveness of AI in cancer diagnostics are very promising. Further research and development of information technology systems may positively affect the quality and effectiveness of tumor diagnostics.

scholarly journals Detection of Breast Cancer using AI Techniques – A Survey

2019 ◽  
Vol 7 (7) ◽  
pp. 08-12
Author(s):  
Riju Bhattacharya ◽  
Diksha Gupta ◽  
Divyatara Rathod
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Cancer Diagnostics ◽  
Detection Methods ◽  
Health Issues ◽  
Process Data ◽  
Mortality And Morbidity ◽  
Medical Progress ◽  
Glass Slides ◽  
Abnormal Cells

Cancer refers to any one of a large number of diseases characterized by the development of abnormal cells that divide uncontrollably and have the ability to infiltrate and destroy normal body tissue.Without treatment, it can cause serious health issues andresult in a loss of life. Breast cancer is the most common cancer among women around the world. Despite enormous medical progress, breast cancer has still remained the second leading cause of death worldwide. Early detection of cancer may reduce mortality and morbidity. This paper presents a review of the detection methods for cancer through Artificial Intelligence (AI) in different ways. Previously Microscopic reviews of tissues on glass slides are used for cancer diagnostics to improve diagnostic accuracy. We can use different techniques such as digital imaging and artificial intelligence algorithm. Cancer care is also advancing thanks to AI’s ability to collect and process data. Due to the nature of processing this information, the task is often a time-consuming and tedious job for doctors. This process may be made much easier, quicker and efficient through the advancement as well as by using modified technologies.

scholarly journals AUTOMATED DETECTION SYSTEM FOR LUNG MASSES AS A STAGE OF ARTIFICIAL INTELLIGENCE DEVELOPMENT IN THE DIAGNOSTICS OF LUNG CANCER

2018 ◽  
pp. 62-68 ◽  
Author(s):  
B. M. Moiseenko ◽  
A. A. Meldo ◽  
L. V. Utkin ◽  
I. Yu. Prokhorov ◽  
M. A. Ryabinin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Industrial Revolution ◽  
Detection System ◽  
Lung Nodule ◽  
Cancer Diagnostics ◽  
Digital Information ◽  
Intellectual Activity ◽  
Domestic Literature ◽  
Modern Computer

In the century of the fourth industrial revolution, there is a rapid progress of technological developments in medicine. Possibilities of collecting large amounts of digital information and the modern computer capacity growth are reasons for the increased attention to artificial intelligence (AI) and its role in the diagnostics and the prediction of diseases. In the diagnostics, AI aims to model the human intellectual activity, providing assistance to a practicing doctor in the processing of big data. Development of AI can be considered as a way for implementation and ensuring of national political and economic interests in the health care improvement. Lung cancer is on the first position of cancer incidences. This implies that the development and implementation of computed-aided systems for lung cancer diagnostic is very urgent and important. The article presents the results concerning the development of a computed-aided system for the lung nodule detection, which is based on the processing of computed tomography data. Perspectives of the AI application to the lung cancer diagnostics are discussed. There is a few information about a role of Russian developments in this area in foreign and domestic literature.

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