scholarly journals eSkin: Study on the Smartphone Application for Early Detection of Malignant Melanoma

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Joanna Jaworek-Korjakowska ◽  
Pawel Kleczek
Keyword(s):  
Malignant Melanoma ◽  
Early Detection ◽  
Digital Camera ◽  
Diagnostic System ◽  
Smartphone Application ◽  
Skin Lesions ◽  
Application Architecture ◽  
Primary Care Doctors ◽  
Application Requirements ◽  
Rapid Deployment

Background. Malignant melanoma is among the fastest increasing malignancies in many countries. With the help of new tools, such as teledermoscopy referrals between primary healthcare and dermatology clinics, the diagnosis of these patients could be made more efficient. The introduction of a high-quality smartphone with a built-in digital camera may make the early detection more convenient. This study presents novel directions for early detection of malignant melanoma based on a smartphone application. Objectives and Methods. In this study, we concentrate on a precise description of a complex infrastructure of a fully automated computer-aided diagnostic system for early detection of malignant melanoma. The framework has been customized for a dermoscope that is customized to attach to the smartphone to be able to carry out mobile teledermoscopy. The application requirements, architecture, and computational methods as well as behavioral and dynamic aspects have been presented in this paper. Conclusion. This paper presents a broad application architecture, which can be easily customized for rapid deployment of a sophisticated health application. Mobile teledermoscopy is a new horizon that might become in the future the basis of the early detection of pigmented skin lesions as a screening tool for primary care doctors and inexperienced dermatologists.

Does the initial biopsy type affect clinically important outcomes in malignant melanoma? A meta-analysis.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e21041-e21041
Author(s):  
Fatima Fayyaz ◽  
Richard A Shellenberger
Keyword(s):  
Malignant Melanoma ◽  
Early Detection ◽  
Risk Ratio ◽  
Meta Analysis ◽  
Excisional Biopsy ◽  
Skin Lesions ◽  
Pigmented Lesions ◽  
Significant Rate ◽  
All Cause Mortality ◽  
Specific Mortality

e21041 Background: Malignant melanoma continues to have an increasing incidence worldwide without a decline in mortality, despite advances in treatment and early detection which have led to improved mortality outcomes for most malignancies. Early detection is particularly favorable for melanoma localized to the site of disease, which confers a five year survival rate of 98.4 %. Guidelines from the American Academy of Dermatology (AAD) give three acceptable options for the initial management of pigmented lesions suspicious for melanoma: elliptical excision, wide punch excision and a deep shave or saucerization. Methods: We performed a systematic review and meta-analysis to better define the evidence for differences between punch incisional and excisional biopsy with regard to clinically important outcomes in the evaluation of skin lesions suspicious for melanoma. These were melanoma specific mortality, all-cause mortality, Breslow tumor thickness, and melanoma recurrence. The comparison groups were punch incisional and excisional biopsy; there was insufficient data to include shave biopsies. Results: The result of pooling the studies that track melanoma specific mortality finds that there is a higher, but non-significant rate of death among those in the punch incisional group. The pooled risk ratio is 1.21, p = 0.153. The results of pooling the all-cause mortality studies also finds a higher, but non-significant, rate of death among the punch incisional group, RR = 1.03, p = 0.390. Pooling the two studies that examine Breslow thickness found that values in the punch incisional group are significantly lower, with a standardized mean difference of -0.17, p = 0.006. Finally, the pooled risk ratio for recurrence was not significant, RR = 1.161, p = 0.198. Conclusions: Until further data is available, there is no evidence to suggest a preferred diagnostic procedure in the initial evaluation of pigmented lesions suspected of cutaneous melanoma related to clinically important outcomes. To our knowledge, this is the first meta-analysis done on this important question regarding melanoma epidemiology and public health.

scholarly journals Primary Oral Malignant Melanoma: Two Case Reports and Review of Literature

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Neeraj Sharma
Keyword(s):  
Malignant Melanoma ◽  
Early Detection ◽  
Oral Cavity ◽  
Tumor Size ◽  
Malignant Tumors ◽  
Case Reports ◽  
Review Of Literature ◽  
Oral Region ◽  
Rare Neoplasm ◽  
Oral Malignant Melanoma

Primary malignant melanoma of the oral cavity is a rare neoplasm. The tumors tend to metastasize or locally invade tissue more readily than other malignant tumors in the oral region. The survival of patients with mucosal melanomas is less than for those with cutaneous melanomas. Tumor size and metastases are related to the prognosis of the disease. Early detection, therefore, is important.

scholarly journals Redesigning Skin Cancer Early Detection and Care Using a New Mobile Health Application: Protocol of the SKIN Research Project, a Randomised Controlled Trial

Dermatology ◽  
2018 ◽  
Vol 235 (1) ◽  
pp. 11-18 ◽  
Author(s):  
Monika Janda ◽  
Caitlin Horsham ◽  
Uyen Koh ◽  
Nicole Gillespie ◽  
Lois J. Loescher ◽  
...  
Keyword(s):  
Randomised Controlled Trial ◽  
Early Detection ◽  
Controlled Trial ◽  
Skin Lesions ◽  
Mobile Technologies ◽  
Whole Body ◽  
Naked Eye ◽  
Cancer Early Detection ◽  
Reported Data ◽  
Randomised Controlled

Patients often detect melanoma themselves; therefore, regular skin self-examinations (SSEs) play an important role in the early detection and prompt treatment of melanoma. Mobile teledermoscopy is a technology that may facilitate consumer SSEs and rapid communication with a dermatologist. This paper describes the planned randomised controlled trial of an intervention to determine whether mobile technologies can help improve the precision of SSE in consumers. A randomised controlled trial will be conducted to evaluate mobile teledermoscopy-enhanced SSE versus naked-eye SSE. Participants in each group will conduct three home whole-body SSEs at baseline, 1 and 2 months, then present for a clinical skin examination (CSE) by a doctor after the 2-month SSE. Specifically, participants will identify skin lesions that meet the AC (asymmetry and colour) rule for detecting a suspicious skin spot. The primary outcomes are sensitivity and specificity of the skin lesions selected by the participants as needing attention by a doctor, compared to the clinical diagnosis by the dermatologist that will serve as the reference standard for this analysis. For the mobile teledermoscopy-enhanced SSE group, researchers will assess the number, location and type of lesions (1) sent by the participant via mobile teledermoscopy, (2) found at CSE or (3) missed by the participant. For the naked-eye SSE group, researchers will assess the number, location and type of lesions (1) recorded on their body chart by the participant, (2) found at CSE or (3) missed by the participant. Secondary outcomes are based on participants’ self-reported data via online questionnaires.

scholarly journals SELECTED APPLICATIONS OF DEEP NEURAL NETWORKS IN SKIN LESION DIAGNOSTIC

2021 ◽  
Vol 11 (4) ◽  
pp. 18-21
Author(s):  
Magdalena Michalska
Keyword(s):  
Neural Networks ◽  
Malignant Melanoma ◽  
Skin Lesion ◽  
Deep Neural Networks ◽  
Skin Lesions ◽  
Multiclass Classification ◽  
Features Selection ◽  
Lesion Segmentation ◽  
Dermatological Diseases ◽  
Segmentation Process

The article provides an overview of selected applications of deep neural networks in the diagnosis of skin lesions from human dermatoscopic images, including many dermatological diseases, including very dangerous malignant melanoma. The lesion segmentation process, features selection and classification was described. Application examples of binary and multiclass classification are given. The described algorithms have been widely used in the diagnosis of skin lesions. The effectiveness, specificity, and accuracy of classifiers were compared and analysed based on available datasets.

scholarly journals MALIGNANT MELANOMA AND PREGNANCY

2021 ◽  
Vol 68 (2) ◽  
pp. 143-146
Author(s):  
Delia Cudalbă ◽  
◽  
Nicolae Gică ◽  
Radu Botezatu ◽  
Corina Gică ◽  
...  
Keyword(s):  
Malignant Melanoma ◽  
Poor Prognosis ◽  
Skin Lesions ◽  
Fetal Medicine ◽  
Treatment Need ◽  
Pigmented Skin Lesions ◽  
Pregnant State ◽  
State Diagnosis ◽  
Patients With Poor Prognosis ◽  
In Pregnancy

Malignant melanoma is one of the most frequent cancers diagnosed during pregnancy. Any pigmented skin lesions that change the color should be examined by an experienced dermatologist and if suspected, should be biopsied. Recent studies showed that malignant melanoma in pregnancy has not a worse outcome compared with non-pregnant state. Diagnosis of melanoma does not require an early delivery excepted pregnant patients with poor prognosis that need more aggressive treatment. Diagnosis and treatment need to be established in specialized centers with a multidisciplinary team. Pregnancy monitoring should be performed by team consisting of an obstetrician, a neonatologist and a specialist in fetal medicine.

Early Detection of Parkinson's Disease

2019 ◽  
pp. 61-95
Author(s):  
Debashree Devi ◽  
Saroj K. Biswas ◽  
Biswajit Purkayastha
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Detection ◽  
Neurodegenerative Disorder ◽  
Diagnostic System ◽  
Diagnostic Model ◽  
Neural Signals ◽  
Learning Machine ◽  
Preprocessing Technique ◽  
Thalamic Region

Parkinson's disease (PD) is a neurodegenerative disorder that occurs due to corrosion of the substantia nigra, located in the thalamic region of the human brain, and is responsible for transmission of neural signals throughout the human body by means of a brain chemical, termed as “dopamine.” Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Through this chapter, an intelligent diagnostic system is proposed by integrating one-class SVM, extreme learning machine, and data preprocessing technique. The proposed diagnostic model is validated with six existing techniques and four learning models. The experimental results prove the combination of proposed method with ELM learning model to be highly effective in case of early detection of Parkinson's disease, even in presence of underlying data issues.

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