The use of teledermatology for the diagnosis of skin cancer in adults

Background Early accurate detection of all skin cancer types is essential to guide appropriate management and to improve morbidity and survival. Melanoma and squamous cell carcinoma (SCC) are high risk skin cancers which have the potential to metastasise and ultimately lead to death, whereas basal cell carcinoma (BCC) is usually localised with potential to infiltrate and damage surrounding tissue. Anxiety around missing early curable cases needs to be balanced against inappropriate referral and unnecessary excision of benign lesions. Teledermatology provides a way for generalist clinicians to access the opinion of a specialist dermatologist for skin lesions that they consider to be suspicious without referring the patients concerned through the normal referral pathway. Teledermatology consultations can be ‘store-and-forward’ with electronic digital images of a lesion sent to a dermatologist for review at a later time, or can be live and interactive consultations using video conferencing to connect the patient, referrer and dermatologist in real time. Objectives To determine the diagnostic accuracy of teledermatology for the detection of any skin cancer (melanoma, BCC or cSCC) inNIH

Towards an Effective Imaging-Based Decision Support System for Skin Cancer

The usage of expert systems to aid in medical decisions has been employed since 1980s in distinct ap plications. With the high demands of medical care and limited human resources, these technologies are required more than ever. Skin cancer has been one of the pathologies with higher growth, which suf fers from lack of dermatology experts in most of the affected geographical areas. A permanent record of examination that can be further analyzed are medical imaging modalities. Most of these modalities were also assessed along with machine learning classification methods. It is the aim of this research to provide background information about skin cancer types, medical imaging modalities, data mining and machine learning methods, and their application on skin cancer imaging, as well as the disclosure of a proposal of a multi-imaging modality decision support system for skin cancer diagnosis and treatment assessment based in the most recent available technology. This is expected to be a reference for further implementation of imaging-based clinical support systems.info:eu-repo/semantics/publishedVersio

Fuzzy C-Means Clustering with Histogram based Cluster Selection for Skin Lesion Segmentation using Non-Dermoscopic Images

Purpose – Pre-screening of skin lesion for malignancy is highly demanded as melanoma being a life-threatening skin cancer due to unpaired DNA damage. In this paper, lesion segmentation based on Fuzzy C-Means clustering using non-dermoscopic images has been proposed. Design/methodology/approach – The proposed methodology consists of automatic cluster selection for FCM using the histogram property. The system used the local maxima along with Euclidean distance to detect the binomial distribution property of the image histogram, to segment the melanoma from normal skin. As the Value channel of HSV color image provides better and distinct histogram distribution based on the entropy, it has been used for segmentation purpose. Findings – The proposed system can effectively segment the lesion region from the normal skin. The system provides a segmentation accuracy of 95.69 % and the comparative analysis has been performed with various segmentation methods. From the analysis, it has been observed that the proposed system can effectively segment the lesion region from normal skin automatically. Originality/Value – This paper suggests a new approach for skin lesion segmentation based on FCM with automatic cluster selection. Here, different color channel has also been analyzed using entropy to select the better channel for segmentation. In future, the classification of melanoma from benign naevi can be performed

Dermoscopy for melanoma detection and triage in primary care: a systematic review.

OBJECTIVE: Most skin lesions first present in primary care, where distinguishing rare melanomas from benign lesions can be challenging. Dermoscopy improves diagnostic accuracy among specialists and is promoted for use by primary care physicians (PCPs). However, when used by untrained clinicians, accuracy may be no better than visual inspection. This study aimed to undertake a systematic review of literature reporting use of dermoscopy to triage suspicious skin lesions in primary care settings, and challenges for implementation. DESIGN: A systematic literature review and narrative synthesis. DATA SOURCES: We searched MEDLINE, Cochrane Central, EMBASE, Cumulative Index to Nursing and Allied Health Literature, and SCOPUS bibliographic databases from 1 January 1990 to 31 December 2017, without language restrictions. INCLUSION CRITERIA: Studies including assessment of dermoscopy accuracy, acceptability to patients and PCPs, training requirements, and cost-effectiveness of dermoscopy modes in primary care, including trials, diagnostic accuracy and acceptability studies. RESULTS: 23 studies met the review criteria, representing 49 769 lesions and 3708 PCPs, all from high-income countries. There was a paucity of studies set truly in primary care and the outcomes measured were diverse. The heterogeneity therefore made meta-analysis unfeasible; the data were synthesised through narrative review. Dermoscopy, with appropriate training, was associated with improved diagnostic accuracy for melanoma and benign lesions, and reduced unnecessary excisions and referrals. Teledermoscopy-based referral systems improved triage accuracy. Only three studies examined cost-effectiveness; hence, there was insufficient evidence to draw conclusions. Costs, training and time requirements were considered important implementation barriers. Patient satisfaction was seldom assessed. Computer-aided dermoscopy and other technological advances have not yet been tested in primary care. CONCLUSIONS: Dermoscopy could help PCPs triage suspicious lesions for biopsy, urgent referral or reassurance. However, it will be important to establish further evidence on minimum training requirements to reach competence, as well as the cost-effectiveness and patient acceptability of implementing dermoscopy in primary care. TRIAL REGISTRATION NUMBER: CRD42018091395.CRU

Hyperspectral Imaging Reveals Spectral Differences and Can Distinguish Malignant Melanoma from Pigmented Basal Cell Carcinomas : A Pilot Study

Pigmented basal cell carcinomas can be difficult to distinguish from melanocytic tumours. Hyperspectral imaging is a non-invasive imaging technique that measures the reflectance spectra of skin in vivo. The aim of this prospective pilot study was to use a convolutional neural network classifier in hyperspectral images for differential diagnosis between pigmented basal cell carcinomas and melanoma. A total of 26 pigmented lesions (10 pigmented basal cell carcinomas, 12 melanomas in situ, 4 invasive melanomas) were imaged with hyperspectral imaging and excised for histopatho-logical diagnosis. For 2-class classifier (melano-cytic tumours vs pigmented basal cell carcinomas) using the majority of the pixels to predict the class of the whole lesion, the results showed a sensitivity of 100% (95% confidence interval 81-100%), specificity of 90% (95% confidence interval 60-98%) and positive predictive value of 94% (95% confidence interval 73-99%). These results indicate that a convolutional neural network classifier can differentiate melanocytic tumours from pigmented basal cell carcinomas in hyperspectral images. Further studies are warranted in order to confirm these preliminary results, using larger samples and multiple tumour types, including all types of melanocytic lesions.Peer reviewe

Machine learning methods for binary and multiclass classification of melanoma thickness From dermoscopic images

Thickness of the melanoma is the most important factor associated with survival in patients with melanoma. It is most commonly reported as a measurement of depth given in millimeters (mm) and computed by means of pathological examination after a biopsy of the suspected lesion. In order to avoid the use of an invasive method in the estimation of the thickness of melanoma before surgery, we propose a computational image analysis system from dermoscopic images. The proposed feature extraction is based on the clinical findings that correlate certain characteristics present in dermoscopic images and tumor depth. Two supervised classification schemes are proposed: a binary classification in which melanomas are classified into thin or thick, and a three-class scheme (thin, intermediate, and thick). The performance of several nominal classification methods, including a recent interpretable method combining logistic regression with artificial neural networks (Logistic regression using Initial variables and Product Units, LIPU), is compared. For the three-class problem, a set of ordinal classification methods (considering ordering relation between the three classes) is included. For the binary case, LIPU outperforms all the other methods with an accuracy of 77.6%, while, for the second scheme, although LIPU reports the highest overall accuracy, the ordinal classification methods achieve a better balance between the performances of all classes

Smartphone applications for triaging adults with skin lesions that are suspicious for melanoma

Background Melanoma accounts for a small proportion of all skin cancer cases but is responsible for the majority of skin cancer-related deaths. Early detection and treatment can improve survival. Smartphone applications are readily accessible and potentially offer an instant risk assessment of the likelihood of malignancy, so that the right people seek further medical attention from a clinician for more detailed assessment of the lesion. There is, however, a risk that melanomas will be missed and treatment delayed if the application reassures the user that their lesion is low risk. Objectives To determine the diagnostic accuracy of smartphone applications to rule out cutaneous invasive melanoma and intraepidermal melanocytic variants in adults with concerns about suspicious skin lesions.NIH