Thomas R. Schopf

Consistency and Standardization of Color in Medical Imaging: a Consensus Report

This article summarizes the consensus reached at the Summit on Color in Medical Imaging held at the Food and Drug Administration (FDA) on May 8–9, 2013, co-sponsored by the FDA and ICC (International Color Consortium). The purpose of the meeting was to gather information on how color is currently handled by medical imaging systems to identify areas where there is a need for improvement, to define objective requirements, and to facilitate consensus development of best practices. Participants were asked to identify areas of concern and unmet needs. This summary documents the topics that were discussed at the meeting and recommendations that were made by the participants. Key areas identified where improvements in color would provide immediate tangible benefits were those of digital microscopy, telemedicine, medical photography (particularly ophthalmic and dental photography), and display calibration. Work in these and other related areas has been started within several professional groups, including the creation of the ICC Medical Imaging Working Group.

Web-based consultations for parents of children with atopic dermatitis: results of a randomized controlled trial

Aim: To analyse how web‐based consultations for parents of children with atopic dermatitis affect self‐management behaviour, health outcome, health resource use and family costs.

Automatic segmentation of dermoscopic images by iterative classification

Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.

A computer aided diagnostic system for malignant melanomas

We describe a system for automatic diagnosis of malignant melanoma based on digital dermoscopic images. The tool is designed for use with general practitioners, saving time and resources in the diagnostic process. A variety of indicative features are described mimicking the human approach for diagnosis. Segmentation, pattern recognition, and change detection are the important steps in our approach.

Computer-Aided Decision Support for Melanoma Detection Applied on Melanocytic and Nonmelanocytic Skin Lesions: A Comparison of Two Systems Based on Automatic Analysis of Dermoscopic Images

Commercially available clinical decision support systems (CDSSs) for skin cancer have been designed for the detection of melanoma only. Correct use of the systems requires expert knowledge, hampering their utility for nonexperts. Furthermore, there are no systems to detect other common skin cancer types, that is, nonmelanoma skin cancer (NMSC). As early diagnosis of skin cancer is essential, there is a need for a CDSS that is applicable to all types of skin lesions and is suitable for nonexperts. Nevus Doctor (ND) is a CDSS being developed by the authors. We here investigate NDs ability to detect both melanoma and NMSC and the opportunities for improvement. An independent test set of dermoscopic images of 870 skin lesions, including 44 melanomas and 101 NMSCs, were analysed by ND. Its sensitivity to melanoma and NMSC was compared to that of Mole Expert (ME), a commercially available CDSS, using the same set of lesions. ND and ME had similar sensitivity to melanoma. For ND at 95% melanoma sensitivity, the NMSC sensitivity was 100%, and the specificity was 12%. The melanomas misclassified by ND at 95% sensitivity were correctly classified by ME, and vice versa. ND is able to detect NMSC without sacrificing melanoma sensitivity.

Doctors and nurses benefit from interprofessional online education in dermatology

BackgroundBenefits of online learning in the health sector have been demonstrated in previous studies. We examined the potential benefits of a joint web-based curriculum on atopic eczema for health personnel.MethodsEnrolled doctors and nurses had access to the curriculum for 8 weeks. After the course learners completed a questionnaire. Two dermatologists rated the quality of the submitted homework assignments. Based on data from the projects budget and the Norwegian Medical Association, we estimated the saved travel expenses.ResultsEighty-eight learners (46 doctors) registered for the course. We received 55 questionnaires (response rate 63%). Twenty-seven learners (31%; 16 doctors, 11 nurses; χ2 = 0.03; P = 0.87) used the discussion forum. We found no significant differences in the total questionnaire scores between doctors and nurses. The homework assignments were given an average score of 3.6 for doctors and 3.5 for nurses (P = 0.8) by rater 1. Rater 2 scored 3.9 and 3.6 for doctors and nurses respectively (P = 0.2). The break-even between travel/hotel expenses and course development costs occurred at 135 saved travel refund applications.ConclusionsDoctors and nurses were equally satisfied with a joint web-based course on atopic eczema. The use of an online discussion forum was limited but similar between doctors and nurses. There were no significant differences in the quality of submitted homework assignments. The cost of developing the course was 716 841 NOK and the first 86 learners saved 455 198 NOK in travel expenses.

Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images

Melanoma is the deadliest form of skin cancer, and early detection is crucial for patient survival. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images, of which 75 were of melanomas, was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to participate in the challenge, and investigate the impact of segmentation and classifier. The diagnostic accuracy when using an automatic versus the semi-automatic/manual segmentation is investigated. The unexpected small impact of segmentation method suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have clinical impact, their performance should be ranked by a high-sensitivity measure.

Impact of Facebook Ads for Sexual Health Promotion Via an Educational Web App: A Case Study

Background: The authors present a case study of a public health campaign, including social media, and aiming at maximizing the use of web app on sexual health. Objective: To analyze the impact of a Facebook fan page, Facebook advertisements, and posters to maximize the number of visits to the educational web app. Methods: The campaign is assessed for 1 year, using data tracked through Facebook statistics and Google Analytics. Results: The site had 3670 visits 10.1 visitors/day, 95%CI 8.7-11.4. During the one-month Facebook Ads campaign, the site received 1263 visits 42.1 visitors/day, 95%CI 37.3-46.9, multiplying by over four the average number of visitors/day. 34.4% of all the participants were recruited during the one-month Facebook ads campaign. Conclusions: Facebook advertisements seem to be a good tool to promote an educational web app on sexual health targeting youth, and to reach a huge number of users rapidly and at a low cost.