Fighting Melanoma with Smartphones: A Snapshot of Where We are a Decade after App Stores Opened Their Doors

Ngoo, Alexander; Finnane, Anna; McMeniman, Erin; Soyer, H. Peter; Janda, Monika

doi:10.1016/j.ijmedinf.2018.08.004

Cited by 30 publications

(39 citation statements)

References 14 publications

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“…However, this is a rapidly evolving field, suggesting frequent re-evaluations are crucial. 1 In this study, we show that the new algorithm for risk assessment has a high accuracy for self-assessment of skin lesions for skin cancer. This result is comparable to some of the recently published, best skin cancer disease classification algorithms.…”

Section: Discussionmentioning

confidence: 70%

“…Previous studies on the usage of SAs to detect skin cancer, which included previous versions of this SA and its competitors, criticized their low capability to detect skin cancer. However, this is a rapidly evolving field, suggesting frequent re‐evaluations are crucial . In this study, we show that the new algorithm for risk assessment has a high accuracy for self‐assessment of skin lesions for skin cancer.…”

Section: Discussionmentioning

confidence: 81%

“…Access to medical care is being transformed with the advent of mobile applications which can monitor, diagnose and provide health information. In the field of dermatology, there is considerable interest in developing algorithms for image analysis of skin lesions to evaluate the risk of malignant melanoma (MM) and keratinocyte carcinomas (KC) . Early detection of skin cancer is important since the prognosis is highly dependent on whether the cancer is detected at an early stage, when it is easier to treat and its prognosis is more favourable…”

Section: Introductionmentioning

confidence: 99%

“…In the field of dermatology, there is considerable interest in developing algorithms for image analysis of skin lesions to evaluate the risk of malignant melanoma (MM) and keratinocyte carcinomas (KC). [1][2][3][4] Early detection of skin cancer is important since the prognosis is highly dependent on whether the cancer is detected at an early stage, when it is easier to treat and its prognosis is more favourable. 5,6 Smartphones equipped with applications that analyse skin lesions can become a powerful tool for early detection of skin cancer.…”

Section: Introductionmentioning

confidence: 99%

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Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms

Udrea

Mitra

Costea

et al. 2019

Acad Dermatol Venereol

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Background Machine learning algorithms achieve expert‐level accuracy in skin lesion classification based on clinical images. However, it is not yet shown whether these algorithms could have high accuracy when embedded in a smartphone app, where image quality is lower and there is high variability in image taking scenarios by users. In the past, these applications were criticized due to lack of accuracy. Objective In this study, we evaluate the accuracy of the newest version of a smartphone application (SA) for risk assessment of skin lesions. Methods This SA uses a machine learning algorithm to compute a risk rating. The algorithm is trained on 131 873 images taken by 31 449 users in multiple countries between January 2016 and August 2018 and rated for risk by dermatologists. To evaluate the sensitivity of the algorithm, we use 285 histopathologically validated skin cancer cases (including 138 malignant melanomas), from two previously published clinical studies (195 cases) and from the SA user database (90 cases). We calculate the specificity on a separate set from the SA user database containing 6000 clinically validated benign cases. Results The algorithm scored a 95.1% (95% CI, 91.9–97.3%) sensitivity in detecting (pre)malignant conditions (93% for malignant melanoma and 97% for keratinocyte carcinomas and precursors). This level of sensitivity was achieved with a 78.3% (95% CI, 77.2–79.3%) specificity. Conclusions This SA provides a high sensitivity to detect skin cancer; however, there is still room for improvement in terms of specificity. Future studies are needed to assess the impact of this SA on the health systems and its users.

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Section: Discussionmentioning

confidence: 70%

Section: Discussionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms

Udrea

Mitra

Costea

et al. 2019

Acad Dermatol Venereol

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“…Thereby, these systems are not feasible for those places. Furthermore, there is a trend in developing CAD systems embedded in smartphones, either for general users or to assist doctors [40,41]. Indeed, the use of smartphones to assist in skin cancer detection seems to be very feasible.…”

mentioning

confidence: 99%

The impact of patient clinical information on automated skin cancer detection

Pacheco

Krohling

2020

Computers in Biology and Medicine

147

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Skin cancer is one of the most common types of cancer around the world. For this reason, over the past years, different approaches have been proposed to assist detect it. Nonetheless, most of them are based only on dermoscopy images and do not take into account the patient clinical information. In this work, first, we present a new dataset that contains clinical images, acquired from smartphones, and patient clinical information of the skin lesions. Next, we introduce a straightforward approach to combine the clinical data and the images using different well-known deep learning models. These models are applied to the presented dataset using only the images and combining them with the patient clinical information. We present a comprehensive study to show the impact of the clinical data on the final predictions. The results obtained by combining both sets of information show a general improvement of around 7% in the balanced accuracy for all models. In addition, the statistical test indicates significant differences between the models with and without considering both data. The improvement achieved shows the potential of using patient clinical information in skin cancer detection and indicates that this piece of information is important to leverage skin cancer detection systems.

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Smartphones in Medical Photography

Freites-Martínez

Hofmann‐Wellenhof

Eber

2020

Photography in Clinical Medicine

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Fighting Melanoma with Smartphones: A Snapshot of Where We are a Decade after App Stores Opened Their Doors

Abstract: Since 2014 there have been an expanding and changing landscape of apps targeting melanoma diagnosis. There remains a lack of evidence backing their efficacy. This is concerning given their public availability and the gravity of their subject matter.

Cited by 30 publications

References 14 publications

Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms

Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms

The impact of patient clinical information on automated skin cancer detection

Smartphones in Medical Photography

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