Skin cancer risk self-assessment using AI as a mass screening tool

Ukharov, A.O.; Shlivko, I. L.; Клеменова, И. А.; Garanina, O.E.; Uskova, K.A.; Mironycheva, A.M.; Stepanova, Y.L.

doi:10.1016/j.imu.2023.101223

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…This system should provide basic guidelines around how to frame the breast such that no occlusion is present; not use the finger to point out parts of interest; and ensure the camera framing can see the entire breast so that the nipple, areola, and breast tissue are all visible. Previous works explore the importance of implementing guidelines for image assessment of external diseases, such as in dermatology disease assessments, and its benefits for better professional evaluation and higher accuracy in diagnosing conditions [ 64 , 65 , 69 ]. Guidelines may be implemented as a set of easy instructions, and more advanced systems could provide immediate image quality feedback.…”

Section: Resultsmentioning

confidence: 99%

Augmenting Telepostpartum Care With Vision-Based Detection of Breastfeeding-Related Conditions: Algorithm Development and Validation

De Souza,

Viswanath,

Echterhoff

et al. 2024

JMIR AI

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Background Breastfeeding benefits both the mother and infant and is a topic of attention in public health. After childbirth, untreated medical conditions or lack of support lead many mothers to discontinue breastfeeding. For instance, nipple damage and mastitis affect 80% and 20% of US mothers, respectively. Lactation consultants (LCs) help mothers with breastfeeding, providing in-person, remote, and hybrid lactation support. LCs guide, encourage, and find ways for mothers to have a better experience breastfeeding. Current telehealth services help mothers seek LCs for breastfeeding support, where images help them identify and address many issues. Due to the disproportional ratio of LCs and mothers in need, these professionals are often overloaded and burned out. Objective This study aims to investigate the effectiveness of 5 distinct convolutional neural networks in detecting healthy lactating breasts and 6 breastfeeding-related issues by only using red, green, and blue images. Our goal was to assess the applicability of this algorithm as an auxiliary resource for LCs to identify painful breast conditions quickly, better manage their patients through triage, respond promptly to patient needs, and enhance the overall experience and care for breastfeeding mothers. Methods We evaluated the potential for 5 classification models to detect breastfeeding-related conditions using 1078 breast and nipple images gathered from web-based and physical educational resources. We used the convolutional neural networks Resnet50, Visual Geometry Group model with 16 layers (VGG16), InceptionV3, EfficientNetV2, and DenseNet169 to classify the images across 7 classes: healthy, abscess, mastitis, nipple blebs, dermatosis, engorgement, and nipple damage by improper feeding or misuse of breast pumps. We also evaluated the models’ ability to distinguish between healthy and unhealthy images. We present an analysis of the classification challenges, identifying image traits that may confound the detection model. Results The best model achieves an average area under the receiver operating characteristic curve of 0.93 for all conditions after data augmentation for multiclass classification. For binary classification, we achieved, with the best model, an average area under the curve of 0.96 for all conditions after data augmentation. Several factors contributed to the misclassification of images, including similar visual features in the conditions that precede other conditions (such as the mastitis spectrum disorder), partially covered breasts or nipples, and images depicting multiple conditions in the same breast. Conclusions This vision-based automated detection technique offers an opportunity to enhance postpartum care for mothers and can potentially help alleviate the workload of LCs by expediting decision-making processes.

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Section: Resultsmentioning

confidence: 99%

Augmenting Telepostpartum Care With Vision-Based Detection of Breastfeeding-Related Conditions: Algorithm Development and Validation

De Souza,

Viswanath,

Echterhoff

et al. 2024

JMIR AI

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“…Apart from the disability and morbidity caused by skin diseases, skin cancer can be fatal if not treated early. Skin cancer occurs when abnormal cells grow uncontrollably in the skin [13]. According to the American Cancer Society [14], 1.9-million new cancer cases are expected to be diagnosed in 2021.…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review

Debelee

2023

Diagnostics

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Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research.

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