Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review

Skin lesions known as naevi exhibit diverse characteristics such as size, shape, and colouration. The concept of an "Ugly Duckling Naevus" comes into play when monitoring for melanoma, referring to a lesion with distinctive features that sets it apart from other lesions in the vicinity. As lesions within the same individual typically share similarities and follow a predictable pattern, an ugly duckling naevus stands out as unusual and may indicate the presence of a cancerous melanoma. Computer-aided diagnosis (CAD) has become a significant player in the research and development field, as it combines machine learning techniques with a variety of patient analysis methods. Its aim is to increase accuracy and simplify decision-making, all while responding to the shortage of specialized professionals. These automated systems are especially important in skin cancer diagnosis where specialist availability is limited. As a result, their use could lead to life-saving benefits and cost reductions within healthcare. Given the drastic change in survival when comparing early stage to late-stage melanoma, early detection is vital for effective treatment and patient outcomes. Machine learning (ML) and deep learning (DL) techniques have gained popularity in skin cancer classification, effectively addressing challenges, and providing results equivalent to that of specialists. This article extensively covers modern Machine Learning and Deep Learning algorithms for detecting melanoma and suspicious naevi. It begins with general information on skin cancer and different types of naevi, then introduces AI, ML, DL, and CAD. The article then discusses the successful applications of various ML techniques like convolutional neural networks (CNN) for melanoma detection compared to dermatologists' performance. Lastly, it examines ML methods for UD naevus detection and identifying suspicious naevi

Revamping AI Models in Dermatology: Overcoming Critical Challenges for Enhanced Skin Lesion Diagnosis

The surge in developing deep learning models for diagnosing skin lesions through image analysis is notable, yet their clinical black faces challenges. Current dermatology AI models have limitations: limited number of possible diagnostic outputs, lack of real-world testing on uncommon skin lesions, inability to detect out-of-distribution images, and over-reliance on dermoscopic images. To address these, we present an All-In-One \textbf{H}ierarchical-\textbf{O}ut of Distribution-\textbf{C}linical Triage (HOT) model. For a clinical image, our model generates three outputs: a hierarchical prediction, an alert for out-of-distribution images, and a recommendation for dermoscopy if clinical image alone is insufficient for diagnosis. When the recommendation is pursued, it integrates both clinical and dermoscopic images to deliver final diagnosis. Extensive experiments on a representative cutaneous lesion dataset demonstrate the effectiveness and synergy of each component within our framework. Our versatile model provides valuable decision support for lesion diagnosis and sets a promising precedent for medical AI applications

A phase 1 study of heat/phenol-killed, E. coli -encapsulated, recombinant modified peanut proteins Ara h 1, Ara h 2, and Ara h 3 (EMP-123) for the treatment of peanut allergy

Immunotherapy for peanut allergy may be limited by the risk of adverse reactions

Allergic Reactions to Foods in Preschool-Aged Children in a Prospective Observational Food Allergy Study

To examine circumstances of allergic reactions to foods in a cohort of preschool-aged children

The natural history of egg allergy in an observational cohort

There are few studies on the natural history of egg allergy and most are single site, not longitudinal, and have not identified early predictors of outcomes

The natural history of milk allergy in an observational cohort

There are few studies on the natural history of milk allergy. Most are single-site and not longitudinal, and these have not identified a means for early prediction of outcomes

Sublingual immunotherapy for peanut allergy: A randomized, double-blind, placebo-controlled multicenter trial

There are presently no available therapeutic options for peanut-allergic patients

Oral Immunotherapy for Treatment of Egg Allergy in Children

For egg allergy, dietary avoidance is the only currently approved treatment. We evaluated oral immunotherapy using egg-white powder for the treatment of children with egg allergy