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

Applied statistical modelling and inference in ophthalmology : analysis of visual field and video data for glaucoma patients : a thesis presented in total fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Manawatu, New Zealand

Eyesight is arguably the most important of our senses with the eye absorbing 80% of external information from our surroundings. The field of ophthalmology studying the anatomy, physiology and diseases of the eye, is of extreme importance. Many methods exist to measure vision and the eye, creating a large range of interesting datasets. We developed methods to analyse three datasets from subjects with glaucoma, the second leading cause of blindness worldwide. Visual field testing using standard automated perimetry, is the most common method for monitoring glaucoma progression. A numerical matrix representing the dimmest intensity seen by a particular locus on the eye is outputted. This can be thought of as a map, and disease mapping techniques applied. We employed conditional autoregressive priors to account for the spatial correlation structure in the visual field results, in a way that respects the physiological and optical properties of the eye. Model diagnostics showed our model superior to the currently used point-wise linear regression methods. Visual field mean deviation, the mean light intensity across all loci adjusted for age matched controls, provides a global estimate of glaucoma progression. We investigated the shape of the relationship between mean deviation and time over long series of visual fields using splines. We considered imposing a monotonic non-increasing constraint. When a curve deviated from being linear or monotonic non-increasing, this was an indication of physiological or treatment change in the eye. We developed methods to extract and analyse data from video sequences of retinal venous pulsation, observed as change in blood flow, varying with the cardiac cycle. Video sequences were divided into individual frames, and the mean pixel intensity was calculated separately for three vessel segments representing the artery, lower vein and upper vein. Simple harmonic terms modelled the periodic component of the trend. The non-periodic trend, caused by patient movement, was modelled by linear splines. An autoregressive process modelled error correlation. Retinal blood flow has been linked to many diseases, so the characteristics of these curves have clinical importance

Modelling retinal pulsatile blood flow from video data

Modern day datasets continue to increase in both size and diversity. One example of such ‘big data’ is video data. Within the medical arena, more disciplines are using video as a diagnostic tool. Given the large amount of data stored within a video image, it is one of most time consuming types of data to process and analyse. Therefore, it is desirable to have automated techniques to extract, process and analyse data from video images. While many methods have been developed for extracting and processing video data, statistical modelling to analyse the outputted data has rarely been employed. We develop a method to take a video sequence of periodic nature, extract the RGB data and model the changes occurring across the contiguous images. We employ harmonic regression to model periodicity with autoregressive terms accounting for the error process associated with the time series nature of the data. A linear spline is included to account for movement between frames. We apply this model to video sequences of retinal vessel pulsation, which is the pulsatile component of blood flow. Slope and amplitude are calculated for the curves generated from the application of the harmonic model, providing clinical insight into the location of obstruction within the retinal vessels. The method can be applied to individual vessels, or to smaller segments such as 2 × 2 pixels which can then be interpreted easily as a heat map

Monitoring acute and chronic kidney failure using statistical process control techniques

Creatinine tests are used to determine a patient's estimated glomerular filtration rate (eGFR) for the assessment of kidney function. This article describes how eGFR results for patients who need to have their cases referred for the attention of a specialist can be filtered out from among the numerous patients whose test results indicate that they are either displaying no reduction in kidney function or whose kidney function is reduced but remains stable. Automation of the process is a key ingredient of our solution because this is the means by which the human effort is directed to those patients in greatest need

Body Site Distribution of Acquired Melanocytic Naevi and Associated Characteristics in the General Population of Caucasian Adults : A Scoping Review

The number of melanocytic naevi is a major risk factor for melanoma. The divergent pathway hypothesis proposes that the propensity for naevus proliferation and malignant transformation may differ by body site and exposure to ultraviolet (UV) radiation. This scoping review aimed to summarise the evidence on the number and distribution of naevi (≥ 2 mm) on the body overall and by individual anatomical sites in Caucasian adults, and to assess whether studies used the International Agency for Research on Cancer (IARC) protocol to guide naevus counting processes. Systematic searches of Embase and PubMed identified 661 potentially relevant studies, and 12 remained eligible after full-text review. Studies varied widely in their counting protocols, reporting of naevus counts overall and by body sites, and used counting personnel with differing qualifications. Only one study used the IARC protocol. Studies reported that the highest number of naevi was on the trunk in males and on the arms in females. Body sites which receive intermittent exposure to UV radiation had higher density of naevi. Larger naevi (≥ 5 mm) were detected mostly on body sites intermittently exposed to UV radiation, and smaller naevi (< 5 mm) on chronically exposed sites. Studies reported that environmental and behavioural aspects related to UV radiation exposure, as well as genetic factors, all impact body site and size distribution of naevi. This review found that to overcome limitations of the current evidence, future studies should use consistent naevus counting protocols. Skin surface imaging could improve the reliability of findings. An updated IARC protocol is required that integrates these emerging standards and technologies to guide reliable and reproducible naevus counting in the future.</p

Spatial Modeling of Visual Field Data for Assessing Glaucoma Progression

PURPOSE. In order to reduce noise and account for spatial correlation, we applied disease mapping techniques to visual field (VF) data. We compared our calculated rates of progression to other established techniques.METHODS. Conditional autoregressive (CAR) priors, weighted to account for physiologic correlations, were employed to describe spatial and spatiotemporal correlation over the VF. Our model is extended to account for several physiologic features, such as the nerve fibers serving adjacent loci on the VF not mapping to the adjacent optic disc regions, the presence of the blind spot, and large measurement fluctuation. The models were applied to VFs from 194 eyes and fitted within a Bayesian framework using Metropolis-Hastings algorithms.RESULTS. Our method (SPROG for Spatial PROGgression) showed progression in 42% of eyes. Using a clinical reference, our method had the best receiver operating characteristics compared with the point-wise linear regression methods. Because our model intrinsically accounts for the large variation of VF data, by adjusting for spatial correlation, the effects of outliers are minimized, and spurious trends are avoided.CONCLUSIONS. By using CAR priors, we have modeled the spatial correlation in the eye. Combining this with physiologic information, we are able to provide a novel method for VF analysis. Model diagnostics, sensitivity, and specificity show our model to be apparently superior to current point-wise linear regression methods. (http://www.anzctr.org. au number, ACTRN12608000274370.) (Invest Ophthalmol Vis Sci. 2013; 54: 1544-1553) DOI: 10.1167/iovs.12-1122