A novel machine learning framework that is able to consistently detect, localize, and measure the severity of human congenital cleft lip anomalies is introduced. The ultimate goal is to fill an important clinical void: to provide an objective and clinically feasible method of gauging baseline facial deformity and the change obtained through reconstructive surgical intervention. The proposed method first employs the StyleGAN2 generative adversarial network with model adaptation to produce a normalized transformation of 125 faces, and then uses a pixel-wise subtraction approach to assess the difference between all baseline images and their normalized counterparts (a proxy for severity of deformity). The pipeline of the proposed framework consists of the following steps: image preprocessing, face normalization, color transformation, heat-map generation, morphological erosion, and abnormality scoring. Heatmaps that finely discern anatomic anomalies visually corroborate the generated scores. The proposed framework is validated through computer simulations as well as by comparison of machine-generated versus human ratings of facial images. The anomaly scores yielded by the proposed computer model correlate closely with human ratings, with a calculated Pearson’s r score of 0.89. The proposed pixel-wise measurement technique is shown to more closely mirror human ratings of cleft faces than two other existing, state-of-the-art image quality metrics (Learned Perceptual Image Patch Similarity and Structural Similarity Index). The proposed model may represent a new standard for objective, automated, and real-time clinical measurement of faces affected by congenital cleft deformity.

Background: Many metagenomic studies have linked the imbalance in microbial abundance profiles to a wide range of diseases. These studies suggest utilizing the microbial abundance profiles as potential markers for metagenomic-associated conditions. Due to the inevitable importance of biomarkers in understanding the disease progression and the development of possible therapies, various computational tools have been proposed for metagenomic biomarker detection. However, most existing tools require prior scripting knowledge and lack user friendly interfaces, causing considerable time and effort to install, configure, and run these tools. Besides, there is no available all-in-one solution for running and comparing various metagenomic biomarker detection simultaneously. In addition, most of these tools just present the suggested biomarkers without any statistical evaluation for their quality.Results: To overcome these limitations, this work presents MetaAnalyst, a software package with a simple graphical user interface (GUI) that (i) automates the installation and configuration of 28 state-of-the-art tools, (ii) supports flexible study design to enable studying the dataset under different scenarios smoothly, iii) runs and evaluates several algorithms simultaneously iv) supports different input formats and provides the user with several preprocessing capabilities, v) provides a variety of metrics to evaluate the quality of the suggested markers, and vi) presents the outcomes in the form of publication quality plots with various formatting capabilities as well as Excel sheets.Conclusions: The utility of this tool has been verified through studying a metagenomic dataset under four scenarios. The executable file for MetaAnalyst along with its user manual are made available athttps://github.com/mshawaqfeh/MetaAnalyst.

This work presents a novel machine learning and signal processing framework designed to consistently detect, localize, and rate facial anomalies such as cleft lip deformity. The goal of this research is to establish a universal and objective measure of facial abnormalities, capable of sensitively identifying both subtle and significant deformities. The proposed model utilizes an enhanced two-phase automatic inpainting method for face normalization, effectively removing anomalies from the image and replacing them with normal facial content. The framework leverages an efficient knowledge distillation model to estimate the initial heatmap that highlights potential facial anomalies. This heatmap is subsequently converted into a mask for inpainting, which is applied to normalize the original face. A deep convolutional neural network (CNN)-based feature extraction method is then employed to compare the anomalous facial image with its normalized counterpart, enabling robust detection and evaluation of various facial anomalies. This is achieved by obtaining a noise-reduced final heatmap that more accurately scores the level of normality in the face. The normalization protocol delivers results comparable to state-of-the-art methods, while being significantly faster, taking less than one second from image upload to obtaining the face rating. This makes it highly feasible for deployment in mobile applications. Additionally, the proposed method does not require anomalous data for model training, while efficiently detecting and assessing various facial anomalies. We demonstrate that this unique computerized image appraisal system generates facial normality/abnormality scores that closely correlate with human intuition, exhibiting 92% correlation with human scores.

This paper introduces an innovative blind in-painting technique designed for image quality enhancement and noise removal. Employing Monte-Carlo simulations, the proposed method approximates the optimal mask necessary for automatic image in-painting. This involves the progressive construction of a noise removal mask, initially sampled randomly from a binomial distribution. A confidence map is iteratively generated, providing a pixel-wise indicator map that discerns whether a particular pixel resides within the dataset domain. Notably, the proposed method eliminates the manual creation of an image mask to eradicate noise, a process prone to additional time overhead, especially when noise is dispersed across the entire image. Furthermore, the proposed method simplifies the determination of pixels involved in the in-painting process, excluding normal pixels and thereby preserving the integrity of the original image content. Computer simulations demonstrate the efficacy of this method in removing various types of noise, including brush painting and random salt and pepper noise. The proposed technique successfully restores similarity between the original and normalized datasets, yielding a Binary Cross Entropy (BCE) of 0.69 and a Peak-Signal-to-Noise-Ratio (PSNR) of 20.069. With its versatile applications, this method proves beneficial in diverse industry and medical contexts.