Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to fulfill basic daily needs.AD is the major cause of dementia.Computer-aided diagnosis(CADx)tools aid medical practitioners in accurately identifying diseases such as AD in patients.This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop(IWD)algorithm and the Random Forest(RF)classifier.The IWD algorithm an efficient feature selection method,was used to identify the most deterministic features of AD in the dataset.RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented(DN)or cognitively normal(CN).The proposed tool also classifies patients as mild cognitive impairment(MCI)or CN.The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative(ADNI).The RF ensemble method achieves 100%accuracy in identifying DN patients from CN patients.The classification accuracy for classifying patients as MCI or CN is 92%.This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.

Computer-aided differential diagnosis system for Alzheimer’s disease based on machine learning with functional and morphological image features in magnetic resonance imaging

Alzheimer’s disease (AD) is a dementing disorder and one of the major public health problems in countries with greater longevity. The cerebral cortical thickness and cerebral blood flow (CBF), which are considered as morphological and functional image features, respectively, could be decreased in specific cerebral regions of patients with dementia of Alzheimer type. Therefore, the aim of this study was to develop a computer-aided classification system for AD patients based on machine learning with the morphological and functional image features derived from a magnetic resonance (MR) imaging system. The cortical thicknesses in ten cerebral regions were derived as morphological features by using gradient vector trajectories in fuzzy membership images. Functional CBF maps were measured with an arterial spin labeling technique, and ten regional CBF values were obtained by registration between the CBF map and Talairach atlas using an affine transformation and a free form deformation. We applied two systems based on an arterial neural network (ANN) and a support vector machine (SVM), which were trained with 4 morphological and 6 functional image features, to 15 AD patients and 15 clinically normal (CN) subjects for classification of AD. The area under the receiver operating characteristic curve (AUC) values for the two systems based on the ANN and SVM with both image?features were 0.901 and 0.915, respectively. The AUC values for the ANN-and SVM-based systems with the morphological features were 0.710 and 0.660, respectively, and those with the functional features were 0.878 and 0.903, respectively. Our preliminary results suggest that the proposed method may have potential for assisting radiologists in the differential diagnosis of AD patients by using morphological and functional image features.

Artificial intelligence and inflammatory bowel disease: Where are we going?

Inflammatory bowel diseases,namely ulcerative colitis and Crohn’s disease,are chronic and relapsing conditions that pose a growing burden on healthcare systems worldwide.Because of their complex and partly unknown etiology and pathogenesis,the management of ulcerative colitis and Crohn’s disease can prove challenging not only from a clinical point of view but also for resource optimization.Artificial intelligence,an umbrella term that encompasses any cognitive function developed by machines for learning or problem solving,and its subsets machine learning and deep learning are becoming ever more essential tools with a plethora of applications in most medical specialties.In this regard gastroenterology is no exception,and due to the importance of endoscopy and imaging numerous clinical studies have been gradually highlighting the relevant role that artificial intelligence has in inflammatory bowel diseases as well.The aim of this review was to summarize the most recent evidence on the use of artificial intelligence in inflammatory bowel diseases in various contexts such as diagnosis,follow-up,treatment,prognosis,cancer surveillance,data collection,and analysis.Moreover,insights into the potential further developments in this field and their effects on future clinical practice were discussed.

Artificial intelligence-assisted esophageal cancer management:Now and future

Esophageal cancer poses diagnostic,therapeutic and economic burdens in highrisk regions.Artificial intelligence(AI)has been developed for diagnosis and outcome prediction using various features,including clinicopathologic,radiologic,and genetic variables,which can achieve inspiring results.One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett’s esophagus.In this review,we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes,and combine the endoscopic images to detect precancerous lesions or early cancer.Pertinent studies conducted in recent two years have surged in numbers,with large datasets and external validation from multi-centers,and have partly achieved intriguing results of expert’s performance of AI in real time.Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets,aiming at real-time video processing,are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists.Meanwhile,supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion,which meets patient-centered satisfaction.Notably,ethical and legal issues regarding the blackbox nature of computer algorithms should be addressed,for both clinicians and regulators.

Artificial intelligence and early esophageal cancer

The development of esophageal cancer(EC)from early to advanced stage results in a high mortality rate and poor prognosis.Advanced EC not only poses a serious threat to the life and health of patients but also places a heavy economic burden on their families and society.Endoscopy is of great value for the diagnosis of EC,especially in the screening of Barrett’s esophagus and early EC.However,at present,endoscopy has a low diagnostic rate for early tumors.In recent years,artificial intelligence(AI)has made remarkable progress in the diagnosis of digestive system tumors,providing a new model for clinicians to diagnose and treat these tumors.In this review,we aim to provide a comprehensive overview of how AI can help doctors diagnose early EC and precancerous lesions and make clinical decisions based on the predicted results.We analyze and summarize the recent research on AI and early EC.We find that based on deep learning(DL)and convolutional neural network methods,the current computer-aided diagnosis system has gradually developed from in vitro image analysis to real-time detection and diagnosis.Based on powerful computing and DL capabilities,the diagnostic accuracy of AI is close to or better than that of endoscopy specialists.We also analyze the shortcomings in the current AI research and corresponding improvement strategies.We believe that the application of AI-assisted endoscopy in the diagnosis of early EC and precancerous lesions will become possible after the further advancement of AI-related research.

A leaf image localization based algorithm for different crops disease classification

Agricultural crop production is a major contributing element to any country’s economy.To maintain the economic growth of any country plants disease detection is a leading factor in agriculture.The contribution of the proposed algorithm is to optimize the extracted infor-mation from the available resources for the betterment of the result without any additional complexity.The proposed technique basically localizes the leaf region prior to the image classification into healthy and diseased.The novelty of this work is to fuse the information extracted from the available resources and optimize it to enhance the expected outcome.The leaf colors are analyzed using color transformation for the seed region identification.The mapping of a low-dimensional RGB color image into L*a*b color space provides an expansion of the spectral range.The neighboring pixels-based leaf region growing is applied on the initial seeds.In order to refine the leaf boundary and the disease-affected areas,we employed a random sample consensus(RANSAC)for suitable curve fitting.The feature sets using bag of visual words,Fisher vectors,and handcrafted features are extracted followed by classification using logistic regression,multilayer perceptron model,and support vector machine.The performance of the proposal is analyzed through PlantVillage datasets of apple,bell pepper,cherry,corn,grape,potato,and tomato.The simulation-based analysis of the proposed contextualization-based image categorization process outperforms as compared with the state of arts.The proposed approach provides average accuracy and area under the curve of 0.932 and 0.903,respectively.