cad detects the polyps by identifying their characteristic "bump-like" shape. to avoid excessive false positives, cad ignores the normal colon wall, including the haustral folds. cardiovascular disease state-of-the-art methods in cardiovascular computing, cardiovascular informatics, and mathematical and computational modeling can provide valuable tools in clinical decision-making. cad systems with novel image-analysis-based markers as input can aid vascular physicians to decide with higher confidence on best suitable treatment for cardiovascular disease patients. reliable early-detection and risk-stratification of carotid atherosclerosis is of outmost importance for predicting strokes in asymptomatic patients. to this end, various noninvasive and low-cost markers have been proposed, using ultrasound-image-based features. these combine echogenicity, texture, and motion characteristics to assist clinical decision towards improved prediction, assessment and management of cardiovascular risk. cad is available for the automatic detection of significant (causing more than 50% stenosis) coronary artery disease in coronary ct angiography (ccta) studies. congenital heart defect early detection of pathology can be the difference between life and death. cade can be done by auscultation with a digital stethoscope and specialized software, also known as computer-aided auscultation. murmurs, irregular heart sounds, caused by blood flowing through a defective heart, can be detected with high sensitivity and specificity. computer-aided auscultation is sensitive to external noise and bodily sounds and requires an almost silent environment to function accurately. pathological brain detection (pbd) chaplot et al. was the first to use discrete wavelet transform (dwt) coefficients to detect pathological brains. maitra and chatterjee employed the slantlet transform, which is an improved version of dwt. their feature vector of each image is created by considering the magnitudes of slantlet transform outputs corresponding to six spatial positions chosen according to a specific logic. in 2010, wang and wu presented a forward neural network (fnn) based method to classify a given mr brain image as normal or abnormal. the parameters of fnn were optimized via adaptive chaotic particle swarm optimization (acpso). results over 160 images showed that the classification accuracy was 98.75%. in 2011, wu and wang proposed using dwt for feature extraction, pca for feature reduction, and fnn with scaled chaotic artificial bee colony (scabc) as classifier. in 2013, saritha et al. were the first to apply wavelet entropy (we) to detect pathological brains. saritha also suggested to use spider-web plots. later, zhang et al. proved removing spider-web plots did not influence the performance. genetic pattern search method was applied to identify abnormal brain from normal controls. its classification accuracy was reported as 95.188%. das et al. proposed to use ripplet transform. zhang et al. proposed to use particle swarm optimization (pso). kalbkhani et al. suggested to use garch model. in 2014, el-dahshan et al. suggested to use pulse coupled neural network. in 2015, zhou et al. suggested to apply naive bayes classifier to detect pathological brains. alzheimer's disease cads can be used to identify subjects with alzheimer's and mild cognitive impairment from normal elder controls. in 2014, padma et al. used combined wavelet statistical texture features to segment and classify ad benign and malignant tumor slices. zhang et al. found kernel support vector machine decision tree had 80% classification accuracy, with an average computation time of 0.022s for each image classification. in 2019, signaevsky et al. have first reported a trained fully convolutional network (fcn) for detection and quantification of neurofibrillary tangles (nft) in alzheimer's disease and an array of other tauopathies. the trained fcn achieved high precision and recall in naive digital whole slide image (wsi) semantic segmentation, correctly identifying nft objects using a segnet model trained for 200 epochs. the fcn reached near-practical efficiency with average processing time of 45 min per wsi per graphic processing unit (gpu), enabling reliable and reproducible large-scale detection of nfts. the measured performance on test data of eight naive wsi across various tauopathies resulted in the recall, precision, and an f1 score of 0.92, 0.72, and 0.81, respectively. eigenbrain is a novel brain feature that can help to detect ad, based on principal component analysis or independent component analysis decomposition. polynomial kernel svm has been shown to achieve good accuracy. the polynomial ksvm performs better than linear svm and rbf kernel svm. other approaches with decent results involve the use of texture analysis, morphological features, or high-order statistical features nuclear medicine cadx is available for nuclear medicine images. commercial cadx systems for the diagnosis of bone metastases in whole-body bone scans and coronary artery disease in myocardial perfusion images exist. with a high sensitivity and an acceptable false lesions detection rate, computer-aided automatic lesion detection system is demonstrated as useful and will probably in the future be able to help nuclear medicine physicians to identify possible bone lesions. diabetic retinopathy diabetic retinopathy is a disease of the retina that is diagnosed predominantly by fundoscopic images. diabetic patients in industrialised countries generally undergo regular screening for the condition. imaging is used to recognize early signs of abnormal retinal blood vessels. manual analysis of these images can be time-consuming and unreliable. cad has been employed to enhance the accuracy, sensitivity, and specificity of automated detection method. the use of some cad systems to replace human graders can be safe and cost effective. image pre-processing, and feature extraction and classification are two main stages of these cad algorithms. pre-processing methods image normalization is minimizing the variation across the entire image. intensity variations in areas between periphery and central macular region of the eye have been reported to cause inaccuracy of vessel segmentation. based on the 2014 review, this technique was the most frequently used and appeared in 11 out of 40 recently (since 2011) published primary research. histogram equalization sample image. left: normal gray-scale fundoscopic image. right: post-histogram equalization processing. histogram equalization is useful in enhancing contrast within an image. this technique is used to increase local contrast. at the end of the processing, areas that were dark in the input image would be brightened, greatly enhancing the contrast among the features present in the area. on the other hand, brighter areas in the input image would remain bright or be reduced in brightness to equalize with the other areas in the image. besides vessel segmentation, other features related to diabetic retinopathy can be further separated by using this pre-processing technique. microaneurysm and hemorrhages are red lesions, whereas exudates are yellow spots. increasing contrast between these two groups allow better visualization of lesions on images. with this technique, 2014 review found that 10 out of the 14 recently (since 2011) published primary research. green channel filtering is another technique that is useful in differentiating lesions rather than vessels. this method is important because it provides the maximal contrast between diabetic retinopathy-related lesions. microaneurysms and hemorrhages are red lesions that appear dark after application of green channel filtering. in contrast, exudates, which appear yellow in normal image, are transformed into bright white spots after green filtering. this technique is mostly used according to the 2014 review, with appearance in 27 out of 40 published articles in the past three years. in addition, green channel filtering can be used to detect center of optic disc in conjunction with double-windowing system. non-uniform illumination correction is a technique that adjusts for non-uniform illumination in fundoscopic image. non-uniform illumination can be a potential error in automated detection of diabetic retinopathy because of changes in statistical characteristics of image. these changes can affect latter processing such as feature extraction and are not observable by humans. correction of non-uniform illumination (f') can be achieved by modifying the pixel intensity using known original pixel intensity (f), and average intensities of local (λ) and desired pixels (μ) (see formula below). walter-klein transformation is then applied to achieve the uniform illumination. this technique is the least used pre-processing method in the review from 2014. f ′ = f + μ − λ morphological operations is the second least used pre-processing method in 2014 review. the main objective of this method is to provide contrast enhancement, especially darker regions compared to background. feature extractions and classifications after pre-processing of funduscopic image, the image will be further analyzed using different computational methods. however, the current literature agreed that some methods are used more often than others during vessel segmentation analyses. these methods are svm, multi-scale, vessel-tracking, region growing approach, and model-based approaches. support vector machine. support vectors (dashed lines) are created to maximize the separation between two groups. support vector machine is by far the most frequently used classifier in vessel segmentation, up to 90% of cases. svm is a supervised learning model that belongs to the broader category of pattern recognition technique. the algorithm works by creating a largest gap between distinct samples in the data. the goal is to create the largest gap between these components that minimize the potential error in classification. in order to successfully segregate blood vessel information from the rest of the eye image, svm algorithm creates support vectors that separate the blood vessel pixel from the rest of the image through a supervised environment. detecting blood vessel from new images can be done through similar manner using support vectors. combination with other pre-processing technique, such as green channel filtering, greatly improves the accuracy of detection of blood vessel abnormalities. some beneficial properties of svm include flexibility – highly flexible in terms of function simplicity – simple, especially with large datasets (only support vectors are needed to create separation between data) multi-scale approach is a multiple resolution approach in vessel segmentation. at low resolution, large-diameter vessels can first be extracted. by increasing resolution, smaller branches from the large vessels can be easily recognized. therefore, one advantage of using this technique is the increased analytical speed. additionally, this approach can be used with 3d images. the surface representation is a surface normal to the curvature of the vessels, allowing the detection of abnormalities on vessel surface. vessel tracking is the ability of the algorithm to detect "centerline" of vessels. these centerlines are maximal peak of vessel curvature. centers of vessels can be found using directional information that is provided by gaussian filter. similar approaches that utilize the concept of centerline are the skeleton-based and differential geometry-based. region growing approach is a method of detecting neighboring pixels with similarities. a seed point is required for such method to start. two elements are needed for this technique to work: similarity and spatial proximity. a neighboring pixel to the seed pixel with similar intensity is likely to be the same type and will be added to the growing region. one disadvantage of this technique is that it requires manual selection of seed point, which introduces bias and inconsistency in the algorithm. this technique is also being used in optic disc identification. model-based approaches employ representation to extract vessels from images. three broad categories of model-based are known: deformable, parametric, and template matching. deformable methods uses objects that will be deformed to fit the contours of the objects on the image. parametric uses geometric parameters such as tubular, cylinder, or ellipsoid representation of blood vessels. classical snake contour in combination with blood vessel topological information can also be used as a model-based approach. lastly, template matching is the usage of a template, fitted by stochastic deformation process using hidden markov mode 1. effects on employment automation of medical diagnosis labor (for example, quantifying red blood cells) has some historical precedent. the deep learning revolution of the 2010s has already produced ais that are more accurate in many areas of visual diagnosis than radiologists and dermatologists, and this gap is expected to grow. some experts, including many doctors, are dismissive of the effects that ai will have on medical specialties. in contrast, many economists and artificial intelligence experts believe that fields such as radiology will be massively disrupted, with unemployment or downward pressure on the wages of radiologists; hospitals will need fewer radiologists overall, and many of the radiologists who still exist will require substantial retraining. geoffrey hinton, the "godfather of deep learning", argues that (in view of the likely advances expected in the next five or ten years) hospitals should immediately stop training radiologists, as their time-consuming and expensive training on visual diagnosis will soon be mostly obsolete, leading to a glut of traditional radiologists. an op-ed in jama argues that pathologists and radiologists should merge into a single "information specialist" role, and state that "to avoid