Computer-Based Diagnostic Systems (Practitioner Series)

Computer-aided diagnosis

CAD is especially established in US and the Netherlands and is used in addition to human evaluation, usually by a radiologist. The first CAD system for mammography was developed in a research project at the University of Chicago. Today it is commercially offered by iCAD and Hologic.

There are currently some non-commercial projects being developed, such as Ashita Project, a gradient-based screening software by Alan Hshieh , as well. However, while achieving high sensitivities, CAD systems tend to have very low specificity and the benefits of using CAD remain uncertain. Some studies suggest a positive impact on mammography screening programs, [14] [15] but others show no improvement.

However, it noted considerable heterogeneity in the impact on recall rate across studies. Procedures to evaluate mammography based on magnetic resonance imaging exist too. In the diagnosis of lung cancer, computed tomography with special three-dimensional CAD systems are established and considered as appropriate second opinions. Today all well-known vendors of medical systems offer corresponding solutions. Early detection of lung cancer is valuable. Lung cancer takes more victims than breast cancer, prostate cancer and colon cancer together.

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This is due to the asymptomatic growth of this cancer. In the majority of cases it is too late for a successful therapy if the patient develops first symptoms e. Indeed, the random detection of lung cancer in the early stage stage 1 in the x-ray image is difficult. CAD is available for detection of colorectal polyps in the colon in CT colonography.

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. This, for example, can be used for chest pain patients' triage in an emergency setting. Early detection of pathology can be the difference between life and death.

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Computer-based diagnostic systems are among the most successful applications of knowledge-based systems (KBS) technology. Practitioner Series. Computer-Based Diagnostic Systems (Practitioner Series) [Chris Price] on domaine-solitude.com *FREE* shipping on qualifying offers. This book addresses the issue of.

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. 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.

Results over images showed that the classification accuracy was In , Saritha et al. Saritha also suggested to use spider-web plots. Its classification accuracy was reported as In , El-Dahshan et al. In , Zhou et al. CADs can be used to identify subjects with Alzheimer's and mild cognitive impairment from normal elder controls.

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In , Padma et al. Eigenbrain is a novel brain feature that can help to detect AD, based on Principal Component Analysis [68] or Independent Component Analysis decomposition [69]. Polynomial kernel SVM has been shown to achieve good accuracy. Other approaches with decent results involve the use of texture analysis [71] , morphological features [72] , or high-order statistical features [73]. 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 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. 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.

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. Histogram equalization is useful in enhancing contrast within an image. 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, review found that 10 out of the 14 recently since 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. 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 review, with appearance in 27 out of 40 published articles in the past three years.

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. Morphological operations is the second least used pre-processing method in review.

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The algorithm works by creating a largest gap between distinct samples in the data. 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. This is the ability to break models of devices down into fragments that can be used again when modeling other related devices. Expert Systems with Applications. Mental state Mini—mental state examination Cranial nerve examination Upper limb neurological examination. Depending on the CAD system these markings can be permanently or temporary saved.

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. 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. 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. 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.

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. 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: 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. Model-based approaches employ representation to extract vessels from images. Three broad categories of model-based are known: Parametric uses geometric parameters such as tubular, cylinder, or ellipsoid representation of blood vessels.

Computer-based diagnostic systems.

Classical snake contour in combination with blood vessel topological information can also be used as a model-based approach. Automation of medical diagnosis labor for example, quantifying red blood cells has some historical precedent. 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. Thus differential diagnosis , in which several possible explanations are compared and contrasted, must be performed. This involves the correlation of various pieces of information followed by the recognition and differentiation of patterns.

Occasionally the process is made easy by a sign or symptom or a group of several that is pathognomonic. Diagnosis is a major component of the procedure of a doctor's visit. From the point of view of statistics , the diagnostic procedure involves classification tests. The first recorded examples of medical diagnosis are found in the writings of Imhotep — BC in ancient Egypt the Edwin Smith Papyrus.

A diagnosis, in the sense of diagnostic procedure, can be regarded as an attempt at classification of an individual's condition into separate and distinct categories that allow medical decisions about treatment and prognosis to be made.

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Subsequently, a diagnostic opinion is often described in terms of a disease or other condition, but in the case of a wrong diagnosis, the individual's actual disease or condition is not the same as the individual's diagnosis. A diagnostic procedure may be performed by various health care professionals such as a physician , physical therapist, optometrist , healthcare scientist , chiropractor , dentist , podiatrist , nurse practitioner , or physician assistant. This article uses diagnostician as any of these person categories.

A diagnostic procedure as well as the opinion reached thereby does not necessarily involve elucidation of the etiology of the diseases or conditions of interest, that is, what caused the disease or condition. Such elucidation can be useful to optimize treatment, further specify the prognosis or prevent recurrence of the disease or condition in the future. The initial task is to detect a medical indication to perform a diagnostic procedure. Even during an already ongoing diagnostic procedure, there can be an indication to perform another, separate, diagnostic procedure for another, potentially concomitant, disease or condition.

This may occur as a result of an incidental finding of a sign unrelated to the parameter of interest, such as can occur in comprehensive tests such as radiological studies like magnetic resonance imaging or blood test panels that also include blood tests that are not relevant for the ongoing diagnosis.

General components which are present in a diagnostic procedure in most of the various available methods include:. There are a number of methods or techniques that can be used in a diagnostic procedure, including performing a differential diagnosis or following medical algorithms. The method of differential diagnosis is based on finding as many candidate diseases or conditions as possible that can possibly cause the signs or symptoms, followed by a process of elimination or at least of rendering the entries more or less probable by further medical tests and other processing until, aiming to reach the point where only one candidate disease or condition remains as probable.

The final result may also remain a list of possible conditions, ranked in order of probability or severity. The resultant diagnostic opinion by this method can be regarded more or less as a diagnosis of exclusion. Even if it does not result in a single probable disease or condition, it can at least rule out any imminently life-threatening conditions. Unless the provider is certain of the condition present, further medical tests, such as medical imaging, are performed or scheduled in part to confirm or disprove the diagnosis but also to document the patient's status and keep the patient's medical history up to date.

If unexpected findings are made during this process, the initial hypothesis may be ruled out and the provider must then consider other hypotheses. In a pattern recognition method the provider uses experience to recognize a pattern of clinical characteristics. This may be the primary method used in cases where diseases are "obvious", or the provider's experience may enable him or her to recognize the condition quickly.

Theoretically, a certain pattern of signs or symptoms can be directly associated with a certain therapy, even without a definite decision regarding what is the actual disease, but such a compromise carries a substantial risk of missing a diagnosis which actually has a different therapy so it may be limited to cases where no diagnosis can be made.

The term diagnostic criteria designates the specific combination of signs , symptoms , and test results that the clinician uses to attempt to determine the correct diagnosis. Some examples of diagnostic criteria, also known as clinical case definitions , are:.

Clinical decision support systems are interactive computer programs designed to assist health professionals with decision-making tasks. Typically the system makes suggestions for the clinician to look through and the clinician picks useful information and removes erroneous suggestions.

Overdiagnosis is the diagnosis of "disease" that will never cause symptoms or death during a patient's lifetime. It is a problem because it turns people into patients unnecessarily and because it can lead to economic waste overutilization and treatments that may cause harm. Overdiagnosis occurs when a disease is diagnosed correctly, but the diagnosis is irrelevant. A correct diagnosis may be irrelevant because treatment for the disease is not available, not needed, or not wanted.

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Most people will experience at least one diagnostic error in their lifetime, according to a report by the National Academies of Sciences, Engineering, and Medicine. Causes and factors of error in diagnosis are: When making a medical diagnosis, a lag time is a delay in time until a step towards diagnosis of a disease or condition is made. Types of lag times are mainly:. The plural of diagnosis is diagnoses. The verb is to diagnose, and a person who diagnoses is called a diagnostician.

Medical diagnosis or the actual process of making a diagnosis is a cognitive process. A clinician uses several sources of data and puts the pieces of the puzzle together to make a diagnostic impression. The initial diagnostic impression can be a broad term describing a category of diseases instead of a specific disease or condition. After the initial diagnostic impression, the clinician obtains follow up tests and procedures to get more data to support or reject the original diagnosis and will attempt to narrow it down to a more specific level.

Diagnostic procedures are the specific tools that the clinicians use to narrow the diagnostic possibilities. Diagnosis can take many forms. It might be a management-naming or prognosis-naming exercise. It may indicate either degree of abnormality on a continuum or kind of abnormality in a classification. It can be a brief summation or an extensive formulation, even taking the form of a story or metaphor.