Computer-Based Diagnostic Systems (Practitioner Series)

---

Computer-aided diagnosis - Wikipedia

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. It might be a means of communication such as a computer code through which it triggers payment, prescription, notification, information or advice.

It might be pathogenic or salutogenic. Once a diagnostic opinion has been reached, the provider is able to propose a management plan, which will include treatment as well as plans for follow-up. From this point on, in addition to treating the patient's condition, the provider can educate the patient about the etiology , progression, prognosis , other outcomes, and possible treatments of her or his ailments, as well as providing advice for maintaining health.

A treatment plan is proposed which may include therapy and follow-up consultations and tests to monitor the condition and the progress of the treatment, if needed, usually according to the medical guidelines provided by the medical field on the treatment of the particular illness. Relevant information should be added to the medical record of the patient. A failure to respond to treatments that would normally work may indicate a need for review of the diagnosis. Medical sign Symptom Syndrome. Medical diagnosis Differential diagnosis Prognosis.

Disease Eponymous disease Acronym or abbreviation. From Wikipedia, the free encyclopedia. History of medical diagnosis. Retrieved February 28, Langlois, Chapter 10 in Fundamentals of clinical practice Medicine That Monitors You".

The New York Times. Archived from the original on June 24, Retrieved September 11, It's here, says UC Riverside team". Improving Diagnosis in Health Care. The National Academies Press. Diagnosis and Risk Management in Primary Care: Retrieved 15 January Basic medical terms used to describe disease conditions. Medical examination and history taking. Inspection Auscultation Palpation Percussion. Temperature Heart rate Blood pressure Respiratory rate. Respiratory sounds Cyanosis Clubbing. Precordial examination Peripheral vascular examination Heart sounds Other Jugular venous pressure Abdominojugular test Carotid bruit Ankle-brachial pressure index.

Liver span Rectal Murphy's sign Bowel sounds. Mental state Mini—mental state examination Cranial nerve examination Upper limb neurological examination. Apgar score Ballard Maturational Assessment. Well-woman examination Vaginal examination Breast examination Cervical motion tenderness. Medical diagnosis Differential diagnosis. Bedside manner Cultural competence Diagnosis Education Universal precautions. Retrieved from " https: Medical diagnosis Medical terminology Nosology Advanced practice registered nursing. Views Read Edit View history.

In , Zhou et al. CADs can be used to identify subjects with Alzheimer's and mild cognitive impairment from normal elder controls. 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.

Supplemental Content

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.

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.

Computer-aided diagnosis

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. 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.

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.

From Wikipedia, the free encyclopedia. For computer aid in other medical fields, see Clinical decision support system. Not to be confused with Diagnosis artificial intelligence. X-ray of a hand, with automatic calculation of bone age by a computer software. The Tipping Point for Digital Pathology". American Journal of Roentgenology.

Medical diagnosis

Explicit use of et al. Journal of Medical Imaging. The New England Journal of Medicine. N Engl J Med. Two systematic reviews to compare effects on cancer detection and recall rate". European Journal of Cancer. Archived from the original PDF on 1 August Automated detection of nodules in peripheral lung fields". Ter Haar; Viergever, M. Computerized Medical Imaging and Graphics.

Result Filters

Physics in Medicine and Biology. Application to suppression of bony structures from chest radiographs". ROC Analyses without and with Localization". Brooke; Glazer, Daniel I. Suppression of rectal tubes". Advanced computer-aided detection scheme utilizing MTANNs for detection of "missed" polyps in a multicenter clinical trial". Jagannathan, Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network.

Biomedical Signal Processing and Control, Chatterjee, A Slantlet transform based intelligent system for magnetic resonance brain image classification. Progress in Electromagnetics Research. Mathew, Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network.

Pattern Recognition Letters,

• Ting and I: A Memoir of Love, Courage, and Devotion
• Land of the Pharaohs (The Adventures of Toby Digz)
• Bon Appetit, Yall: Recipes and Stories from Three Generations of Southern Cooking
• What the F*** Did I Do Last Night?: The memoir of an accidental comedian
• TV-a-Go-Go: Rock on TV from American Bandstand to American Idol