Computer-aided diagnosis
This article needs more primary sources. (December 2023) |
Computer-aided diagnosis | |
---|---|
Purpose | computer assistance diagnosis of images |
Computer-aided detection (CADe), also called computer-aided diagnosis (CADx), are systems that assist doctors in the interpretation of
CAD also has potential future applications in digital pathology with the advent of whole-slide imaging and machine learning algorithms. So far its application has been limited to quantifying immunostaining but is also being investigated for the standard H&E stain.[1]
CAD is an
Computer-aided detection (CADe) systems are usually confined to marking conspicuous structures and sections. Computer-aided diagnosis (CADx) systems evaluate the conspicuous structures. For example, in mammography CAD highlights microcalcification clusters and hyperdense structures in the soft tissue. This allows the radiologist to draw conclusions about the condition of the pathology. Another application is CADq, which quantifies, e.g., the size of a tumor or the tumor's behavior in contrast medium uptake. Computer-aided simple triage (CAST) is another type of CAD, which performs a fully automatic initial interpretation and triage of studies into some meaningful categories (e.g. negative and positive). CAST is particularly applicable in emergency diagnostic imaging, where a prompt diagnosis of critical, life-threatening condition is required.
Although CAD has been used in clinical environments for over 40 years, CAD usually does not substitute the doctor or other professional, but rather plays a supporting role. The professional (generally a radiologist) is generally responsible for the final interpretation of a medical image.[2] However, the goal of some CAD systems is to detect earliest signs of abnormality in patients that human professionals cannot, as in diabetic retinopathy, architectural distortion in mammograms,[3][4] ground-glass nodules in thoracic CT,[5][6] and non-polypoid (“flat”) lesions in CT colonography.[7]
Topics
A Brief History
In the late 1950s, with the dawn of modern computers researchers in various fields started exploring the possibility of building computer-aided medical diagnostic (CAD) systems.[8] These first CAD systems used flow-charts, statistical pattern-matching, probability theory or knowledge bases to drive their decision-making process.[9]
Since the early 1970s, some of the very early CAD systems in medicine, which were often referred as “
are some of such examples.During the beginning of the early developments, the researchers were aiming at building entirely automated CAD / expert systems. The expectation of what computers can do was unrealistically optimistic among these scientists. However, after the breakthrough paper, “Reducibility among Combinatorial Problems” by Richard M. Karp,[13] it became clear that there were limitations but also potential opportunities when one develops algorithms to solve groups of important computational problems.[9]
As result of the new understanding of the various algorithmic limitations that Karp discovered in the early 1970s, researchers started realizing the serious limitations that CAD and expert systems in medicine have.[9] The recognition of these limitations brought the investigators to develop new kinds of CAD systems by using advanced approaches. Thus, by the late 1980s and early 1990s the focus sifted in the use of data mining approaches for the purpose of using more advanced and flexible CAD systems.
In 1998, the first commercial CAD system for mammography, the ImageChecker system, was approved by the US Food and Drug Administration (FDA). In the following years several commercial CAD systems for analyzing mammography, breast MRI, medical imagining of lung, colon, and heart also received FDA approvals. Currently, CAD systems are used as a diagnostic aid to provide physicians for better medical decision-making.[14]
Methodology
CAD is fundamentally based on highly complex pattern recognition. X-ray or other types of images are scanned for suspicious structures. Normally a few thousand images are required to optimize the algorithm. Digital image data are copied to a CAD server in a DICOM-format and are prepared and analyzed in several steps.
1. Preprocessing for
- Reduction of artifacts (bugs in images)
- Image noise reduction
- Leveling (harmonization) of image quality (increased contrast) for clearing the image's different basic conditions e.g. different exposure parameter.
- Filtering
2. Segmentation for
- Differentiation of different structures in the image, e.g. heart, lung, ribcage, blood vessels, possible round lesions
- Matching with anatomic databank
- Sample gray-values in volume of interest[15]
3. Structure/ROI (Region of Interest) Analyze Every detected region is analyzed individually for special characteristics:
- Compactness
- Form, size and location
- Reference to close by structures / ROIs
- Average grey level value analyze within a ROI
- Proportion of grey levels to border of the structure inside the ROI
4. Evaluation / classification After the structure is analyzed, every ROI is evaluated individually (scoring) for the probability of a TP. The following procedures are examples of classification algorithms.
- Nearest-Neighbor Rule (e.g. k-nearest neighbors)[16]
- Minimum distance classifier
- Cascade classifier
- Naive Bayes classifier
- Radial basis function network (RBF)
- Support vector machine (SVM)[22][23]
- Principal component analysis (PCA)
If the detected structures have reached a certain threshold level, they are highlighted in the image for the radiologist. Depending on the CAD system these markings can be permanently or temporary saved. The latter's advantage is that only the markings which are approved by the radiologist are saved. False hits should not be saved, because an examination at a later date becomes more difficult then.
Sensitivity and specificity
CAD systems seek to highlight suspicious structures. Today's CAD systems cannot detect 100% of pathological changes. The hit rate (sensitivity) can be up to 90% depending on system and application.[24] A correct hit is termed a True Positive (TP), while the incorrect marking of healthy sections constitutes a False Positive (FP). The less FPs indicated, the higher the specificity is. A low specificity reduces the acceptance of the CAD system because the user has to identify all of these wrong hits. The FP-rate in lung overview examinations (CAD Chest) could be reduced to 2 per examination. In other segments (e.g. CT lung examinations) the FP-rate could be 25 or more. In CAST systems the FP rate must be extremely low (less than 1 per examination) to allow a meaningful study triage.
Absolute detection rate
The absolute detection rate of the radiologist is an alternative metric to sensitivity and specificity. Overall, results of clinical trials about sensitivity, specificity, and the absolute detection rate can vary markedly. Each study result depends on its basic conditions and has to be evaluated on those terms. The following facts have a strong influence:
- Retrospective or prospective design
- Quality of the used images
- Condition of the x-ray examination
- Radiologist's experience and education
- Type of lesion
- Size of the considered lesion
Challenges that CAD in Medicine Faces Today
Despite the many developments that CAD has achieved since the dawn of computers, there are still certain challenges that CAD systems face today.[25]
Some challenges are related to various algorithmic limitations in the procedures of a CAD system including input data collection, preprocessing, processing and system assessments. Algorithms are generally designed to select a single likely diagnosis, thus providing suboptimal results for patients with multiple, concurrent disorders.
Due to the massive availability of data and the need to analyze such data, big data is also one of the biggest challenges that CAD systems face today. The increasingly vast amount of patient data is a serious problem. Often the patient data are complex and can be semi-structured or unstructured data. It requires highly developed approaches to store, retrieve and analyze them in reasonable time.[25]
During the preprocessing stage, input data requires to be normalized. The normalization of input data includes noise reduction, and filtering. Processing may contain a few sub-steps depending on applications. Basic three sub-steps on medical imaging are segmentation,
There is also a lack of standardized assessment measures for CAD Systems.[25] This fact may cause the difficulty for obtaining FDA approval for commercial use. Moreover, while many positive developments of CAD systems have been proven, studies for validating their algorithms for clinical practice has hardly been confirmed.[27]
Other challenges are related to the problem for healthcare providers to adopt new CAD systems in clinical practice. Some negative studies may discourage the use of CAD. In addition, the lack of training of health professionals on the use of CAD sometimes brings the incorrect interpretation of the system outcomes. These challenges are described in more detail in.[25]
Applications
CAD is used in the diagnosis of
Breast cancer
CAD is used in screening
Recent advances in
Procedures to evaluate mammography based on magnetic resonance imaging exist too.
Lung cancer (bronchial carcinoma)
In the diagnosis of lung cancer,
Early detection of lung cancer is valuable. However, the random detection of lung cancer in the early stage (stage 1) in the X-ray image is difficult. Round lesions that vary from 5–10 mm are easily overlooked.[31] The routine application of CAD Chest Systems may help to detect small changes without initial suspicion. A number of researchers developed CAD systems for detection of lung nodules (round lesions less than 30 mm) in chest radiography[32][33][34] and CT,[35][36] and CAD systems for diagnosis (e.g., distinction between malignant and benign) of lung nodules in CT. Virtual dual-energy imaging[37][38][39][40] improved the performance of CAD systems in chest radiography.[41]
Colon cancer
CAD is available for detection of
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.[44] 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.[45] To this end, various noninvasive and low-cost markers have been proposed, using ultrasound-image-based features.[46] These combine echogenicity, texture, and motion[47][48][49][50] characteristics to assist clinical decision towards improved prediction, assessment and management of cardiovascular risk.[51]
CAD is available for the automatic detection of significant (causing more than 50% stenosis) coronary artery disease in coronary CT angiography (CCTA) studies.[52]
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
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%.[55]
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.[56]
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.[57] Later, Zhang et al. proved removing spider-web plots did not influence the performance.[58] Genetic pattern search method was applied to identify abnormal brain from normal controls. Its classification accuracy was reported as 95.188%.[59] Das et al. proposed to use Ripplet transform.[60] Zhang et al. proposed to use particle swarm optimization (PSO).[61] Kalbkhani et al. suggested to use GARCH model.[62]
In 2014, El-Dahshan et al. suggested to use pulse coupled neural network.[63]
In 2015, Zhou et al. suggested to apply naive Bayes classifier to detect pathological brains.[64]
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.[57] 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.[65]
In 2019, Signaevsky et al. have first reported a trained Fully Convolutional Network (FCN) for detection and quantification of
Eigenbrain is a novel brain feature that can help to detect AD, based on principal component analysis (PCA)[67] or independent component analysis decomposition.[68] Polynomial kernel SVM has been shown to achieve good accuracy. The polynomial KSVM performs better than linear SVM and RBF kernel SVM.[69] Other approaches with decent results involve the use of texture analysis,[70] morphological features,[71] or high-order statistical features[72]
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.[73]
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.[74]
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.[75][76] 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.[76]
Image pre-processing, and feature extraction and classification are two main stages of these CAD algorithms.[77]
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.[78] 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.[77]
Histogram equalization is useful in enhancing contrast within an image.[80] 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.[77]
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.[81] 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.[77] In addition, green channel filtering can be used to detect center of optic disc in conjunction with double-windowing system.[citation needed]
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.[77] 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).[82] Walter-Klein transformation is then applied to achieve the uniform illumination.[82] This technique is the least used pre-processing method in the review from 2014.
Morphological operations is the second least used pre-processing method in 2014 review.[77] 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 is by far the most frequently used classifier in vessel segmentation, up to 90% of cases.[citation needed] 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.[83] 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.[77] Some beneficial properties of SVM include[83]
- 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.[75] 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.[citation needed]
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.[citation needed] Similar approaches that utilize the concept of centerline are the skeleton-based and differential geometry-based.[75]
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.[75] This technique is also being used in optic disc identification.[citation needed]
Model-based approaches employ representation to extract vessels from images. Three broad categories of model-based are known: deformable, parametric, and template matching.[75] 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.[84] 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,
See also
References
- ^ "Computer-aided Diagnosis: The Tipping Point for Digital Pathology". Digital Pathology Association. 27 April 2017.
- PMID 33937793.
- PMID 1509041.
- PMID 14500236.
- PMID 8668800.
- S2CID 2690415.
- S2CID 13487349.
- ISBN 978-1421446813.
- ^ S2CID 199019309.
- S2CID 118063112.
- PMID 7048091.
- ISBN 978-0451152640.
- ^ Richard M. Karp (1972). "Reducibility Among Combinatorial Problems" (PDF). In R. E. Miller; J. W. Thatcher (eds.). Complexity of Computer Computations. New York: Plenum. pp. 85–103. Archived from the original (PDF) on 2011-06-29. Retrieved 2019-08-14.
- PMID 17349778.
- PMID 26587549.
- S2CID 25181663.
- PMID 12906178.)
{{cite journal}}
: CS1 maint: numeric names: authors list (link - PMID 8551980.
- S2CID 18918667.
- PMID 17189044.
- .
- PMID 21626918.
- PMID 15894178.
- S2CID 73122975.
- ^ S2CID 198287435.
- S2CID 53758271.
- PMID 25652394.
- PMID 18353630.
- PMID 32984550.
- PMID 15816616.
- S2CID 31230950.
- PMID 3386584.
- S2CID 6280485.
- S2CID 15121082.
- S2CID 9800069.
- PMID 9719856.[permanent dead link]
- S2CID 206020910.
- S2CID 922550.
- S2CID 17961280.
- PMID 16859953.
- PMID 23193306.
- S2CID 4500060.
- PMID 17089846.
- ^ Golemati, Spyretta; Nikita, Konstantina (2019). Cardiovascular Computing-Methodologies and Clinical Applications. Springer.
- S2CID 5924749.
- S2CID 5653986.
- PMID 32641773.
- S2CID 225545904.
- S2CID 23333131.
- S2CID 11571104.
- PMID 24636805.
- S2CID 5627031.
- .
- .
- .
- .
- ^ S2CID 62615810.
- .
- .
- .
- PMID 24163610.
- .
- .
- ISBN 978-3-319-16482-3.
- .
- PMID 30770886.
- S2CID 9074888.
- PMID 23660005.
- PMID 26082713.
- S2CID 10069584.
- PMID 19437497.
- S2CID 30472186.
- ^ "EXINI Diagnostics".
- S2CID 20730927.
- ^ S2CID 17460643.
- ^ PMID 28024825.
- ^ S2CID 16465894.
- PMID 21963241.
- ^ Priya, R; Aruna, P (2011). "Review of automated diagnosis of diabetic retinopathy using the support vector machine". International Journal of Applied Engineering Research, Dindigul. 1 (4): 844–862.
- ^ Abedin, Zain ul (2023-11-10). "What is CAD/CAM?". Retrieved 2024-02-07.
- PMID 22551841.
- ^ S2CID 16382245.
- ^ .
- ISBN 9783540728481.
- PMID 29085178.
- ^ Mukherjee, Siddhartha (27 March 2017). "A.I. Versus M.D." The New Yorker. Retrieved 3 February 2018.
- ^ "Why scan-reading artificial intelligence is bad news for radiologists". The Economist. 29 November 2017. Retrieved 3 February 2018.
- S2CID 3662362.