Artificial neural networks in chest radiography: application to the differential diagnosis of interstitial lung disease

Contribution to pulmonary diseases diagnostic from X-ray images using innovative deep learning models

Pulmonary disease identification and characterization are among the most intriguing research topics of recent years since they require an accurate and prompt diagnosis. Although pulmonary radiography has helped in lung disease diagnosis, the interpretation of the radiographic image has always been a major concern for doctors and radiologists to reduce diagnosis errors. Due to their success in image classification and segmentation tasks, cutting-edge artificial intelligence techniques like machine learning (ML) and deep learning (DL) are widely encouraged to be applied in the field of diagnosing lung disorders and identifying them using medical images, particularly radiographic ones. For this end, the researchers are concurring to build systems based on these techniques in particular deep learning ones. In this paper, we proposed three deep-learning models that were trained to identify the presence of certain lung diseases using thoracic radiography. The first model, named "CovCXR-Net", identifies the COVID-19 disease (two cases: COVID-19 or normal). The second model, named "MDCXR3-Net", identifies the COVID-19 and pneumonia diseases (three cases: COVID-19, pneumonia, or normal), and the last model, named "MDCXR4-Net", is destined to identify the COVID-19, pneumonia and the pulmonary opacity diseases (4 cases: COVID-19, pneumonia, pulmonary opacity or normal). These models have proven their superiority in comparison with the state-of-the-art models and reached an accuracy of 99,09 %, 97.74 %, and 90,37 % respectively with three benchmarks.

Review on chest pathogies detection systems using deep learning techniques

Chest radiography is the standard and most affordable way to diagnose, analyze, and examine different thoracic and chest diseases. Typically, the radiograph is examined by an expert radiologist or physician to decide about a particular anomaly, if exists. Moreover, computer-aided methods are used to assist radiologists and make the analysis process accurate, fast, and more automated. A tremendous improvement in automatic chest pathologies detection and analysis can be observed with the emergence of deep learning. The survey aims to review, technically evaluate, and synthesize the different computer-aided chest pathologies detection systems. The state-of-the-art of single and multi-pathologies detection systems, which are published in the last five years, are thoroughly discussed. The taxonomy of image acquisition, dataset preprocessing, feature extraction, and deep learning models are presented. The mathematical concepts related to feature extraction model architectures are discussed. Moreover, the different articles are compared based on their contributions, datasets, methods used, and the results achieved. The article ends with the main findings, current trends, challenges, and future recommendations.

Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions

BACKGROUND

Having several applications in medicine, and in ophthalmology in particular, artificial intelligence (AI) tools have been used to detect visual function deficits, thus playing a key role in diagnosing eye diseases and in predicting the evolution of these common and disabling diseases. AI tools, i.e., artificial neural networks (ANNs), are progressively involved in detecting and customized control of ophthalmic diseases. The studies that refer to the efficiency of AI in medicine and especially in ophthalmology were analyzed in this review.

MATERIALS AND METHODS

We conducted a comprehensive review in order to collect all accounts published between 2015 and 2022 that refer to these applications of AI in medicine and especially in ophthalmology. Neural networks have a major role in establishing the demand to initiate preliminary anti-glaucoma therapy to stop the advance of the disease.

RESULTS

Different surveys in the literature review show the remarkable benefit of these AI tools in ophthalmology in evaluating the visual field, optic nerve, and retinal nerve fiber layer, thus ensuring a higher precision in detecting advances in glaucoma and retinal shifts in diabetes. We thus identified 1762 applications of artificial intelligence in ophthalmology: review articles and research articles (301 pub med, 144 scopus, 445 web of science, 872 science direct). Of these, we analyzed 70 articles and review papers (diabetic retinopathy (N = 24), glaucoma (N = 24), DMLV (N = 15), other pathologies (N = 7)) after applying the inclusion and exclusion criteria.

CONCLUSION

In medicine, AI tools are used in surgery, radiology, gynecology, oncology, etc., in making a diagnosis, predicting the evolution of a disease, and assessing the prognosis in patients with oncological pathologies. In ophthalmology, AI potentially increases the patient's access to screening/clinical diagnosis and decreases healthcare costs, mainly when there is a high risk of disease or communities face financial shortages. AI/DL (deep learning) algorithms using both OCT and FO images will change image analysis techniques and methodologies. Optimizing these (combined) technologies will accelerate progress in this area.

A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification

Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.

Use of Artificial Neural Networks to Predict the Progression of Glaucoma in Patients with Sleep Apnea

Aim: To construct neural models to predict the progression of glaucoma in patients with sleep apnea. Materials and Methods: Modeling the use of neural networks was performed using the Neurosolutions commercial simulator. The built databases gather information on a group of patients with primitive open-angle glaucoma and normal-tension glaucoma, who have been associated with sleep apnea syndrome and various stages of disease severity. The data within the database were divided as follows: 65 were used in the neural network training stage and 8 were kept for the validation stage. In total, 21 parameters were selected as input parameters for neural models including: age of patients, BMI (body mass index), systolic and diastolic blood pressure, intraocular pressure, central corneal thickness, corneal biomechanical parameters (IOPcc, HC, CRF), AHI, desaturation index, nocturnal oxygen saturation, remaining AHI, type of apnea, and associated general conditions (diabetes, hypertension, obesity, COPD). The selected output parameters are: c/d ratio, modified visual field parameters (MD, PSD), ganglion cell layer thickness. Forward-propagation neural networks (multilayer perceptron) were constructed with a layer of hidden neurons. The constructed neural models generated the output values for these data. The obtained results were then compared with the experimental values. Results: The best results were obtained during the training stage with the ANN network (21:35:4). If we consider a 25% confidence interval, we find that very good results are obtained during the validation stage, except for the average GCL thickness, for which the errors are slightly higher. Conclusions: Excellent results were obtained during the validation stage, which support the results obtained in other studies in the literature that strengthen the connection between sleep apnea syndrome and glaucoma changes.

Deep Convolutional Neural Networks for Chest Diseases Detection

Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. The timely diagnosis of chest diseases is very important. Many methods have been developed for this purpose. In this paper, we demonstrate the feasibility of classifying the chest pathologies in chest X-rays using conventional and deep learning approaches. In the paper, convolutional neural networks (CNNs) are presented for the diagnosis of chest diseases. The architecture of CNN and its design principle are presented. For comparative purpose, backpropagation neural networks (BPNNs) with supervised learning, competitive neural networks (CpNNs) with unsupervised learning are also constructed for diagnosis chest diseases. All the considered networks CNN, BPNN, and CpNN are trained and tested on the same chest X-ray database, and the performance of each network is discussed. Comparative results in terms of accuracy, error rate, and training time between the networks are presented.

Intelligent Computing in Medical Imaging

Biomedical imaging is considered main procedure to acquire valuable physical information about the human body and some other biological species. It produces specialized images of different parts of the biological species for clinical analysis. It assimilates various specialized domains including nuclear medicine, radiological imaging, Positron emission tomography (PET), and microscopy. From the early discovery of X-rays, progress in biomedical imaging continued resulting in highly sophisticated medical imaging modalities, such as magnetic resonance imaging (MRI), ultrasound, Computed Tomography (CT), and lungs monitoring. These biomedical imaging techniques assist physicians for faster and accurate analysis and treatment. The present chapter discussed the impact of intelligent computing methods for biomedical image analysis and healthcare. Different Artificial Intelligence (AI) based automated biomedical image analysis are considered. Different approaches are discussed including the AI ability to resolve various medical imaging problems. It also introduced the popular AI procedures that employed to solve some special problems in medicine. Artificial Neural Network (ANN) and support vector machine (SVM) are active to classify different types of images from various imaging modalities. Different diagnostic analysis, such as mammogram analysis, MRI brain image analysis, CT images, PET images, and bone/retinal analysis using ANN, feed-forward back propagation ANN, probabilistic ANN, and extreme learning machine continuously. Various optimization techniques of ant colony optimization (ACO), genetic algorithm (GA), particle swarm optimization (PSO) and other bio-inspired procedures are also frequently conducted for feature extraction/selection and classification. The advantages and disadvantages of some AI approaches are discussed in the present chapter along with some suggested future research perspectives.

Two-dimensional MR Spectroscopic Characterization of Breast Cancer In Vivo

The major goal of this work was to characterize invasive ductal carcinoma and healthy fatty breast tissues noninvasively using the classification and regression tree analysis (CART) of 2D MR spectral data. 2D L-COSY spectra were acquired in 14 invasive breast carcinoma and 21 healthy fatty breasts using a GE 1.5 Tesla MRI/MRS scanner equipped with a 2-channel phased-array breast MR coil. The 2D spectra were recorded in approximately 10 minutes using a minimum voxel size of 1 ml without any water suppression technique. For healthy breasts, spectra were acquired from at least one fatty region. 2D L-COSY spectra were recorded in a total of 43 voxels. Five diagonal and six cross peak volumes were integrated and at least eighteen ratios were selected as potential features for the statistical method, namely CART. The 2D L-COSY data showed a significant increase for the majority of these ratios in invasive breast carcinomas compared to healthy fatty tissues. Better accuracy of identifying carcinomas and fatty tissues is reported using CART analysis of different combinations of ratios calculated from the relative levels of water, choline, and saturated and unsaturated lipids. This is a first report on the statistical classification of 2D L-COSY in human breast carcinomas in vivo.

Dynamic chest radiography: flat-panel detector (FPD) based functional X-ray imaging

Dynamic chest radiography is a flat-panel detector (FPD)-based functional X-ray imaging, which is performed as an additional examination in chest radiography. The large field of view (FOV) of FPDs permits real-time observation of the entire lungs and simultaneous right-and-left evaluation of diaphragm kinetics. Most importantly, dynamic chest radiography provides pulmonary ventilation and circulation findings as slight changes in pixel value even without the use of contrast media; the interpretation is challenging and crucial for a better understanding of pulmonary function. The basic concept was proposed in the 1980s; however, it was not realized until the 2010s because of technical limitations. Dynamic FPDs and advanced digital image processing played a key role for clinical application of dynamic chest radiography. Pulmonary ventilation and circulation can be quantified and visualized for the diagnosis of pulmonary diseases. Dynamic chest radiography can be deployed as a simple and rapid means of functional imaging in both routine and emergency medicine. Here, we focus on the evaluation of pulmonary ventilation and circulation. This review article describes the basic mechanism of imaging findings according to pulmonary/circulation physiology, followed by imaging procedures, analysis method, and diagnostic performance of dynamic chest radiography.

Neural network evaluation of PET scans of the liver: a potentially useful adjunct in clinical interpretation

PURPOSE

To assess the performance of an artificial neural network in the evaluation of fluorine 18 fluorodeoxyglucose (FDG) uptake in the liver, compared with the results of expert interpretation of abdominal liver magnetic resonance (MR) images.

MATERIALS AND METHODS

The study was approved by the institutional human research committee and was HIPAA compliant, with waiver of informed consent. Digital data from positron emission tomographic (PET)/computed tomographic (CT) examinations, along with patient demographics, were obtained from 98 consecutive patients who underwent both whole-body PET/CT examinations and liver MR imaging examinations within 2 months. Interpretations of the scans from PET/CT examinations by trained neural networks were cross-classified with expert interpretations of the findings on images from MR examinations for intrahepatic benignity or malignancy. Receiver operating characteristic (ROC) curves were obtained for the designed networks. The significance of the difference between neural network ROC curves and the ROC curves detailing the performance of two expert blinded observers in the interpretation of liver FDG uptake was determined.

RESULTS

A neural network incorporating lesion data demonstrated an ROC curve with an area under the curve (AUC) of 0.905 (standard error, 0.0370). A network independent of lesion data demonstrated an ROC curve with an AUC of 0.896 (standard error, 0.0386). These results compare favorably with results of expert blinded observers 1 and 2 who demonstrated ROCs with AUCs of 0.786 (standard error, 0.0522) and 0.796 (standard error, 0.0514), respectively. Following unblinding to network data, the AUCs for readers 1 and 2 improved to 0.924 (standard error, 0.0335) and 0.881 (standard error, 0.0409), respectively.

CONCLUSION

Computers running artificial neural networks employing PET/CT scan data are sensitive and specific in the designation of the presence of intrahepatic malignancy, with comparison with interpretation by expert observers. When used in conjunction with human expertise, network data improve accuracy of the human interpreter.

A comparative study on chronic obstructive pulmonary and pneumonia diseases diagnosis using neural networks and artificial immune system

Millions of people are diagnosed every year with a chest disease in the world. Chronic obstructive pulmonary and pneumonia diseases are two of the most important chest diseases. And these are very common illnesses in Turkey. In this paper, a comparative study on chronic obstructive pulmonary and pneumonia diseases diagnosis was realized by using neural networks and artificial immune systems. For this purpose, three different neural networks structures and one artificial immune system were used. Used neural network structures in this study were multilayer, probabilistic, and learning vector quantization neural networks. The results of the study were compared with the results of the pervious similar studies reported focusing on chronic obstructive pulmonary and pneumonia diseases diagnosis. The chronic obstructive pulmonary and pneumonia diseases dataset were prepared from a chest diseases hospital's database using patient's epicrisis reports.

Feasibility of automated quantification of regional disease patterns depicted on high-resolution computed tomography in patients with various diffuse lung diseases

OBJECTIVE

This study was designed to develop an automated system for quantification of various regional disease patterns of diffuse lung diseases as depicted on high-resolution computed tomography (HRCT) and to compare the performance of the automated system with human readers.

MATERIALS AND METHODS

A total of 600 circular regions-of-interest (ROIs), 10 pixels in diameter, were utilized. The 600 ROIs comprised 100 ROIs that represented six typical regional patterns (normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). The ROIs were used to train the automated classification system based on the use of a Support Vector Machine classifier and 37 features of texture and shape. The performance of the classification system was tested with a 5-fold cross-validation method. An automated quantification system was developed with a moving ROI in the lung area, which helped classify each pixel into six categories. A total of 92 HRCT images obtained from patients with different diseases were used to validate the quantification system. Two radiologists independently classified lung areas of the same CT images into six patterns using the manual drawing function of dedicated software. Agreement between the automated system and the readers and between the two individual readers was assessed.

RESULTS

The overall accuracy of the system to classify each disease pattern based on the typical ROIs was 89%. When the quantification results were examined, the average agreement between the system and each radiologist was 52% and 49%, respectively. The agreement between the two radiologists was 67%.

CONCLUSION

An automated quantification system for various regional patterns of diffuse interstitial lung diseases can be used for objective and reproducible assessment of disease severity.

Differentiation of common large sellar-suprasellar masses effect of artificial neural network on radiologists' diagnosis performance

RATIONALE AND OBJECTIVES

When pituitary adenoma, craniopharyngioma, and Rathke's cleft cyst grow in the sellar and suprasellar region, it is often difficult to differentiate among these three lesions on magnetic resonance (MR) images. The purpose of this study was to apply an artificial neural network (ANN) for differential diagnosis among these three lesions with MR images and retrospectively evaluate the effect of ANN output on radiologists' performance.

MATERIALS AND METHODS

Forty-three patients with sellar-suprasellar masses were studied. The ANN was designed to differentiate among pituitary adenoma, craniopharyngioma, and Rathke's cleft cyst by using patients' ages and nine MR image findings obtained by three neuroradiologists using a subjective rating scale. In the observer performance test, MR images were viewed by nine radiologists, including four neuroradiologists and five general radiologists, first without and then with ANN output. The radiologists' performance was evaluated using receiver-operating characteristic analysis with a continuous rating scale.

RESULTS

The ANN showed high performance in differentiation among the three lesions (area under the receiver-operating characteristic curve, 0.990). The average area under the curve for all radiologists for differentiation among the three lesions increased significantly from 0.910 to 0.985 (P = .0024) when they used the computer output. Areas under the curves for the general radiologists and neuroradiologists increased from 0.876 to 0.983 (P = .0083) and from 0.952 to 0.989 (P = .038), respectively.

CONCLUSION

In diagnostic performance for differentiation among pituitary macroadenoma, craniopharyngioma, and Rathke's cleft cyst with MR imaging, the ANN resulted in parity between neuroradiologists and general radiologists.

A study on chronic obstructive pulmonary disease diagnosis using multilayer neural networks

Chronic Obstructive Pulmonary Disease (COPD) is a disease state characterized by airflow limitation that is not fully reversible. The airflow limitation is usually both progressive and associated with an abnormal inflammatory response of the lungs to noxious particles or gases. COPD is important health problem and one of the most common illnesses in Turkey. It is generally accepted that cigarette smoking is the most important risk factor and genetic factors are believed to play a role in the individual susceptibility. In this study, a study on COPD diagnosis was realized by using multilayer neural networks (MLNN). For this purpose, two different MLNN structures were used. One of the structures was the MLNN with one hidden layer and the other was the MLNN with two hidden layers. Back propagation with momentum and Levenberg-Marquardt algorithms were used for the training of the neural networks. The COPD dataset were prepared from a chest diseases hospital's database using patient's epicrisis reports.

Investigation of breast cancer using two-dimensional MRS

Proton (1H) MRS enables non-invasive biochemical assay with the potential to characterize malignant, benign and healthy breast tissues. In vitro studies using perchloric acid extracts and ex vivo magic angle spinning spectroscopy of intact biopsy tissues have been used to identify detectable metabolic alterations in breast cancer. The challenges of 1H MRS in vivo include low sensitivity and significant overlap of resonances due to limited chemical shift dispersion and significant inhomogeneous broadening at most clinical magnetic field strengths. Improvement in spectral resolution can be achieved in vivo and in vitro by recording the MR spectra spread over more than one dimension, thus facilitating unambiguous assignment of metabolite and lipid resonances in breast cancer. This article reviews the recent progress with two-dimensional MRS of breast cancer in vitro, ex vivo and in vivo. The discussion includes unambiguous detection of saturated and unsaturated fatty acids, as well as choline-containing groups such as free choline, phosphocholine, glycerophosphocholine and ethanolamines using two-dimensional MRS. In addition, characterization of invasive ductal carcinomas and healthy fatty/glandular breast tissues non-invasively using the classification and regression tree (CART) analysis of two-dimensional MRS data is reviewed.

Computer-aided diagnosis in medical imaging: historical review, current status and future potential

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. In this article, the motivation and philosophy for early development of CAD schemes are presented together with the current status and future potential of CAD in a PACS environment. With CAD, radiologists use the computer output as a "second opinion" and make the final decisions. CAD is a concept established by taking into account equally the roles of physicians and computers, whereas automated computer diagnosis is a concept based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, a large number of CAD systems have been employed for assisting physicians in the early detection of breast cancers on mammograms. A CAD scheme that makes use of lateral chest images has the potential to improve the overall performance in the detection of lung nodules when combined with another CAD scheme for PA chest images. Because vertebral fractures can be detected reliably by computer on lateral chest radiographs, radiologists' accuracy in the detection of vertebral fractures would be improved by the use of CAD, and thus early diagnosis of osteoporosis would become possible. In MRA, a CAD system has been developed for assisting radiologists in the detection of intracranial aneurysms. On successive bone scan images, a CAD scheme for detection of interval changes has been developed by use of temporal subtraction images. In the future, many CAD schemes could be assembled as packages and implemented as a part of PACS. For example, the package for chest CAD may include the computerized detection of lung nodules, interstitial opacities, cardiomegaly, vertebral fractures, and interval changes in chest radiographs as well as the computerized classification of benign and malignant nodules and the differential diagnosis of interstitial lung diseases. In order to assist in the differential diagnosis, it would be possible to search for and retrieve images (or lesions) with known pathology, which would be very similar to a new unknown case, from PACS when a reliable and useful method has been developed for quantifying the similarity of a pair of images for visual comparison by radiologists.

Computerized detection of vertebral compression fractures on lateral chest radiographs: preliminary results with a tool for early detection of osteoporosis

Vertebral fracture (or vertebral deformity) is a very common outcome of osteoporosis, which is one of the major public health concerns in the world. Early detection of vertebral fractures is important because timely pharmacologic intervention can reduce the risk of subsequent additional fractures. Chest radiographs are used routinely for detection of lung and heart diseases, and vertebral fractures can be visible on lateral chest radiographs. However, investigators noted that about 50% of vertebral fractures visible on lateral chest radiographs were underdiagnosed or under-reported, even when the fractures were severe. Therefore, our goal was to develop a computerized method for detection of vertebral fractures on lateral chest radiographs in order to assist radiologists' image interpretation and thus allow the early diagnosis of osteoporosis. The cases used in this study were 20 patients with severe vertebral fractures and 118 patients without fractures, as confirmed by the consensus of two radiologists. Radiologists identified the locations of fractured vertebrae, and they provided morphometric data on the vertebral shape for evaluation of the accuracy of detecting vertebral end plates by computer. In our computerized method, a curved search area, which included a number of vertebral end plates, was first extracted automatically, and was straightened so that vertebral end plates became oriented horizontally. Edge candidates were enhanced by use of a horizontal line-enhancement filter in the straightened image, and a multiple thresholding technique, followed by feature analysis, was used for identification of the vertebral end plates. The height of each vertebra was determined from locations of identified vertebral end plates, and fractured vertebrae were detected by comparison of the measured vertebral height with the expected height. The sensitivity of our computerized method for detection of fracture cases was 95% (19/20), with 1.03 (139/135) false-positive fractures per image. The accuracy of identifying vertebral end plates, marked by radiologists in a morphometric study, was 76.6% (400/522) and 70.9% (420/592) for cases used for training and those for testing, respectively. We prepared 32 additional fracture cases for a validation test, and we examined the detection accuracy of our computerized method. The sensitivity for these cases was 75% (24/32) at 1.03 (33/32) false-positive fractures per image. Our preliminary results show that the automated computerized scheme for detecting vertebral fractures on lateral chest radiographs has the potential to assist radiologists in detecting vertebral fractures.

Computer-aided diagnosis in chest radiography

We have developed computer-aided diagnosis (CAD) schemes for the detection of lung nodules, interstitial lung diseases, interval changes, and asymmetric opacities, and also for the differential diagnosis of lung nodules and interstitial lung diseases on chest radiographs. Observer performance studies indicate clearly that radiologists' diagnostic accuracy was improved significantly when radiologists used a computer output in their interpretations of chest radiographs. In addition, the automated recognition methods for the patient and the projection view by use of chest radiographs were useful for integrating the chest CAD schemes into the picture-archiving and communication system (PACS).

Computer-aided classification of interstitial lung diseases via MDCT: 3D adaptive multiple feature method (3D AMFM)

RATIONALE AND OBJECTIVES

Computer-aided detection algorithms applied to multidetector row CT (MDCT) lung image data sets have the potential to significantly alter clinical practice through the early, quantitative detection of pulmonary pathology. In this project, we have further developed a computer-aided detection tool, the adaptive multiple feature method (AMFM), for the detection of interstitial lung diseases based on MDCT-generated volumetric data.

MATERIALS AND METHODS

We performed MDCT (Siemens Sensation 16 or 64 120 kV, B50f convolution kernel, and <or=0.75-mm slice thickness) on 20 human volunteers recruited from four cohorts studied under an National Institutes of Health-sponsored Bioengineering Research Partnership Grant: 1) normal never smokers; 2) normal smokers; 3) those with emphysema, and 4) those with interstitial lung disease (total: 11 males, 9 females; age range 20-75 years, mean age 40 years). A total of 1,184 volumes of interest (VOIs; 21 x 21 pixels in plane) were marked by a senior radiologist and a senior pulmonologist as emphysema (EMPH, n = 287); ground-glass (GG, n = 147), honeycombing (HC, n = 137), normal nonsmokers (NN, n = 287), and normal smokers (NS, n = 326). For each VOI, we calculated 24 volumetric features, including statistical features (first-order features, run-length, and co-occurrence features), histogram, and fractal features. We compared two methods of classification (a Support Vector Machine (SVM) and a Bayesian classifier) using a 10-fold cross validation method and McNemar's test.

RESULTS

The sensitivity of five patterns in the form of Bayesian/SVM was: EMPH: 91/93%; GG: 89/86%; HC: 93/90%; NN: 90/73%; and NS: 75/82%. The specificity of five patterns in the form of Bayesian/support vector machine was: EMPH: 98/98%; GG: 98/98%; HC: 99/99%; NN: 90/94%; and NS: 96/91%.

CONCLUSION

We conclude that volumetric features including statistical features, histogram and fractal features can be successfully used in differentiation of parenchymal pathology associated with both emphysema and interstitial lung diseases. Additionally, support vector machine and Bayesian methods are comparable classifiers for characterization of interstitial lung diseases on MDCT images.

Usefulness of artificial neural network for differential diagnosis of hepatic masses on CT images

RATIONALE AND OBJECTIVE

Our purpose in this study is to apply an artificial neural network (ANN) for differential diagnosis of certain hepatic masses on computed tomographic (CT) images and evaluate the effect of ANN output on radiologist diagnostic performance.

MATERIALS AND METHODS

We collected 120 cases of hepatic disease. We used a single three-layer feed-forward ANN with a back-propagation algorithm. The ANN is designed to differentiate four hepatic masses (hepatocellular carcinoma, intrahepatic peripheral cholangiocarcinoma, hemangioma, and metastasis) by using nine clinical parameters and 24 radiological findings in dual-phase contrast-enhanced CT images. Thus, the ANN consisted of 33 input units and four output units. Subjective ratings for the 24 radiological findings were provided independently by two attending radiologists. All clinical cases were used for training and testing of the ANN by implementation of a round-robin technique. In the observer test, CT images of all 120 cases (30 cases for each disease) were used. CT images were viewed by seven radiologists first without and then with ANN output. Radiologist performance was evaluated by using receiver operating characteristic (ROC) analysis on a continuous rating scale.

RESULTS

Averaged area under the ROC curve for ANN alone was 0.961. The diagnostic performance of seven radiologists increased from 0.888 to 0.934 (P < .02) when they used ANN output.

CONCLUSION

The ANN can provide useful output as a second opinion to improve radiologist diagnostic performance in the differential diagnosis of hepatic masses seen on contrast-enhanced CT.

Potential usefulness of biological fingerprints in chest radiographs for automated patient recognition and identification

RATIONALE AND OBJECTIVES

The purpose of this study was to demonstrate the potential usefulness of "biological fingerprints" in chest radiographs for automated patient recognition and identification.

MATERIALS AND METHODS

Thoracic fields, cardiac shadows, the superior mediastinum, lung apices, a part of the right lung, and the right lower lung that includes the costophrenic angle were used as biological fingerprints in chest radiographs. Each of the biological fingerprints in a current chest radiograph was used as a template for determination of the correlation value with the corresponding biological fingerprint in a previous chest radiograph for patient recognition and identification. The overall performance of the method developed was examined in terms of receiver operating characteristic curves.

RESULTS

Receiver operating characteristic curves obtained with different biological fingerprints, except for the part of the right lung, indicated a high performance in identifying patients. These results showed that a new concept of biological fingerprints in radiologic images would be useful in patient recognition and identification. The low performance with the part of the right lung seems to be related to a general observation that this region does not usually include features unique to a specific patient. The performance of the artificial neural networks by use of a combination of five biological fingerprints was higher than results obtained with each biological fingerprint.

CONCLUSION

The use of automated patient identification based on biological fingerprints in chest radiographs is promising for helping to discover misfiled patient images, especially in a picture archiving and communication system environment.

Projection profile analysis for identifying different views of chest radiographs

RATIONALE AND OBJECTIVES

For computerized analysis of chest images in the clinical environment, identification of frontal (posteroanterior/anteroposterior) and lateral chest radiographs is an important preprocessing step. In this study, we developed a method to distinguish frontal from lateral views of the chest radiographs based on an analysis of the projection profile.

MATERIALS AND METHODS

Projection profile is obtained by projecting a chest image on to the mediolateral axis. Two indices, body symmetry index and background percentage index, are computed from the projection profile. The combination of body symmetry index and background percentage index is used to determine the view of chest radiographs. The method is evaluated on a sample of 2000 frontal and 1000 lateral chest images.

RESULTS

The values of body symmetry index are found to be 1.18 +/- 0.23 and 3.07 +/- 1.42 for frontal and lateral chest images, respectively. The values of background percentage index are found to be 0.03 +/- 0.05 and 0.33 +/- 0.09 for frontal and lateral chest images, respectively. The discrimination is evaluated by linear discriminant analysis and receiver operating characteristic analysis. Area Az under the receiver operating characteristic curve with the combination of the two indices is 0.993.

CONCLUSION

The method can be used as a preprocessing step for further analysis in chest radiographs.

How can a massive training artificial neural network (MTANN) be trained with a small number of cases in the distinction between nodules and vessels in thoracic CT?

RATIONALE AND OBJECTIVES

To demonstrate that a massive training artificial neural network (MTANN) can be adequately trained with a small number of cases in the distinction between nodules and vessels (non-nodules) in thoracic computed tomography (CT) images.

MATERIALS AND METHODS

An MTANN is a trainable, highly nonlinear filter consisting of a linear-output multilayer artificial neural network model. For enhancement of nodules and suppression of vessels, we used 10 nodules and 10 non-nodule images as training cases for MTANNs. The MTANN is trained with a large number of input subregions selected from the training cases and the corresponding pixels in teaching images that contain Gaussian distributions for nodules and zero for non-nodules. We trained three MTANNs with different numbers (1, 9, and 361) of training samples (pairs of the subregion and the teaching pixel) selected from the training cases. In order to investigate the basic characteristics of the trained MTANNs, we applied the MTANNs to simulated CT images containing various-sized model nodules (spheres) with different contrasts and various-sized model vessels (cylinders) with different orientations. In addition, we applied the trained MTANNs to nontraining actual clinical cases with 59 nodules and 1,726 non-nodules.

RESULTS

In the output images for the simulated CT images by use of the MTANNs trained with small numbers (one and nine) of subregions, model vessels were clearly visible and were not removed; thus, the MTANNs were not trained properly. However, in the output image of the MTANN trained with a large number of subregions, various-sized model nodules with different contrasts were represented by light nodular distributions, whereas various-sized model vessels with different orientations were dark and thus were almost removed. This result indicates that the MTANN was able to learn, from a very small number of actual nodule and non-nodule cases, the distinction between nodules (spherelike objects) and vessels (cylinder-like objects). In nontraining clinical cases, the MTANN was able to distinguish actual nodules from actual vessels in CT images. For 59 actual nodules and 1,726 non-nodules, the performance of the MTANN decreased as the number of training samples (subregions) in each case decreased.

CONCLUSIONS

The MTANN can be trained with a very small number of training cases (10 nodules and 10 non-nodules) in the distinction between nodules and non-nodules (vessels) in CT images. Massive training by scanning of training cases to produce a large number of training samples (input subregions and teaching pixels) would contributed to a high generalization ability of the MTANN.

Current status and future potential of computer-aided diagnosis in medical imaging

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. The basic concept of CAD is to provide a computer output as a second opinion to assist radiologists' image interpretation by improving the accuracy and consistency of radiological diagnosis and also by reducing the image reading time. In this article, a number of CAD schemes are presented, with emphasis on potential clinical applications. These schemes include: (1) detection and classification of lung nodules on digital chest radiographs; (2) detection of nodules in low dose CT; (3) distinction between benign and malignant nodules on high resolution CT; (4) usefulness of similar images for distinction between benign and malignant lesions; (5) quantitative analysis of diffuse lung diseases on high resolution CT; and (6) detection of intracranial aneurysms in magnetic resonance angiography. Because CAD can be applied to all imaging modalities, all body parts and all kinds of examinations, it is likely that CAD will have a major impact on medical imaging and diagnostic radiology in the 21st century.

Overview on research and development of computer-aided diagnostic schemes

Computer-aided diagnosis (CAD) has become a practical clinical approach in diagnostic radiology, although at present only in the area of detection of breast cancer in mammograms. Current research efforts have been focused on detection and classification of images of many different types of lesions in a number of organs, obtained with various imaging modalities. It is likely that the present results of CAD are only at the tip of the iceberg. Although automated computer diagnosis is a concept based on computer algorithms only, CAD is a concept established by taking into account equally the roles of physicians and computers. The effect of CAD on differential diagnosis has already indicated that the performance level is high, and that CAD would be ready for clinical trials and commercialization efforts. The presentation of images similar to those of an unknown case may be useful as a supplemental tool for CAD in the differential diagnosis.

Computer-aided diagnosis in chest radiology

Chest radiography is still a useful examination in various situations, although CT has become a modality of choice as a diagnostic examination in many cases. Current computer-aided diagnosis (CAD) schemes for chest radiographs include nodule detection, interstitial disease detection, temporal subtraction, differential diagnosis of interstitial disease, and distinction between benign and malignant pulmonary nodules. All of these schemes are demonstrated as providing potentially useful tools for radiologists when the output of these schemes is used as a "second opinion." There are some commercially available products for these schemes and more are expected to be available in the near future. The current status of CAD for CT is also discussed briefly in this article.

Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumours in ultrasound images

The purpose of this study was to apply a novel method of multiscale echo texture analysis for distinguishing benign (hemangiomas) from malignant (hepatocellular carcinomas (HCCs) and metastases) focal liver lesions in B-mode ultrasound images. In this method, regions of interest (ROIs) extracted from within the lesions were decomposed into subimages by wavelet packets. Multiscale texture features that quantify homogeneity of the echogenicity were calculated from these subimages and were combined by an artificial neural network (ANN). A subset of the multiscale features was selected that yielded the highest performance in the classification of lesions measured by the area under the receiver operating characteristic curve (Az). In an analysis of 193 ROIs consisting of 50 hemangiomas, 87 hepatocellular carcinomas and 56 metastases, the multiscale features yielded a high A: value of 0.92 in distinguishing benign from malignant lesions, 0.93 in distinguishing hemangiomas from HCCs and 0.94 in distinguishing hemangiomas from metastases. Our new multiscale texture analysis method can effectively differentiate malignant from benign lesions, and thus has the potential to increase the accuracy of diagnosis of focal liver lesions in ultrasound images.

Application of an Artificial Neural Network to High-Resolution CT: Usefulness in Differential Diagnosis of Diffuse Lung Disease

OBJECTIVE

The purpose of our study was to evaluate the diagnostic performance of an artificial neural network (ANN) in differentiating among certain diffuse lung diseases using high-resolution CT (HRCT) and the effect of ANN output on radiologists' diagnostic performance.

MATERIALS AND METHODS

We selected 130 clinical cases of diffuse lung disease. We used a single three-layer, feed-forward ANN with a back-propagation algorithm. The ANN was designed to differentiate among 11 diffuse lung diseases by using 10 clinical parameters and 23 HRCT features. Therefore, the ANN consisted of 33 input units and 11 output units. Subjective ratings for 23 HRCT features were provided independently by eight radiologists. All clinical cases were used for training and testing of the ANN by implementing a round-robin technique. In the observer test, a subset of 45 cases was selected from the database of 130 cases. HRCT images were viewed by eight radiologists first without and then with ANN output. The radiologists' performance was evaluated with receiver operating characteristic (ROC) analysis with a continuous rating scale.

RESULTS

The average area under the ROC curve for ANN performance obtained with all clinical parameters and HRCT features was 0.956. The diagnostic performance of four chest radiologists and four general radiologists was increased from 0.986 to 0.992 (p = 0.071) and 0.958 and 0.971 (p < 0.001), respectively, when they used the ANN output based on their own feature ratings.

CONCLUSION

The ANN can provide a useful output as a second opinion to improve general radiologists' diagnostic performance in the differential diagnosis of certain diffuse lung diseases using HRCT.

Investigation of new psychophysical measures for evaluation of similar images on thoracic computed tomography for distinction between benign and malignant nodules

We have been developing a computerized scheme to assist radiologists in improving the diagnostic accuracy for lung cancers on low-dose computed tomography (LDCT) scans by use of similar images for malignant nodules and benign nodules. A database of 415 LDCT scans including 73 cases with 76 confirmed cancers and 342 cases with 413 confirmed benign nodules was first collected in an LDCT screening program for early detection of lung cancers in Nagano, Japan. An observer study by use of receiver operating characteristics analysis was first conducted with five radiologists to demonstrate that presenting similar images for malignant nodules and benign nodules can significantly improve radiologists' performance in the diagnosis of unknown nodules. Another observer study was then conducted for obtaining reliable data on subjective similarity ratings by 10 radiologists. Based on the subjective similarity ratings, three important features were selected from a number of nodule features, and four different techniques for the determination of similarity measures, namely, a feature-based technique, a pixel-value-difference based technique, a cross-correlation-based technique, and a neural-network-based technique, were investigated and evaluated in terms of the correlation coefficient with the subjective similarity ratings. The experimental results in this study indicated that the neural-network-based technique can provide a reliable psychophysical similarity measure which is comparable to the subjective similarity ratings for a single radiologist when evaluated by use of correlation with the average similarity ratings for the other nine radiologists.

Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography

An automated computerized scheme has been developed for the detection and characterization of diffuse lung diseases on high-resolution computed tomography (HRCT) images. Our database consisted of 315 HRCT images selected from 105 patients, which included normal and abnormal slices related to six different patterns, i.e., ground-glass opacities, reticular and linear opacities, nodular opacities, honeycombing, emphysematous change, and consolidation. The areas that included specific diffuse patterns in 315 HRCT images were marked by three radiologists independently on the CRT monitor in the same manner as they commonly describe in their radiologic reports. The areas with a specific pattern, which three radiologists marked independently and consistently as the same patterns, were used as "gold standard" for specific abnormal opacities in this study. The lungs were first segmented from the background in each slice by use of a morphological filter and a thresholding technique, and then divided into many contiguous regions of interest (ROIs) with a 32x32 matrix. Six physical measures which were determined in each ROI included the mean and the standard deviation of the CT value, air density components, nodular components, line components, and multilocular components. Artificial neural networks (ANNs) were employed for distinguishing between seven different patterns which included normals and six patterns associated with diffuse lung disease. The sensitivity of this computerized method for a detection of the six abnormal patterns in each ROI was 99.2% (122/123) for ground-glass opacities, 100% (15/15) for reticular and linear opacities, 88.0% (132/150) for nodular opacities, 100% (98/98) for honeycombing, 95.8% (369/385) for emphysematous change, and 100% (43/43) for consolidation. The specificity in detecting a normal ROI was 88.1% (940/1067). This computerized method may be useful in assisting radiologists in their assessment of diffuse lung disease in HRCT images.

Artificial neural networks (ANNs) for differential diagnosis of interstitial lung disease : results of a simulation test with actual clinical cases1

RATIONALE AND OBJECTIVES

To evaluate the performance of an artificial neural network (ANN) scheme with use of consecutive clinical cases and its effect on radiologists with an observer test.

MATERIALS AND METHODS

Artificial neural networks were designed to distinguish among 11 interstitial lung diseases on the basis of 26 inputs (16 radiologic findings, 10 clinical parameters). Chest radiographs of 96 consecutive cases with interstitial lung disease were used. Five radiologists independently rated their radiologic findings on the 96 chest radiographs. Based on their ratings of radiologic findings and clinical parameters obtained from the hospital information system, the output values indicating the likelihood of each of the 11 interstitial lung diseases were determined. Subsequently, 30 cases were selected from these 96 cases for an observer test. Five radiologists marked their confidence levels for diagnosis of 11 possible diseases in each case without and with ANN output. The performance of ANNs and radiologists was evaluated by receiver operating characteristic analysis based on their outputs and on confidence levels, respectively. RESULTS; The average Az value (area under the receiver operating characteristic curve) indicating ANN performance for the 96 consecutive cases was 0.85 +/- 0.03. The average Az values indicating radiologists' performance without and with ANN outputs were 0.81 +/- 0.11 and 0.87 +/- 0.06, respectively. The diagnostic accuracy was improved significantly when radiologists read chest radiographs with ANN outputs (P < .05).

CONCLUSION

Artificial neural networks for differential diagnosis of interstitial lung disease may be useful in clinical situations, and radiologists may be able to utilize the ANN output to their advantage in the differential diagnosis of interstitial lung disease on chest radiographs.

Computer-aided diagnosis in chest radiography: results of large-scale observer tests at the 1996-2001 RSNA scientific assemblies

Since 1996, computer-aided diagnosis (CAD) schemes have been presented as interactive demonstrations on computer workstations at each scientific assembly of the Radiological Society of North America. The schemes involved (a) detection of pulmonary nodules, (b) temporal subtraction, (c) detection of interstitial lung disease, (d) differential diagnosis of interstitial lung disease, and (e) distinction between benign and malignant pulmonary nodules on chest radiographs. Large-scale observer tests were carried out to examine how radiologists can benefit from CAD systems. Observer performance was evaluated by analysis of receiver operating characteristic (ROC) curves. The statistical significance of the difference between the areas under the ROC curves without and with CAD was analyzed with the Student t test. In all of the tests, the diagnostic accuracy of the radiologists in total improved significantly when CAD was used. This result provides additional evidence that CAD has the potential to improve the performance of radiologists in their decision-making process in interpreting chest radiographs.

Outcome analysis of patients with acute pancreatitis by using an artificial neural network

RATIONALE AND OBJECTIVES

The authors performed this study to evaluate the ability of an artificial neural network (ANN) that uses radiologic and laboratory data to predict the outcome in patients with acute pancreatitis.

MATERIALS AND METHODS

An ANN was constructed with data from 92 patients with acute pancreatitis who underwent computed tomography (CT). Input nodes included clinical, laboratory, and CT data. The ANN was trained and tested by using a round-robin technique, and the performance of the ANN was compared with that of linear discriminant analysis and Ranson and Balthazar grading systems by using receiver operating characteristic analysis. The length of hospital stay was used as an outcome measure.

RESULTS

Hospital stay ranged from 0 to 45 days, with a mean of 8.4 days. The hospital stay was shorter than the mean for 62 patients and longer than the mean for 30. The 23 input features were reduced by using stepwise linear discriminant analysis, and an ANN was developed with the six most statistically significant parameters (blood pressure, extent of inflammation, fluid aspiration, serum creatinine level, serum calcium level, and the presence of concurrent severe illness). With these features, the ANN successfully predicted whether the patient would exceed the mean length of stay (Az = 0.83 +/- 0.05). Although the Az performance of the ANN was statistically significantly better than that of the Ranson (Az = 0.68 +/- 0.06, P < .02) and Balthazar (Az = 0.62 +/- 0.06, P < .003) grades, it was not significantly better than that of linear discriminant analysis (Az = 0.82 +/- 0.05, P = .53).

CONCLUSION

An ANN may be useful for predicting outcome in patients with acute pancreatitis.

Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT: evaluation with receiver operating characteristic analysis

OBJECTIVE

The purpose of our study was to use an artificial neural network to differentiate benign from malignant pulmonary nodules on high-resolution CT findings and to evaluate the effect of artificial neural network output on the performance of radiologists using receiver operating characteristic analysis.

MATERIALS AND METHODS

We selected 155 cases with pulmonary nodules less than 3 cm (99 malignant nodules and 56 benign nodules). An artificial neural network was used to distinguish benign from malignant nodules on the basis of seven clinical parameters and 16 radiologic findings that were extracted by attending radiologists using subjective rating scales. In the observer test, 12 radiologists (four attending radiologists, four radiology fellows, and four radiology residents) were presented with high-resolution CT images, first without and then with the artificial neural network output. Observer performance was evaluated by means of receiver operating characteristic analysis using a continuous rating scale.

RESULTS

The artificial neural network showed a high performance in differentiating benign from malignant pulmonary nodules (A(z) = 0.951). The average A(z) value for all radiologists increased by a statistically significant level, from 0.831 to 0.959, with the use of the artificial neural network output.

CONCLUSION

Our computerized scheme using the artificial neural network can improve the diagnostic accuracy of radiologists who are differentiating benign from malignant pulmonary nodules on high-resolution CT.

Use of an artificial neural network to determine the diagnostic value of specific clinical and radiologic parameters in the diagnosis of interstitial lung disease on chest radiographs

RATIONALE AND OBJECTIVES

The authors investigated the diagnostic value of each of multiple clinical parameters and radiologic findings in differentiating between various interstitial lung diseases by using an artificial neural network (ANN).

MATERIALS AND METHODS

The ANN was designed to differentiate between 11 interstitial lung diseases. The authors employed 10 clinical parameters and 16 radiologic findings that were divided into three groups (location, general appearance, specific findings). The performance of the ANN was evaluated with receiver operating characteristic analysis with amodified round-robin (leave-one-out) method and 370 cases (150 actual cases, 110 published cases, and 110 hypothetical cases). The Az values of ANNs were evaluated with various combinations of 10 clinical parameters and 16 radiologic findings.

RESULTS

The Az value obtained with the complete set of clinical parameters and radiologic findings was 0.947. The Az value obtained with the 10 clinical parameters alone was 0.900, which was greater than 0.843 obtained with the 16 radiologic findings alone. There were statistically significant differences among Az values for some diseases when certain clinical parameters were removed from the input. Omission of specific findings among the three groups of radiologic findings decreased the Az value significantly.

CONCLUSION

These results appear to confirm that clinical parameters can be equally as or more important than radiologic findings in the diagnosis of interstitial lung diseases. Among radiologic findings, certain specific findings can be more important than the location or general appearance of abnormal findings.

Computer-aided diagnosis in chest radiography: a survey

The traditional chest radiograph is still ubiquitous in clinical practice, and will likely remain so for quite some time. Yet, its interpretation is notoriously difficult. This explains the continued interest in computer-aided diagnosis for chest radiography. The purpose of this survey is to categorize and briefly review the literature on computer analysis of chest images, which comprises over 150 papers published in the last 30 years. Remaining challenges are indicated and some directions for future research are given.

Diagnóstico auxiliado por computador na radiologia

Vários desenvolvimentos tecnológicos estão convergindo de forma a aumentar a influência da área de imagens nas pesquisas biomédicas e na medicina clínica. Muitos pesquisadores têm trabalhado no desenvolvimento de sistemas computadorizados para detecção automatizada e quantificação de anormalidades em imagens radiológicas. Estes sistemas são dedicados ao diagnóstico auxiliado por computador. Este artigo discute os conceitos básicos relacionados ao diagnóstico auxiliado por computador e apresenta uma revisão bibliográfica sobre o assunto.

Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks

The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification. Expression of several of these genes has been reported in SRBCTs, but most have not been associated with these cancers. To test the ability of the trained ANN models to recognize SRBCTs, we analyzed additional blinded samples that were not previously used for the training procedure, and correctly classified them in all cases. This study demonstrates the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy.

Computer-aided diagnosis in radiology: potential and pitfalls

Computer-aided diagnosis (CAD) may be defined as a diagnosis made by a physician who takes into account the computer output as a second opinion. The purpose of CAD is to improve the diagnostic accuracy and the consistency of the radiologists' image interpretation. This article is to provide a brief overview of some of CAD schemes for detection and differential diagnosis of pulmonary nodules and interstitial opacities in chest radiographs as well as clustered micro-calcifications and masses in mammograms. ROC analysis clearly indicated that the radiologists' performances were significantly improved when the computer output was available. An intelligent CAD workstation was developed for detection of breast lesions in mammograms. Results obtained from the first 10,000 cases indicated the potential of CAD in detecting approximately one-half of 'missed' breast cancer.