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Bodaghi A, Fattahi N, Ramazani A. Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases. Heliyon 2023; 9:e13323. [PMID: 36744065 PMCID: PMC9884646 DOI: 10.1016/j.heliyon.2023.e13323] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 01/21/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.
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Affiliation(s)
- Ali Bodaghi
- Department of Chemistry, Tuyserkan Branch, Islamic Azad University, Tuyserkan, Iran
| | - Nadia Fattahi
- Department of Chemistry, University of Zanjan, Zanjan, 45371-38791, Iran,Trita Nanomedicine Research and Technology Development Center (TNRTC), Zanjan Health Technology Park, 45156-13191, Zanjan, Iran
| | - Ali Ramazani
- Department of Chemistry, University of Zanjan, Zanjan, 45371-38791, Iran,Department of Biotechnology, Research Institute of Modern Biological Techniques (RIMBT), University of Zanjan, Zanjan, 45371-38791, Iran,Corresponding author. Department of Chemistry, University of Zanjan, Zanjan, 45371-38791, Iran.;
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Lugtu EJ, Ramos DB, Agpalza AJ, Cabral EA, Carandang RP, Dee JE, Martinez A, Jose JE, Santillan A, Bangaoil R, Albano PM, Tomas RC. Artificial neural network in the discrimination of lung cancer based on infrared spectroscopy. PLoS One 2022; 17:e0268329. [PMID: 35551276 PMCID: PMC9098097 DOI: 10.1371/journal.pone.0268329] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 04/27/2022] [Indexed: 12/19/2022] Open
Abstract
Given the increasing prevalence of lung cancer worldwide, an auxiliary diagnostic method is needed alongside the microscopic examination of biopsy samples, which is dependent on the skills and experience of pathologists. Thus, this study aimed to advance lung cancer diagnosis by developing five (5) artificial neural network (NN) models that can discriminate malignant from benign samples based on infrared spectral data of lung tumors (n = 122; 56 malignant, 66 benign). NNs were benchmarked with classical machine learning (CML) models. Stratified 10-fold cross-validation was performed to evaluate the NN models, and the performance metrics-area under the curve (AUC), accuracy (ACC) positive predictive value (PPV), negative predictive value (NPV), specificity rate (SR), and recall rate (RR)-were averaged for comparison. All NNs were able to outperform the CML models, however, support vector machine is relatively comparable to NNs. Among the NNs, CNN performed best with an AUC of 92.28% ± 7.36%, ACC of 98.45% ± 1.72%, PPV of 96.62% ± 2.30%, NPV of 90.50% ± 11.92%, SR of 96.01% ± 3.09%, and RR of 89.21% ± 12.93%. In conclusion, NNs can be potentially used as a computational tool in lung cancer diagnosis based on infrared spectroscopy of lung tissues.
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Affiliation(s)
- Eiron John Lugtu
- Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila, Philippines
| | - Denise Bernadette Ramos
- Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila, Philippines
| | - Alliah Jen Agpalza
- Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila, Philippines
| | - Erika Antoinette Cabral
- Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila, Philippines
| | - Rian Paolo Carandang
- Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila, Philippines
| | - Jennica Elia Dee
- Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila, Philippines
| | - Angelica Martinez
- Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila, Philippines
| | - Julius Eleazar Jose
- Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila, Philippines
| | - Abegail Santillan
- Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines
- The Graduate School, University of Santo Tomas, Manila, Philippines
| | - Ruth Bangaoil
- Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines
- The Graduate School, University of Santo Tomas, Manila, Philippines
- University of Santo Tomas Hospital, Manila, Philippines
| | - Pia Marie Albano
- Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines
- The Graduate School, University of Santo Tomas, Manila, Philippines
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
| | - Rock Christian Tomas
- Department of Electrical Engineering, University of the Philippines Los Baños, Laguna, Philippines
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Yan Y, Yao XJ, Wang SH, Zhang YD. A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network. BIOLOGY 2021; 10:biology10111084. [PMID: 34827077 PMCID: PMC8615026 DOI: 10.3390/biology10111084] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/19/2021] [Accepted: 10/20/2021] [Indexed: 01/10/2023]
Abstract
Simple Summary One of the hottest areas in deep learning is computerized tumor diagnosis and treatment. The identification of tumor markers, the outline of tumor growth activity, and the staging of various tumor kinds are frequently included. There are several deep learning models based on convolutional neural networks that have high performance and accurate identification, with the potential to improve medical tasks. Breakthroughs and updates in computer algorithms and hardware devices, and intelligent algorithms applied in medical images have a diagnostic accuracy that doctors cannot match in some diseases. This paper reviews the progress of tumor detection from traditional computer-aided methods to convolutional neural networks and demonstrates the potential of the practical application of convolutional neural networks from practical cases to transform the detection model from experiment to clinical application. Abstract Tumors are new tissues that are harmful to human health. The malignant tumor is one of the main diseases that seriously affect human health and threaten human life. For cancer treatment, early detection of pathological features is essential to reduce cancer mortality effectively. Traditional diagnostic methods include routine laboratory tests of the patient’s secretions, and serum, immune and genetic tests. At present, the commonly used clinical imaging examinations include X-ray, CT, MRI, SPECT scan, etc. With the emergence of new problems of radiation noise reduction, medical image noise reduction technology is more and more investigated by researchers. At the same time, doctors often need to rely on clinical experience and academic background knowledge in the follow-up diagnosis of lesions. However, it is challenging to promote clinical diagnosis technology. Therefore, due to the medical needs, research on medical imaging technology and computer-aided diagnosis appears. The advantages of a convolutional neural network in tumor diagnosis are increasingly obvious. The research on computer-aided diagnosis based on medical images of tumors has become a sharper focus in the industry. Neural networks have been commonly used to research intelligent methods to assist medical image diagnosis and have made significant progress. This paper introduces the traditional methods of computer-aided diagnosis of tumors. It introduces the segmentation and classification of tumor images as well as the diagnosis methods based on CNN to help doctors determine tumors. It provides a reference for developing a CNN computer-aided system based on tumor detection research in the future.
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Wang JK, Chang YF, Tsai KH, Wang WC, Tsai CY, Cheng CH, Tsao Y. Automatic recognition of murmurs of ventricular septal defect using convolutional recurrent neural networks with temporal attentive pooling. Sci Rep 2020; 10:21797. [PMID: 33311565 PMCID: PMC7732853 DOI: 10.1038/s41598-020-77994-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 11/18/2020] [Indexed: 12/22/2022] Open
Abstract
Recognizing specific heart sound patterns is important for the diagnosis of structural heart diseases. However, the correct recognition of heart murmur depends largely on clinical experience. Accurately identifying abnormal heart sound patterns is challenging for young and inexperienced clinicians. This study is aimed at the development of a novel algorithm that can automatically recognize systolic murmurs in patients with ventricular septal defects (VSDs). Heart sounds from 51 subjects with VSDs and 25 subjects without a significant heart malformation were obtained in this study. Subsequently, the soundtracks were divided into different training and testing sets to establish the recognition system and evaluate the performance. The automatic murmur recognition system was based on a novel temporal attentive pooling-convolutional recurrent neural network (TAP-CRNN) model. On analyzing the performance using the test data that comprised 178 VSD heart sounds and 60 normal heart sounds, a sensitivity rate of 96.0% was obtained along with a specificity of 96.7%. When analyzing the heart sounds recorded in the second aortic and tricuspid areas, both the sensitivity and specificity were 100%. We demonstrated that the proposed TAP-CRNN system can accurately recognize the systolic murmurs of VSD patients, showing promising potential for the development of software for classifying the heart murmurs of several other structural heart diseases.
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Affiliation(s)
- Jou-Kou Wang
- National Taiwan University Children's Hospital, Taipei, Taiwan
| | | | | | | | | | | | - Yu Tsao
- Research Center for Information Technology Innovation at Academia Sinica, Taipei, Taiwan.
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5
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A morphological image processing method to improve the visibility of pulmonary nodules on chest radiographic images. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101744] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Computer-aided detection in musculoskeletal projection radiography: A systematic review. Radiography (Lond) 2018; 24:165-174. [DOI: 10.1016/j.radi.2017.11.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 10/31/2017] [Accepted: 11/16/2017] [Indexed: 11/17/2022]
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HAGIWARA YUKI, SUDARSHAN VIDYAK, LEONG SOOKSAM, VIJAYNANTHAN ANUSHYA, NG KWANHOONG. APPLICATION OF ENTROPIES FOR AUTOMATED DIAGNOSIS OF ABNORMALITIES IN ULTRASOUND IMAGES: A REVIEW. J MECH MED BIOL 2017. [DOI: 10.1142/s0219519417400127] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Automation of diagnosis process in medical imaging using various computer-aided techniques is a leading topic of research. Among many computer-aided methods, nonlinear entropies are widely applied in the development of automated algorithms to diagnose abnormalities present in medical images. The use of entropy features in development of Computer-Aided Diagnosis (CAD) may enhance the accuracy of the system. Entropy features depict the nonlinearity of images and thereby the presence of complexity in the images. Various types of entropies have been employed in medical image analysis for automated diagnosis of abnormalities present in the images. This paper focuses on the diverse types of entropies employed in the development of CAD systems for the diagnosis of abnormalities in the medical images. In addition to the diagnosis, these entropies can be used to differentiate the images based on the severity of the abnormalities and for other biomedical applications.
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Affiliation(s)
- YUKI HAGIWARA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - VIDYA K SUDARSHAN
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Science, Singapore
- School of Electrical and Computer Engineering, University of Newcastle, Singapore
| | - SOOK SAM LEONG
- Department of Biomedical Imaging, University of Malaya, Malaysia
- University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, Malaysia
| | - ANUSHYA VIJAYNANTHAN
- Department of Biomedical Imaging, University of Malaya, Malaysia
- University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, Malaysia
| | - KWAN HOONG NG
- Department of Biomedical Imaging, University of Malaya, Malaysia
- University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, Malaysia
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Sudarshan VK, Mookiah MRK, Acharya UR, Chandran V, Molinari F, Fujita H, Ng KH. Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review. Comput Biol Med 2015; 69:97-111. [PMID: 26761591 DOI: 10.1016/j.compbiomed.2015.12.006] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2015] [Revised: 11/12/2015] [Accepted: 12/11/2015] [Indexed: 02/01/2023]
Abstract
Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images.
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Affiliation(s)
- Vidya K Sudarshan
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | | | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Malaysia; Department of Biomedical Engineering, School of Science and Technology, SIM University, 599491, Singapore
| | - Vinod Chandran
- School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane QLD 4000, Australia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
| | - Hamido Fujita
- Faculty of Software and Information Science, Iwate Prefectural University (IPU), Iwate 020-0693, Japan
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603, Malaysia
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Angayarkanni SP, Kamal NB, Thangaiya RJ. Dynamic graph cut based segmentation of mammogram. SPRINGERPLUS 2015; 4:591. [PMID: 26543726 PMCID: PMC4628050 DOI: 10.1186/s40064-015-1180-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Accepted: 07/23/2015] [Indexed: 12/02/2022]
Abstract
This work presents the dynamic graph cut based Otsu’s method to segment the masses in mammogram images. Major concern that threatens human life is cancer. Breast cancer is the most common type of disease among women in India and abroad. Breast cancer increases the mortality rate in India especially in women since it is considered to be the second largest form of disease which leads to death. Mammography is the best method for diagnosing early stage of cancer. The computer aided diagnosis lacks accuracy and it is time consuming. The main approach which makes the detection of cancerous masses accurate is segmentation process. This paper is a presentation of the dynamic graph cut based approach for effective segmentation of region of interest (ROI). The sensitivity, the specificity, the positive prediction value and the negative prediction value of the proposed algorithm are determined and compared with the existing algorithms. Both qualitative and quantitative methods are used to detect the accuracy of the proposed system. The sensitivity, the specificity, the positive prediction value and the negative prediction value of the proposed algorithm accounts to 98.88, 98.89, 93 and 97.5% which rates very high when compared to the existing algorithms.
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Affiliation(s)
| | - Nadira Banu Kamal
- Department of M.C.A., TBAK College, Kilakarai, Ramnad, Tamil Nadu India
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Viana-Ferreira C, Ribeiro L, Matos S, Costa C. Pattern recognition for cache management in distributed medical imaging environments. Int J Comput Assist Radiol Surg 2015; 11:327-36. [PMID: 26239372 DOI: 10.1007/s11548-015-1272-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Accepted: 07/21/2015] [Indexed: 10/23/2022]
Abstract
PURPOSE Traditionally, medical imaging repositories have been supported by indoor infrastructures with huge operational costs. This paradigm is changing thanks to cloud outsourcing which not only brings technological advantages but also facilitates inter-institutional workflows. However, communication latency is one main problem in this kind of approaches, since we are dealing with tremendous volumes of data. To minimize the impact of this issue, cache and prefetching are commonly used. The effectiveness of these mechanisms is highly dependent on their capability of accurately selecting the objects that will be needed soon. METHODS This paper describes a pattern recognition system based on artificial neural networks with incremental learning to evaluate, from a set of usage pattern, which one fits the user behavior at a given time. The accuracy of the pattern recognition model in distinct training conditions was also evaluated. RESULTS The solution was tested with a real-world dataset and a synthesized dataset, showing that incremental learning is advantageous. Even with very immature initial models, trained with just 1 week of data samples, the overall accuracy was very similar to the value obtained when using 75% of the long-term data for training the models. Preliminary results demonstrate an effective reduction in communication latency when using the proposed solution to feed a prefetching mechanism. CONCLUSIONS The proposed approach is very interesting for cache replacement and prefetching policies due to the good results obtained since the first deployment moments.
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Affiliation(s)
- Carlos Viana-Ferreira
- Department of Electronics, Telecommunications and Informatics and Institute of Electronics and Telematics Engineering of Aveiro, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal.
| | - Luís Ribeiro
- Department of Electronics, Telecommunications and Informatics and Institute of Electronics and Telematics Engineering of Aveiro, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal.
| | - Sérgio Matos
- Department of Electronics, Telecommunications and Informatics and Institute of Electronics and Telematics Engineering of Aveiro, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal.
| | - Carlos Costa
- Department of Electronics, Telecommunications and Informatics and Institute of Electronics and Telematics Engineering of Aveiro, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal.
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Yazid H, Kalti K, Benamara NE. A new similarity measure based on Bayesian Network signature correspondence for brain tumors cases retrieval. INT J COMPUT INT SYS 2014. [DOI: 10.1080/18756891.2014.963980] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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Bogoni L, Ko JP, Alpert J, Anand V, Fantauzzi J, Florin CH, Koo CW, Mason D, Rom W, Shiau M, Salganicoff M, Naidich DP. Impact of a computer-aided detection (CAD) system integrated into a picture archiving and communication system (PACS) on reader sensitivity and efficiency for the detection of lung nodules in thoracic CT exams. J Digit Imaging 2013; 25:771-81. [PMID: 22710985 DOI: 10.1007/s10278-012-9496-0] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The objective of this study is to assess the impact on nodule detection and efficiency using a computer-aided detection (CAD) device seamlessly integrated into a commercially available picture archiving and communication system (PACS). Forty-eight consecutive low-dose thoracic computed tomography studies were retrospectively included from an ongoing multi-institutional screening study. CAD results were sent to PACS as a separate image series for each study. Five fellowship-trained thoracic radiologists interpreted each case first on contiguous 5 mm sections, then evaluated the CAD output series (with CAD marks on corresponding axial sections). The standard of reference was based on three-reader agreement with expert adjudication. The time to interpret CAD marking was automatically recorded. A total of 134 true-positive nodules, measuring 3 mm and larger were included in our study; with 85 ≥ 4 and 50 ≥ 5 mm in size. Readers detection improved significantly in each size category when using CAD, respectively, from 44 to 57 % for ≥3 mm, 48 to 61 % for ≥4 mm, and 44 to 60 % for ≥5 mm. CAD stand-alone sensitivity was 65, 68, and 66 % for nodules ≥3, ≥4, and ≥5 mm, respectively, with CAD significantly increasing the false positives for two readers only. The average time to interpret and annotate a CAD mark was 15.1 s, after localizing it in the original image series. The integration of CAD into PACS increases reader sensitivity with minimal impact on interpretation time and supports such implementation into daily clinical practice.
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Medical decision-making system of ultrasound carotid artery intima-media thickness using neural networks. J Digit Imaging 2012; 24:1112-25. [PMID: 21181487 DOI: 10.1007/s10278-010-9356-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Abstract
The objective of this work is to develop and implement a medical decision-making system for an automated diagnosis and classification of ultrasound carotid artery images. The proposed method categorizes the subjects into normal, cerebrovascular, and cardiovascular diseases. Two contours are extracted for each and every preprocessed ultrasound carotid artery image. Two types of contour extraction techniques and multilayer back propagation network (MBPN) system have been developed for classifying carotid artery categories. The results obtained show that MBPN system provides higher classification efficiency, with minimum training and testing time. The outputs of decision support system are validated with medical expert to measure the actual efficiency. MBPN system with contour extraction algorithms and preprocessing scheme helps in developing medical decision-making system for ultrasound carotid artery images. It can be used as secondary observer in clinical decision making.
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Bilello M, Lao Z, Krejza J, Hillis AE, Herskovits EH. Atlas-Based Classification of Hyperintense Regions from MR Diffusion-Weighted Images of the Brain: Preliminary Results. Neuroradiol J 2012; 25:112-20. [PMID: 24028884 DOI: 10.1177/197140091202500115] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2011] [Accepted: 01/03/2012] [Indexed: 11/16/2022] Open
Abstract
The study of subjects with acquired brain damage in a specific location is important in exploring human brain function. Description of lesion locations within and across subjects is a crucial methodological component that usually involves the distinction of normal from damaged tissue (lesion segmentation) in relation to lesion locations in terms of a standard anatomical reference space (lesion mapping). Our study provides an atlas-based, computer-aided methodology for classification of hyperintense regions on diffusion-weighted images of the brain, representing either ischemic lesions or susceptibility artifacts. We applied a leave-one-out method of cross-validation that computed probabilistic atlases of true lesions and artifacts, based on training data. Our approach accurately classifies lesions and artifacts, but leaves a significant number of regions unclassified, due to the relatively small number of training samples. An initial segmentation step based on a larger sample of data sets is required to automate discrimination of lesions and artifacts.
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Affiliation(s)
- M Bilello
- Department of Radiology, University of Pennsylvania; Philadelphia, PA, USA -
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Faust O, Acharya UR, Tamura T. Formal Design Methods for Reliable Computer-Aided Diagnosis: A Review. IEEE Rev Biomed Eng 2012; 5:15-28. [DOI: 10.1109/rbme.2012.2184750] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Shiraishi J, Li Q, Appelbaum D, Doi K. Computer-Aided Diagnosis and Artificial Intelligence in Clinical Imaging. Semin Nucl Med 2011; 41:449-62. [DOI: 10.1053/j.semnuclmed.2011.06.004] [Citation(s) in RCA: 120] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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Ganeshan B, Strukowska O, Skogen K, Young R, Chatwin C, Miles K. Heterogeneity of focal breast lesions and surrounding tissue assessed by mammographic texture analysis: preliminary evidence of an association with tumor invasion and estrogen receptor status. Front Oncol 2011; 1:33. [PMID: 22649761 PMCID: PMC3355915 DOI: 10.3389/fonc.2011.00033] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2011] [Accepted: 09/21/2011] [Indexed: 11/13/2022] Open
Abstract
AIM This pilot study investigates whether heterogeneity in focal breast lesions and surrounding tissue assessed on mammography is potentially related to cancer invasion and hormone receptor status. MATERIALS AND METHODS Texture analysis (TA) assessed the heterogeneity of focal lesions and their surrounding tissues in digitized mammograms from 11 patients randomly selected from an imaging archive [ductal carcinoma in situ (DCIS) only, n = 4; invasive carcinoma (IC) with DCIS, n = 3; IC only, n = 4]. TA utilized band-pass image filtration to highlight image features at different spatial frequencies (filter values: 1.0-2.5) from fine to coarse texture. The distribution of features in the derived images was quantified using uniformity. RESULTS Significant differences in uniformity were observed between patient groups for all filter values. With medium scale filtration (filter value = 1.5) pure DCIS was more uniform (median = 0.281) than either DCIS with IC (median = 0.246, p = 0.0102) or IC (median = 0.249, p = 0.0021). Lesions with high levels of estrogen receptor expression were more uniform, most notably with coarse filtration (filter values 2.0 and 2.5, r(s) = 0.812, p = 0.002). Comparison of uniformity values in focal lesions and surrounding tissue showed significant differences between DCIS with or without IC versus IC (p = 0.0009). CONCLUSION This pilot study shows the potential for computer-based assessments of heterogeneity within focal mammographic lesions and surrounding tissue to identify adverse pathological features in mammographic lesions. The technique warrants further investigation as a possible adjunct to existing computer aided diagnosis systems.
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Affiliation(s)
- Balaji Ganeshan
- Clinical and Laboratory Investigation, Clinical Imaging Sciences Centre, University of Sussex Brighton, UK
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An online evidence-based decision support system for distinguishing benign from malignant vertebral compression fractures by magnetic resonance imaging feature analysis. J Digit Imaging 2011; 24:507-15. [PMID: 20680384 DOI: 10.1007/s10278-010-9316-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
Decision support systems have been used to promote the practice of evidence-based medicine. Computer-assisted diagnosis can serve as one element of evidence-based radiology. One area where such tools may provide benefit is analysis of vertebral compression fractures (VCFs), which can be a challenge in MRI interpretation. VCFs may be benign or malignant in etiology, and several MRI features may help to make this important distinction. We describe a web-based decision support system for discriminating benign from malignant VCFs as a prototype for a more general diagnostic decision support framework for radiologists. The system has three components: a feature checklist with an image gallery derived from proven reference cases, a prediction model, and a reporting mechanism. The website allows users to input the findings for a case to be interpreted using a structured feature checklist. The image gallery complements the checklist, for clarity and training purposes. The input from the checklist is then used to calculate the likelihood of malignancy by a logistic regression prediction model. Standardized report text is generated that summarizes pertinent positive and negative findings. This computer-assisted diagnosis system demonstrates the integration of three areas where diagnostic decision support can aid radiologists: first, in image interpretation, through feature checklists and illustrative image galleries; second, in feature-based prediction modeling; and third, in structured reporting. We present a diagnostic decision support tool that provides radiologists with evidence-based guidance for discriminating benign from malignant VCF. This model may be useful in other difficult-diagnosis situations and requires further clinical testing.
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Eadie LH, Taylor P, Gibson AP. Recommendations for research design and reporting in computer-assisted diagnosis to facilitate meta-analysis. J Biomed Inform 2011; 45:390-7. [PMID: 21840421 DOI: 10.1016/j.jbi.2011.07.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2010] [Revised: 05/13/2011] [Accepted: 07/30/2011] [Indexed: 11/30/2022]
Abstract
Computer-assisted diagnosis (CAD) describes a diverse, heterogeneous range of applications rather than a single entity. The aims and functions of CAD systems vary considerably and comparing studies and systems is challenging due to methodological and design differences. In addition, poor study quality and reporting can reduce the value of some publications. Meta-analyses of CAD are therefore difficult and may not provide reliable conclusions. Aiming to determine the major sources of heterogeneity and thereby what CAD researchers could change to allow this sort of assessment, this study reviews a sample of 147 papers concerning CAD used with imaging for cancer diagnosis. It discusses sources of variability, including the goal of the CAD system, learning methodology, study population, design, outcome measures, inclusion of radiologists, and study quality. Based upon this evidence, recommendations are made to help researchers optimize the quality and comparability of their trial design and reporting.
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Affiliation(s)
- Leila H Eadie
- Department of Medical Physics and Bioengineering, University College London, Malet Place Engineering Building, Gower Street, London WC1E 6BT, UK.
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Rajendran P, Madheswaran M. An improved brain image classification technique with mining and shape prior segmentation procedure. J Med Syst 2010; 36:747-64. [PMID: 20703655 DOI: 10.1007/s10916-010-9542-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2010] [Accepted: 06/06/2010] [Indexed: 10/19/2022]
Abstract
The shape prior segmentation procedure and pruned association rule with ImageApriori algorithm has been used to develop an improved brain image classification system are presented in this paper. The CT scan brain images have been classified into three categories namely normal, benign and malignant, considering the low-level features extracted from the images and high level knowledge from specialists to enhance the accuracy in decision process. The experimental results on pre-diagnosed brain images showed 97% sensitivity, 91% specificity and 98.5% accuracy. The proposed algorithm is expected to assist the physicians for efficient classification with multiple key features per image.
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Affiliation(s)
- P Rajendran
- Department of Computer Science and Engineering, K. S. Rangasamy College of Technology, Tamilnadu, India.
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Dubey RB, Hanmandlu M, Gupta SK, Gupta SK. The brain MR Image segmentation techniques and use of diagnostic packages. Acad Radiol 2010; 17:658-71. [PMID: 20211569 DOI: 10.1016/j.acra.2009.12.017] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2009] [Revised: 12/10/2009] [Accepted: 12/12/2009] [Indexed: 11/27/2022]
Abstract
RATIONALE AND OBJECTIVES This article provides a survey of segmentation methods for medical images. Usually, classification of segmentation methods is done based on the approaches adopted and the domain of application. MATERIALS AND METHODS This survey is conducted on the recent segmentation methods used in biomedical image processing and explores the methods useful for better segmentation. A critical appraisal of the current status of semiautomated and automated methods is made for the segmentation of anatomical medical images emphasizing the advantages and disadvantages. Computer-aided diagnosis (CAD) used by radiologists as a second opinion has become one of the major research areas in medical imaging and diagnostic radiology. A picture archiving communication system (PACS) is an integrated workflow system for managing images and related data that is designed to streamline operations throughout the whole patient care delivery process. RESULTS By using PACS, the medical image interpretation may be changed from conventional hard-copy images to soft-copy studies viewed on the systems workstations. CONCLUSION The automatic segmentations assist the doctors in making quick diagnosis. The CAD need not be comparable to that of physicians, but is surely complementary.
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Computer-aided Diagnosis Using Neural Networks and Support Vector Machines for Breast Ultrasonography. J Med Ultrasound 2009. [DOI: 10.1016/s0929-6441(09)60011-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Liu W, Feng H, Li C, Huang Y, Wu D, Tong T. Accelerated detection of intracranial space-occupying lesions with CUDA based on statistical texture atlas in brain HRCT. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2009; 2009:1131-1134. [PMID: 19963990 DOI: 10.1109/iembs.2009.5333454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
In this paper, we present a method that detects intracranial space-occupying lesions in two-dimensional (2D) brain high-resolution CT images. Use of statistical texture atlas technique localizes anatomy variation in the gray level distribution of brain images, and in turn, identifies the regions with lesions. The statistical texture atlas involves 147 HRCT slices of normal individuals and its construction is extremely time-consuming. To improve the performance of atlas construction, we have implemented the pixel-wise texture extraction procedure on Nvidia 8800GTX GPU with Compute Unified Device Architecture (CUDA) platform. Experimental results indicate that the extracted texture feature is distinctive and robust enough, and is suitable for detecting uniform and mixed density space-occupying lesions. In addition, a significant speedup against straight forward CPU version was achieved with CUDA.
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Affiliation(s)
- Wei Liu
- Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, China. bmewliu@mail. ustc.edu.cn
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Nemoto M, Honmura S, Shimizu A, Furukawa D, Kobatake H, Nawano S. A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows. Int J Comput Assist Radiol Surg 2008; 4:27-36. [PMID: 20033599 DOI: 10.1007/s11548-008-0267-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2007] [Accepted: 09/14/2008] [Indexed: 11/29/2022]
Abstract
OBJECTIVE We present herein a novel algorithm for architectural distortion detection that utilizes the point convergence index with the likelihood of lines (e.g., spiculations) relating to architectural distortion. MATERIALS AND METHODS Validation was performed using 25 computed radiography (CR) mammograms, each of which has an architectural distortion with radiating spiculations. The proposed method comprises five steps. First, the lines were extracted on mammograms, such as spiculations of architectural distortion as well as lines in the mammary gland. Second, the likelihood of spiculation for each extracted line was calculated. In the third step, point convergence index weighted by this likelihood was evaluated at each pixel to enhance distortion only. Fourth, local maxima of the index were extracted as candidates for the distortion, then classified based on nine features in the last step. RESULTS Point convergence index without the proposed likelihood generated 84.48/image false-positives (FPs) on average. Conversely, the proposed index succeeded in decreasing this number to 12.48/image on average when sensitivity was 100%. After the classification step, number of FPs was reduced to 0.80/image with 80.0% sensitivity. CONCLUSION Combination of the likelihood of lines with point convergence index is effective in extracting architectural distortion with radiating spiculations.
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Affiliation(s)
- Mitsutaka Nemoto
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo Bunkyo-ku, Tokyo, Japan.
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Heckemann RA, Hammers A, Rueckert D, Aviv RI, Harvey CJ, Hajnal JV. Automatic volumetry on MR brain images can support diagnostic decision making. BMC Med Imaging 2008; 8:9. [PMID: 18500985 PMCID: PMC2413211 DOI: 10.1186/1471-2342-8-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2008] [Accepted: 05/23/2008] [Indexed: 01/09/2023] Open
Abstract
Background Diagnostic decisions in clinical imaging currently rely almost exclusively on visual image interpretation. This can lead to uncertainty, for example in dementia disease, where some of the changes resemble those of normal ageing. We hypothesized that extracting volumetric data from patients' MR brain images, relating them to reference data and presenting the results as a colour overlay on the grey scale data would aid diagnostic readers in classifying dementia disease versus normal ageing. Methods A proof-of-concept forced-choice reader study was designed using MR brain images from 36 subjects. Images were segmented into 43 regions using an automatic atlas registration-based label propagation procedure. Seven subjects had clinically probable AD, the remaining 29 of a similar age range were used as controls. Seven of the control subject data sets were selected at random to be presented along with the seven AD datasets to two readers, who were blinded to all clinical and demographic information except age and gender. Readers were asked to review the grey scale MR images and to record their choice of diagnosis (AD or non-AD) along with their confidence in this decision. Afterwards, readers were given the option to switch on a false-colour overlay representing the relative size of the segmented structures. Colorization was based on the size rank of the test subject when compared with a reference group consisting of the 22 control subjects who were not used as review subjects. The readers were then asked to record whether and how the additional information had an impact on their diagnostic confidence. Results The size rank colour overlays were useful in 18 of 28 diagnoses, as determined by their impact on readers' diagnostic confidence. A not useful result was found in 6 of 28 cases. The impact of the additional information on diagnostic confidence was significant (p < 0.02). Conclusion Volumetric anatomical information extracted from brain images using automatic segmentation and presented as colour overlays can support diagnostic decision making.
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Affiliation(s)
- Rolf A Heckemann
- Division of Neurosciences and Mental Health, Imperial College London, Hammersmith Campus, Du Cane Road, London, UK.
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Chan T, Huang HK. Effect of a computer-aided diagnosis system on clinicians' performance in detection of small acute intracranial hemorrhage on computed tomography. Acad Radiol 2008; 15:290-9. [PMID: 18280927 DOI: 10.1016/j.acra.2007.09.022] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2007] [Revised: 09/21/2007] [Accepted: 09/21/2007] [Indexed: 11/19/2022]
Abstract
RATIONALE AND OBJECTIVES To analyze the effect of a computer-aided diagnosis (CAD) system on clinicians' performance in detection of small acute intracranial hemorrhage (AIH) on computed tomography (CT). MATERIALS AND METHODS The authors have developed a CAD scheme that used both image processing techniques and anatomic knowledge based classification system to improve diagnosis of small AIH on CT. A multiple-reader, multiple-case receiver operating characteristic (ROC) study was performed. Twenty clinicians, including seven emergency physicians, seven radiology residents, and six radiology specialists were recruited as readers of 60 sets of brain CT, including 30 cases that show AIH smaller than 1 cm, and 30 controls. Each reader read the same 60 cases twice, first without, then with the prompts produced by the CAD system. The clinicians ranked their confidence in diagnosing a case of showing AIH, which produced the ROC curves. RESULTS Significantly improved performance is observed in emergency physicians, average area under the ROC curve (Az) increased from 0.8422 to 0.9294 (P = .0107) when they make the diagnosis without and with the support of CAD. Az for radiology residents increased from 0.9371 to 0.9762 (P = .0088). Az for radiology specialists increased from 0.9742 to 0.9868, but was statistically insignificant (P = .1755). CONCLUSIONS CAD can improve the clinicians' performance in detecting AIH on CT. In particular, emergency physicians can benefit most from the CAD and improve their performance to a level approaching that of the average radiology residents.
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Affiliation(s)
- Tao Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
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Bommanna Raja K, Madheswaran M, Thyagarajah K. A Hybrid Fuzzy-Neural System for Computer-Aided Diagnosis of Ultrasound Kidney Images Using Prominent Features. J Med Syst 2007; 32:65-83. [DOI: 10.1007/s10916-007-9109-5] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 2007; 31:198-211. [PMID: 17349778 PMCID: PMC1955762 DOI: 10.1016/j.compmedimag.2007.02.002] [Citation(s) in RCA: 706] [Impact Index Per Article: 41.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
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.
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Affiliation(s)
- Kunio Doi
- Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA.
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Abstract
Imaging informatics is a distinct subspecialty of radiology that endeavors to improve the efficiency, accuracy, and reliability of radiologic services within the medical enterprise. Although picture archiving and communication systems (PACS) are a major focus of imaging informatics, there are many other ways in which technology can improve the efficiency of individual radiologists and of the entire department. Understanding informatics principles is important because these principles affect major purchase decisions, not only for PACS but also for other supporting software and for modalities themselves. This review, which is the first of two parts, will focus on PACS and its parts and on supporting software for PACS.
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Affiliation(s)
- Barton F Branstetter
- Department of Radiology, University of Pittsburgh School of Medicine, 200 Lothrop St, PUH Room D-132, Pittsburgh, PA 15213, USA.
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Chan T. Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Comput Med Imaging Graph 2007; 31:285-98. [PMID: 17376649 DOI: 10.1016/j.compmedimag.2007.02.010] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
INTRODUCTION Detection of acute intracranial hemorrhage (AIH) is a primary task in image interpretation of computer tomography (CT) of brain for patients suffering from acute neurological disturbance or head injury. Although CT readily depicts AIH, interpretation can be difficult especially when the lesion is inconspicuous or the reader is inexperienced. OBJECTIVE To develop a computer aided detection system that improves diagnostic accuracy of small AIH on brain CT. MATERIALS AND METHODS Intracranial contents are first segmented by thresholding and morphological operations, which are then subjected to denoising and adjustment for CT cupping artifacts. The brain is then automatically realigned into normal position. AIH candidates are extracted based on top-hat transformation and left-right asymmetry. AIH candidates are registered against a normalized coordinate system such that the candidates are rendered anatomical information. True AIH is differentiated from mimicking normal variants or artifacts by a knowledge-based classification system incorporating rules that make use of quantified imaging features and anatomical information. A total of 186 clinical cases, including 62 CT studies showing small (<1cm) AIH, and 124 controls, were retrospectively collected. Forty positive cases and 80 controls were used for the training of the CAD. Twenty-two positive cases and 44 controls were used in the validation of the CAD system. Regions of AIH identified by two experienced radiologists were used as gold standard. The size of individual AIH volume was also recorded. RESULTS On a per patient basis, the system achieved sensitivity of 95% (38/40) and specificity of 88.8% (71/80) in the training dataset. The sensitivity and specificity were 100% (22/22) and 84.1% (37/44) respectively for the diagnosis of AIH in the validation cases. Individual cases contained variable number of AIH volumes. There were 77 lesions in the 40 training cases and 46 lesions in the 22 validation cases. On a per lesion basis, the sensitivities were 84.4% (65/77) and 82.6% (38/46) for all lesions 10mm or smaller for the training and validation datasets, respectively. False positive rates were 0.19 (23/120) and 0.29 (19/66) false positive lesion per case for the training and validation datasets, respectively. CONCLUSION This study demonstrated that CAD is valuable for detection of small AIH on brain CT.
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Affiliation(s)
- Tao Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
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31
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Kainberger F, Langs G, Peloschek P, Schlager T, Schüller-Weidekamm C, Valentinitsch A. [Computer assisted radiological diagnostics of arthritic joint alterations]. Z Rheumatol 2006; 65:676-80. [PMID: 17171394 DOI: 10.1007/s00393-006-0126-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Computer assisted diagnosis (CAD) schemes are currently used in the field of musculoskeletal diseases to quantitatively assess vertebral fractures, joint space narrowing, andr erosion. Most systems work semi-automatically, i.e. they are operator dependent in the selection of anatomical landmarks. Fully automatic programs are currently under development. Some CAD products have already been successfully used in clinical trials.
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Affiliation(s)
- F Kainberger
- Universitätsklinik für Radiodiagnostik, Medizinische Universität, AKH, Währinger Gürtel 18-20, 1090, Wien, Osterreich.
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Zysk AM, Boppart SA. Computational methods for analysis of human breast tumor tissue in optical coherence tomography images. JOURNAL OF BIOMEDICAL OPTICS 2006; 11:054015. [PMID: 17092164 DOI: 10.1117/1.2358964] [Citation(s) in RCA: 43] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Optical coherence tomography (OCT) has been demonstrated as a promising means of identifying the boundaries between normal and diseased breast tissue. This capability has yielded promise for the development of OCT techniques for biopsy guidance, surgical margin assessment, and minimally invasive evaluation of disease states. We present methods for the assessment of human breast tissue based on spatial and Fourier-domain analysis. Derived from preliminary OCT data, these methods are aimed at the development of automated diagnostic tools that will aid in the translation of this technology into the clinical environment.
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Affiliation(s)
- Adam M Zysk
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Beckman Institute for Advanced Science and Technology, 405 N. Mathews Ave., Urbana, Illinois 61801, USA
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Abstract
Advances in genomics, proteomics and molecular pathology have generated many candidate biomarkers with potential clinical value. Their use for cancer staging and personalization of therapy at the time of diagnosis could improve patient care. However, translation from bench to bedside outside of the research setting has proved more difficult than might have been expected. Understanding how and when biomarkers can be integrated into clinical care is crucial if we want to translate the promise into reality.
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Affiliation(s)
- Joseph A Ludwig
- Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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Doi K. Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol 2005; 78 Spec No 1:S3-S19. [PMID: 15917443 DOI: 10.1259/bjr/82933343] [Citation(s) in RCA: 154] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
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.
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Affiliation(s)
- K Doi
- Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, The University of Chicago, 5841 South Maryland, MC 2026, Chicago, IL 60637, USA
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Abstract
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.
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Affiliation(s)
- Kunio Doi
- Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, The University of Chicago, Chicago, Illinois 60637, USA.
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Affiliation(s)
- Jane P Ko
- Division of Thoracic Imaging, Department of Radiology, New York University Medical Center, New York, NY 10016, USA.
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Le CAD améliore-t-il les performances en détection ? IMAGERIE DE LA FEMME 2004. [DOI: 10.1016/s1776-9817(04)94787-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Affiliation(s)
- Elizabeth A Krupinski
- Department of Radiology, University of Arizona, 1609 N. Warren Bldg 211, Rm 112, Tucson, AZ 85724, USA
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Summers RM. Road maps for advancement of radiologic computer-aided detection in the 21st century. Radiology 2003; 229:11-3. [PMID: 14519863 DOI: 10.1148/radiol.2291030010] [Citation(s) in RCA: 55] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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