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Lokaj B, Pugliese MT, Kinkel K, Lovis C, Schmid J. Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review. Eur Radiol 2024; 34:2096-2109. [PMID: 37658895 PMCID: PMC10873444 DOI: 10.1007/s00330-023-10181-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/07/2023] [Accepted: 07/10/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVE Although artificial intelligence (AI) has demonstrated promise in enhancing breast cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various barriers. This scoping review aims to identify these barriers and facilitators to highlight key considerations for developing and implementing AI solutions in breast cancer imaging. METHOD A literature search was conducted from 2012 to 2022 in six databases (PubMed, Web of Science, CINHAL, Embase, IEEE, and ArXiv). The articles were included if some barriers and/or facilitators in the conception or implementation of AI in breast clinical imaging were described. We excluded research only focusing on performance, or with data not acquired in a clinical radiology setup and not involving real patients. RESULTS A total of 107 articles were included. We identified six major barriers related to data (B1), black box and trust (B2), algorithms and conception (B3), evaluation and validation (B4), legal, ethical, and economic issues (B5), and education (B6), and five major facilitators covering data (F1), clinical impact (F2), algorithms and conception (F3), evaluation and validation (F4), and education (F5). CONCLUSION This scoping review highlighted the need to carefully design, deploy, and evaluate AI solutions in clinical practice, involving all stakeholders to yield improvement in healthcare. CLINICAL RELEVANCE STATEMENT The identification of barriers and facilitators with suggested solutions can guide and inform future research, and stakeholders to improve the design and implementation of AI for breast cancer detection in clinical practice. KEY POINTS • Six major identified barriers were related to data; black-box and trust; algorithms and conception; evaluation and validation; legal, ethical, and economic issues; and education. • Five major identified facilitators were related to data, clinical impact, algorithms and conception, evaluation and validation, and education. • Coordinated implication of all stakeholders is required to improve breast cancer diagnosis with AI.
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Affiliation(s)
- Belinda Lokaj
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland.
- Faculty of Medicine, University of Geneva, Geneva, Switzerland.
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.
| | - Marie-Thérèse Pugliese
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
| | - Karen Kinkel
- Réseau Hospitalier Neuchâtelois, Neuchâtel, Switzerland
| | - Christian Lovis
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland
| | - Jérôme Schmid
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
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2
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Huang CC, Chiang HF, Hsieh CC, Chou CL, Jhou ZY, Hou TY, Shaw JS. Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke. Tomography 2023; 9:647-656. [PMID: 36961011 PMCID: PMC10037617 DOI: 10.3390/tomography9020052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 03/11/2023] [Accepted: 03/13/2023] [Indexed: 03/18/2023] Open
Abstract
BACKGROUND Collateral status is an important predictor for the outcome of acute ischemic stroke with large vessel occlusion. Multiphase computed-tomography angiography (mCTA) is useful to evaluate the collateral status, but visual evaluation of this examination is time-consuming. This study aims to use an artificial intelligence (AI) technique to develop an automatic AI prediction model for the collateral status of mCTA. METHODS This retrospective study enrolled subjects with acute ischemic stroke receiving endovascular thrombectomy between January 2015 and June 2020 in a tertiary referral hospital. The demographic data and images of mCTA were collected. The collateral status of all mCTA was visually evaluated. Images at the basal ganglion and supraganglion levels of mCTA were selected to produce AI models using the convolutional neural network (CNN) technique to automatically predict the collateral status of mCTA. RESULTS A total of 82 subjects were enrolled. There were 57 cases randomly selected for the training group and 25 cases for the validation group. In the training group, there were 40 cases with a positive collateral result (good or intermediate) and 17 cases with a negative collateral result (poor). In the validation group, there were 21 cases with a positive collateral result and 4 cases with a negative collateral result. During training for the CNN prediction model, the accuracy of the training group could reach 0.999 ± 0.015, whereas the prediction model had a performance of 0.746 ± 0.008 accuracy on the validation group. The area under the ROC curve was 0.7. CONCLUSIONS This study suggests that the application of the AI model derived from mCTA images to automatically evaluate the collateral status is feasible.
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Affiliation(s)
- Chun-Chao Huang
- Department of Radiology, MacKay Memorial Hospital, Taipei 104217, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City 252005, Taiwan
| | - Hsin-Fan Chiang
- Department of Radiology, MacKay Memorial Hospital, Taipei 104217, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City 252005, Taiwan
- Mackay Junior College of Medicine, Nursing, and Management, Taipei 112021, Taiwan
| | - Cheng-Chih Hsieh
- Department of Radiology, MacKay Memorial Hospital, Taipei 104217, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City 252005, Taiwan
- Mackay Junior College of Medicine, Nursing, and Management, Taipei 112021, Taiwan
| | - Chao-Liang Chou
- Department of Medicine, MacKay Medical College, New Taipei City 252005, Taiwan
- Department of Neurology, MacKay Memorial Hospital, Taipei 104217, Taiwan
| | - Zong-Yi Jhou
- Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei 106344, Taiwan
| | - Ting-Yi Hou
- Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei 106344, Taiwan
| | - Jin-Siang Shaw
- Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei 106344, Taiwan
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Mahajan A, Chakrabarty N, Majithia J, Ahuja A, Agarwal U, Suryavanshi S, Biradar M, Sharma P, Raghavan B, Arafath R, Shukla S. Multisystem Imaging Recommendations/Guidelines: In the Pursuit of Precision Oncology. Indian J Med Paediatr Oncol 2023. [DOI: 10.1055/s-0043-1761266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023] Open
Abstract
AbstractWith an increasing rate of cancers in almost all age groups and advanced screening techniques leading to an early diagnosis and longer longevity of patients with cancers, it is of utmost importance that radiologists assigned with cancer imaging should be prepared to deal with specific expected and unexpected circumstances that may arise during the lifetime of these patients. Tailored integration of preventive and curative interventions with current health plans and global escalation of efforts for timely diagnosis of cancers will pave the path for a cancer-free world. The commonly encountered circumstances in the current era, complicating cancer imaging, include coronavirus disease 2019 infection, pregnancy and lactation, immunocompromised states, bone marrow transplant, and screening of cancers in the relevant population. In this article, we discuss the imaging recommendations pertaining to cancer screening and diagnosis in the aforementioned clinical circumstances.
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Affiliation(s)
- Abhishek Mahajan
- Department of Radiology, The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Nivedita Chakrabarty
- Radiodiagnosis, Tata Memorial Hospital, Mumbai, India
- Homi Bhabha National Institute, Mumbai, India
| | - Jinita Majithia
- Department of Radiodiagnosis, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | | | - Ujjwal Agarwal
- Radiodiagnosis, Tata Memorial Hospital, Mumbai, India
- Homi Bhabha National Institute, Mumbai, India
| | - Shubham Suryavanshi
- Radiodiagnosis, Tata Memorial Hospital, Mumbai, India
- Homi Bhabha National Institute, Mumbai, India
| | - Mahesh Biradar
- Radiodiagnosis, Tata Memorial Hospital, Mumbai, India
- Homi Bhabha National Institute, Mumbai, India
| | - Prerit Sharma
- Radiodiagnosis, Sharma Diagnostic Centre, Wardha, India
| | | | | | - Shreya Shukla
- Radiodiagnosis, Tata Memorial Hospital, Mumbai, India
- Homi Bhabha National Institute, Mumbai, India
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4
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Samee NA, Alhussan AA, Ghoneim VF, Atteia G, Alkanhel R, Al-antari MA, Kadah YM. A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms. SENSORS 2022; 22:s22134938. [PMID: 35808433 PMCID: PMC9269713 DOI: 10.3390/s22134938] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 12/16/2022]
Abstract
One of the most promising research areas in the healthcare industry and the scientific community is focusing on the AI-based applications for real medical challenges such as the building of computer-aided diagnosis (CAD) systems for breast cancer. Transfer learning is one of the recent emerging AI-based techniques that allow rapid learning progress and improve medical imaging diagnosis performance. Although deep learning classification for breast cancer has been widely covered, certain obstacles still remain to investigate the independency among the extracted high-level deep features. This work tackles two challenges that still exist when designing effective CAD systems for breast lesion classification from mammograms. The first challenge is to enrich the input information of the deep learning models by generating pseudo-colored images instead of only using the input original grayscale images. To achieve this goal two different image preprocessing techniques are parallel used: contrast-limited adaptive histogram equalization (CLAHE) and Pixel-wise intensity adjustment. The original image is preserved in the first channel, while the other two channels receive the processed images, respectively. The generated three-channel pseudo-colored images are fed directly into the input layer of the backbone CNNs to generate more powerful high-level deep features. The second challenge is to overcome the multicollinearity problem that occurs among the high correlated deep features generated from deep learning models. A new hybrid processing technique based on Logistic Regression (LR) as well as Principal Components Analysis (PCA) is presented and called LR-PCA. Such a process helps to select the significant principal components (PCs) to further use them for the classification purpose. The proposed CAD system has been examined using two different public benchmark datasets which are INbreast and mini-MAIS. The proposed CAD system could achieve the highest performance accuracies of 98.60% and 98.80% using INbreast and mini-MAIS datasets, respectively. Such a CAD system seems to be useful and reliable for breast cancer diagnosis.
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Affiliation(s)
- Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; (N.A.S.); (G.A.); (R.A.)
| | - Amel A. Alhussan
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
- Correspondence:
| | | | - Ghada Atteia
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; (N.A.S.); (G.A.); (R.A.)
| | - Reem Alkanhel
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; (N.A.S.); (G.A.); (R.A.)
| | - Mugahed A. Al-antari
- Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Korea;
| | - Yasser M. Kadah
- Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 22254, Saudi Arabia;
- Biomedical Engineering Department, Cairo University, Giza 12613, Egypt
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Oza P, Sharma P, Patel S, Bruno A. A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms. J Imaging 2021; 7:190. [PMID: 34564116 PMCID: PMC8466003 DOI: 10.3390/jimaging7090190] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 09/09/2021] [Accepted: 09/14/2021] [Indexed: 11/17/2022] Open
Abstract
Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper's main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.
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Affiliation(s)
- Parita Oza
- Computer Science and Engineering Department, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India; (P.S.); (S.P.)
| | - Paawan Sharma
- Computer Science and Engineering Department, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India; (P.S.); (S.P.)
| | - Samir Patel
- Computer Science and Engineering Department, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India; (P.S.); (S.P.)
| | - Alessandro Bruno
- Department of Computing and Informatics, Bournemouth University, Poole, Dorset BH12 5BB, UK
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Tsafas V, Oikonomidis I, Gavgiotaki E, Tzamali E, Tzedakis G, Fotakis C, Athanassakis I, Filippidis G. Application of a deep-learning technique to non-linear images from human tissue biopsies for shedding new light on breast cancer diagnosis. IEEE J Biomed Health Inform 2021; 26:1188-1195. [PMID: 34379601 DOI: 10.1109/jbhi.2021.3104002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The development of label-free non-invasive techniques to be used as diagnostic tools in cancer research is of great importance for improving the quality of life for millions of patients. Previous studies have demonstrated that Third Harmonic Generation (THG) imaging could differentiate malignant from benign unlabeled human breast biopsies and distinguish the different grades of cancer. Towards the application of such technologies to clinic, in the present report, a deep learning technique was applied to THG images recorded from breast cancer tissues of grades 0, I, II and III. By the implementation of a convolutional neural network (CNN) model, the differentiation of malignant from benign breast tissue samples and the discrimination of the different grades of cancer in a fast and accurate way were achieved. The obtained results provide a step ahead towards the use of optical diagnostic tools in conjunction with the CNN image classifier for the reliable and rapid malignancy diagnosis in clinic.
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A review on recent advancements in diagnosis and classification of cancers using artificial intelligence. Biomedicine (Taipei) 2021; 10:5-17. [PMID: 33854922 PMCID: PMC7721470 DOI: 10.37796/2211-8039.1012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 06/16/2020] [Indexed: 12/09/2022] Open
Abstract
Artificial intelligence has illustrated drastic changes in radiology and medical imaging techniques which in turn led to tremendous changes in screening patterns. In particular, advancements in these techniques led to the development of computer aided detection (CAD) strategy. These approaches provided highly accurate diagnostic reports which served as a "second-opinion" to the radiologists. However, with significant advancements in artificial intelligence strategy, the diagnostic and classifying capabilities of CAD system are meeting the levels of radiologists and clinicians. Thus, it shifts the CAD system from second opinion approach to a high utility tool. This article reviews the strategies and algorithms developed using artificial intelligence for the foremost cancer diagnosis and classification which overcomes the challenges in the traditional method. In addition, the possible direction of AI in medical aspects is also discussed in this study.
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8
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Goyal S. An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging. REPORTS IN MEDICAL IMAGING 2021. [DOI: 10.2147/rmi.s295205] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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9
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Desai M, Shah M. An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). CLINICAL EHEALTH 2021. [DOI: 10.1016/j.ceh.2020.11.002] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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10
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Baessler B. [Artificial Intelligence in Radiology - Definition, Potential and Challenges]. PRAXIS 2021; 110:48-53. [PMID: 33406927 DOI: 10.1024/1661-8157/a003597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Artificial Intelligence in Radiology - Definition, Potential and Challenges Abstract. Artificial Intelligence (AI) is omnipresent. It has neatly permeated our daily life, even if we are not always fully aware of its ubiquitous presence. The healthcare sector in particular is experiencing a revolution which will change our daily routine considerably in the near future. Due to its advanced digitization and its historical technical affinity radiology is especially prone to these developments. But what exactly is AI and what makes AI so potent that established medical disciplines such as radiology worry about their future job perspectives? What are the assets of AI in radiology today - and what are the major challenges? This review article tries to give some answers to these questions.
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Affiliation(s)
- Bettina Baessler
- Institut für Diagnostische und Interventionelle Radiologie, Universitätsspital Zürich
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11
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Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10228298] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.
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12
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Ramadan SZ. Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:9162464. [PMID: 32300474 PMCID: PMC7091549 DOI: 10.1155/2020/9162464] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 12/25/2019] [Accepted: 02/13/2020] [Indexed: 12/28/2022]
Abstract
According to the American Cancer Society's forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.
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Affiliation(s)
- Saleem Z. Ramadan
- Department of Industrial Engineering, German Jordanian University, Mushaqar 11180, Amman, Jordan
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van Hoek J, Huber A, Leichtle A, Härmä K, Hilt D, von Tengg-Kobligk H, Heverhagen J, Poellinger A. A survey on the future of radiology among radiologists, medical students and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over. Eur J Radiol 2019; 121:108742. [DOI: 10.1016/j.ejrad.2019.108742] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 10/28/2019] [Accepted: 11/06/2019] [Indexed: 02/07/2023]
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14
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Gardezi SJS, Elazab A, Lei B, Wang T. Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review. J Med Internet Res 2019; 21:e14464. [PMID: 31350843 PMCID: PMC6688437 DOI: 10.2196/14464] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 06/11/2019] [Accepted: 06/12/2019] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Machine learning (ML) has become a vital part of medical imaging research. ML methods have evolved over the years from manual seeded inputs to automatic initializations. The advancements in the field of ML have led to more intelligent and self-reliant computer-aided diagnosis (CAD) systems, as the learning ability of ML methods has been constantly improving. More and more automated methods are emerging with deep feature learning and representations. Recent advancements of ML with deeper and extensive representation approaches, commonly known as deep learning (DL) approaches, have made a very significant impact on improving the diagnostics capabilities of the CAD systems. OBJECTIVE This review aimed to survey both traditional ML and DL literature with particular application for breast cancer diagnosis. The review also provided a brief insight into some well-known DL networks. METHODS In this paper, we present an overview of ML and DL techniques with particular application for breast cancer. Specifically, we search the PubMed, Google Scholar, MEDLINE, ScienceDirect, Springer, and Web of Science databases and retrieve the studies in DL for the past 5 years that have used multiview mammogram datasets. RESULTS The analysis of traditional ML reveals the limited usage of the methods, whereas the DL methods have great potential for implementation in clinical analysis and improve the diagnostic capability of existing CAD systems. CONCLUSIONS From the literature, it can be found that heterogeneous breast densities make masses more challenging to detect and classify compared with calcifications. The traditional ML methods present confined approaches limited to either particular density type or datasets. Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms.
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Affiliation(s)
- Syed Jamal Safdar Gardezi
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong, Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Ahmed Elazab
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong, Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Baiying Lei
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong, Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Tianfu Wang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong, Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
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15
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He T, Puppala M, Ezeana CF, Huang YS, Chou PH, Yu X, Chen S, Wang L, Yin Z, Danforth RL, Ensor J, Chang J, Patel T, Wong ST. A Deep Learning-Based Decision Support Tool for Precision Risk Assessment of Breast Cancer. JCO Clin Cancer Inform 2019; 3:1-12. [PMID: 31141423 PMCID: PMC10445790 DOI: 10.1200/cci.18.00121] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2019] [Indexed: 08/25/2023] Open
Abstract
PURPOSE The Breast Imaging Reporting and Data System (BI-RADS) lexicon was developed to standardize mammographic reporting to assess cancer risk and facilitate the decision to biopsy. Because of substantial interobserver variability in the application of the BI-RADS lexicon, the decision to biopsy varies greatly and results in overdiagnosis and excessive biopsies. The false-positive rate from mammograms is estimated to be 7% to approximately 10% overall, but within the BI-RADS 4 category, it is greater than 70%. Therefore, we developed the Breast Cancer Risk Calculator (BRISK) to target a well-characterized and specific patient subgroup (BI-RADS 4) rather than a broad heterogeneous group in assessing breast cancer risk. METHODS BRISK provides a novel precise risk assessment model to reduce overdiagnosis and unnecessary biopsies. It was developed by applying natural language processing and deep learning methods on 5,147 patient records archived in the Houston Methodist systemwide data warehouse from 2006 to May 2015, including imaging and pathology reports, mammographic images, and patient demographics. Key characteristics for BI-RADS 4 patients were collected and computed to output an index measure for biopsy recommendation that is clinically relevant and informative and improves upon the traditional BI-RADS 4 scores. RESULTS For the validation set, we assessed data from 1,247 BI-RADS 4 patients, including mammographic images and medical reports. The BRISK model sensitivity to predict malignancy was 100%, whereas the specificity was 74%. The total accuracy of our implemented model in BRISK was 81%. Overall area under the curve was 0.93. CONCLUSION BRISK for abnormal mammogram uses integrative artificial intelligence technology and has demonstrated high sensitivity in the prediction of malignancy. Prospective evaluation is under way and can lead to improvement in patient-physician engagement in making informed decisions with regard to biopsy.
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Affiliation(s)
| | | | | | - Yan-siang Huang
- Houston Methodist, Houston, TX
- Far-Eastern Memorial Hospital, Taiwan,
Republic of China
| | - Ping-hsuan Chou
- Houston Methodist, Houston, TX
- Far-Eastern Memorial Hospital, Taiwan,
Republic of China
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What the radiologist should know about artificial intelligence - an ESR white paper. Insights Imaging 2019; 10:44. [PMID: 30949865 PMCID: PMC6449411 DOI: 10.1186/s13244-019-0738-2] [Citation(s) in RCA: 155] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 03/20/2019] [Indexed: 02/08/2023] Open
Abstract
This paper aims to provide a review of the basis for application of AI in radiology, to discuss the immediate ethical and professional impact in radiology, and to consider possible future evolution.Even if AI does add significant value to image interpretation, there are implications outside the traditional radiology activities of lesion detection and characterisation. In radiomics, AI can foster the analysis of the features and help in the correlation with other omics data. Imaging biobanks would become a necessary infrastructure to organise and share the image data from which AI models can be trained. AI can be used as an optimising tool to assist the technologist and radiologist in choosing a personalised patient's protocol, tracking the patient's dose parameters, providing an estimate of the radiation risks. AI can also aid the reporting workflow and help the linking between words, images, and quantitative data. Finally, AI coupled with CDS can improve the decision process and thereby optimise clinical and radiological workflow.
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Abstract
OBJECTIVE The purpose of this article is to discuss potential applications of artificial intelligence (AI) in breast imaging and limitations that may slow or prevent its adoption. CONCLUSION The algorithms of AI for workflow improvement and outcome analyses are advancing. Using imaging data of high quality and quantity, AI can support breast imagers in diagnosis and patient management, but AI cannot yet be relied on or be responsible for physicians' decisions that may affect survival. Education in AI is urgently needed for physicians.
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Pan P, Jin W, Li X, Chen Y, Jiang J, Wan H, Yu D. Optimization of multiplex quantitative polymerase chain reaction based on response surface methodology and an artificial neural network-genetic algorithm approach. PLoS One 2018; 13:e0200962. [PMID: 30044832 PMCID: PMC6059488 DOI: 10.1371/journal.pone.0200962] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 07/04/2018] [Indexed: 11/19/2022] Open
Abstract
Multiplex quantitative polymerase chain reaction (qPCR) has found an increasing range of applications. The construction of a reliable and dynamic mathematical model for multiplex qPCR that analyzes the effects of interactions between variables is therefore especially important. This work aimed to analyze the effects of interactions between variables through response surface method (RSM) for uni- and multiplex qPCR, and further optimize the parameters by constructing two mathematical models via RSM and back-propagation neural network-genetic algorithm (BPNN-GA) respectively. The statistical analysis showed that Mg2+ was the most important factor for both uni- and multiplex qPCR. Dynamic models of uni- and multiplex qPCR could be constructed using both RSM and BPNN-GA methods. But RSM was better than BPNN-GA on prediction performance in terms of the mean absolute error (MAE), the mean square error (MSE) and the Coefficient of Determination (R2). Ultimately, optimal parameters of uni- and multiplex qPCR were determined by RSM.
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Affiliation(s)
- Ping Pan
- Hangzhou First People’s Hospital, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Weifeng Jin
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Xiaohong Li
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Yi Chen
- Hangzhou First People’s Hospital, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
- Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Jiahui Jiang
- Hangzhou First People’s Hospital, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Haitong Wan
- College of Life Science, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Daojun Yu
- Hangzhou First People’s Hospital, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
- Department of Clinical Laboratory, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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Fulceri F, Grossi E, Contaldo A, Narzisi A, Apicella F, Parrini I, Tancredi R, Calderoni S, Muratori F. Motor Skills as Moderators of Core Symptoms in Autism Spectrum Disorders: Preliminary Data From an Exploratory Analysis With Artificial Neural Networks. Front Psychol 2018; 9:2683. [PMID: 30687159 PMCID: PMC6333655 DOI: 10.3389/fpsyg.2018.02683] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 12/13/2018] [Indexed: 02/05/2023] Open
Abstract
Motor disturbances have been widely observed in children with autism spectrum disorder (ASD), and motor problems are currently reported as associated features supporting the diagnosis of ASD in the current Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Studies on this issue reported disturbances in different motor domains, including both gross and fine motor areas as well as coordination, postural control, and standing balance. However, they failed to clearly state whether motor impairments are related to demographical and developmental features of ASD. Both the different methodological approaches assessing motor skills and the heterogeneity in clinical features of participants analyzed have been implicated as contributors to variance in findings. However, the non-linearity of the relationships between variables may account for the inability of the traditional analysis to grasp the core problem suggesting that the "single symptom approach analysis" should be overcome. Artificial neural networks (ANNs) are computational adaptive systems inspired by the functioning processes of the human brain particularly adapted to solving non-linear problems. This study aimed to apply the ANNs to reveal the entire spectrum of the relationship between motor skills and clinical variables. Thirty-two male children with ASD [mean age: 48.5 months (SD: 8.8); age range: 30-60 months] were recruited in a tertiary care university hospital. A multidisciplinary comprehensive diagnostic evaluation was associated with a standardized assessment battery for motor skills, the Peabody Developmental Motor Scale-Second Edition. Exploratory analyses were performed through the ANNs. The findings revealed that poor motor skills were a common clinical feature of preschoolers with ASD, relating both to the high level of repetitive behaviors and to the low level of expressive language. Moreover, unobvious trends among motor, cognitive and social skills have been detected. In conclusion, motor abnormalities in preschoolers with ASD were widespread, and the degree of impairment may inform clinicians about the severity of ASD core symptoms. Understanding motor disturbances in children with ASD may be relevant to clarify neurobiological basis and ultimately to guide the development of tailored treatments.
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Affiliation(s)
- Francesca Fulceri
- Research Coordination and Support Service, Istituto Superiore di Sanità, Rome, Italy
| | - Enzo Grossi
- Autism Research Unit, Villa Santa Maria Institute, Tavernerio, Italy
| | | | | | | | | | | | - Sara Calderoni
- IRCCS Fondazione Stella Maris, Pisa, Italy
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
- *Correspondence: Sara Calderoni, ;
| | - Filippo Muratori
- IRCCS Fondazione Stella Maris, Pisa, Italy
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
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van der Waal I. Skin cancer diagnosed using artificial intelligence on clinical images. Oral Dis 2017; 24:873-874. [DOI: 10.1111/odi.12668] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Accepted: 03/16/2017] [Indexed: 01/08/2023]
Affiliation(s)
- I van der Waal
- Department of Oral and Maxillofacial Surgery/Pathology; VU Medical Center/ACTA; Amsterdam The Netherlands
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Mehdy MM, Ng PY, Shair EF, Saleh NIM, Gomes C. Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:2610628. [PMID: 28473865 PMCID: PMC5394406 DOI: 10.1155/2017/2610628] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2017] [Accepted: 03/09/2017] [Indexed: 12/26/2022]
Abstract
Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. Automated classifiers could substantially upgrade the diagnosis process, in terms of both accuracy and time requirement by distinguishing benign and malignant patterns automatically. Neural network (NN) plays an important role in this respect, especially in the application of breast cancer detection. Despite the large number of publications that describe the utilization of NN in various medical techniques, only a few reviews are available that guide the development of these algorithms to enhance the detection techniques with respect to specificity and sensitivity. The purpose of this review is to analyze the contents of recently published literature with special attention to techniques and states of the art of NN in medical imaging. We discuss the usage of NN in four different medical imaging applications to show that NN is not restricted to few areas of medicine. Types of NN used, along with the various types of feeding data, have been reviewed. We also address hybrid NN adaptation in breast cancer detection.
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Affiliation(s)
- M. M. Mehdy
- Department of Computer and Communication System Engineering, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - P. Y. Ng
- Department of Computer and Communication System Engineering, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - E. F. Shair
- Department of Electrical and Electronics Engineering, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - N. I. Md Saleh
- Department of Chemical and Environmental Engineering, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - C. Gomes
- Department of Electrical and Electronics Engineering, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
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Ruiz-Fernandez D, Jose Galiana-Merino J, Pacheco Lloret MB. Influence of the surrounded tissue in the detection of microcalcifications using wavelets. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:198-201. [PMID: 26736234 DOI: 10.1109/embc.2015.7318334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Breast cancer is the most common cancer in women. Many clinical decision support systems aimed to help in the diagnosis of breast cancer have been developed because an early diagnosis is fundamental to improve the results of the treatment. Most of the developments are aimed to detect microcalcifications using the same system and parameters for all the mammograms without considering any other characteristic of the breast. In this paper we introduce the type of tissue in the breast as an element that can affect the selection of the right algorithm to improve the detection rates. We adapt the system setup depending on the type of tissue improving the results of the aid system.
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Bozkurt S, Lipson JA, Senol U, Rubin DL. Automatic abstraction of imaging observations with their characteristics from mammography reports. J Am Med Inform Assoc 2014; 22:e81-92. [PMID: 25352567 DOI: 10.1136/amiajnl-2014-003009] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2014] [Accepted: 09/04/2014] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND Radiology reports are usually narrative, unstructured text, a format which hinders the ability to input report contents into decision support systems. In addition, reports often describe multiple lesions, and it is challenging to automatically extract information on each lesion and its relationships to characteristics, anatomic locations, and other information that describes it. The goal of our work is to develop natural language processing (NLP) methods to recognize each lesion in free-text mammography reports and to extract its corresponding relationships, producing a complete information frame for each lesion. MATERIALS AND METHODS We built an NLP information extraction pipeline in the General Architecture for Text Engineering (GATE) NLP toolkit. Sequential processing modules are executed, producing an output information frame required for a mammography decision support system. Each lesion described in the report is identified by linking it with its anatomic location in the breast. In order to evaluate our system, we selected 300 mammography reports from a hospital report database. RESULTS The gold standard contained 797 lesions, and our system detected 815 lesions (780 true positives, 35 false positives, and 17 false negatives). The precision of detecting all the imaging observations with their modifiers was 94.9, recall was 90.9, and the F measure was 92.8. CONCLUSIONS Our NLP system extracts each imaging observation and its characteristics from mammography reports. Although our application focuses on the domain of mammography, we believe our approach can generalize to other domains and may narrow the gap between unstructured clinical report text and structured information extraction needed for data mining and decision support.
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Affiliation(s)
- Selen Bozkurt
- Department of Biostatistics and Medical Informatics, Akdeniz University Faculty of Medicine, Antalya, Turkey
| | - Jafi A Lipson
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Utku Senol
- Department of Radiology, Akdeniz University Faculty of Medicine, Antalya, Turkey
| | - Daniel L Rubin
- Department of Radiology, Stanford University, Stanford, California, USA Department of Medicine (Biomedical Informatics Research), Stanford University, Stanford, California, USA
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