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López-Úbeda P, Martín-Noguerol T, Paulano-Godino F, Luna A. Comparative evaluation of image-based vs. text-based vs. multimodal AI approaches for automatic breast density assessment in mammograms. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 255:108334. [PMID: 39053353 DOI: 10.1016/j.cmpb.2024.108334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/23/2024] [Accepted: 07/17/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND AND OBJECTIVES In the last decade, there has been a growing interest in applying artificial intelligence (AI) systems to breast cancer assessment, including breast density evaluation. However, few models have been developed to integrate textual mammographic reports and mammographic images. Our aims are (1) to generate a natural language processing (NLP)-based AI system, (2) to evaluate an external image-based software, and (3) to develop a multimodal system, using the late fusion approach, by integrating image and text inferences for the automatic classification of breast density according to the American College of Radiology (ACR) guidelines in mammograms and radiological reports. METHODS We first compared different NLP models, three based on n-gram term frequency - inverse document frequency and two transformer-based architectures, using 1533 unstructured mammogram reports as a training set and 303 reports as a test set. Subsequently, we evaluated an external image-based software using 303 mammogram images. Finally, we assessed our multimodal system taking into account both text and mammogram images. RESULTS Our best NLP model achieved 88 % accuracy, while the external software and the multimodal system achieved 75 % and 80 % accuracy, respectively, in classifying ACR breast densities. CONCLUSION Although our multimodal system outperforms the image-based tool, it currently does not improve the results offered by the NLP model for ACR breast density classification. Nevertheless, the promising results observed here open the possibility to more comprehensive studies regarding the utilization of multimodal tools in the assessment of breast density.
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Affiliation(s)
| | | | - Félix Paulano-Godino
- Image Processing Unit, Engineering Department, HT Médica, Carmelo Torres n 2, 23007, Jaén, Spain
| | - Antonio Luna
- MRI unit, Radiology department, HT Médica, Carmelo Torres n 2, 23007, Jaén, Spain
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Li JW, Sheng DL, Chen JG, You C, Liu S, Xu HX, Chang C. Artificial intelligence in breast imaging: potentials and challenges. Phys Med Biol 2023; 68:23TR01. [PMID: 37722385 DOI: 10.1088/1361-6560/acfade] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.
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Affiliation(s)
- Jia-Wei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Dan-Li Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Shuai Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, People's Republic of China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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3
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Abbey CK, Zuley ML, Victor JD. Local texture statistics augment the power spectrum in modeling radiographic judgments of breast density. J Med Imaging (Bellingham) 2023; 10:065502. [PMID: 38074625 PMCID: PMC10704190 DOI: 10.1117/1.jmi.10.6.065502] [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: 05/23/2023] [Revised: 10/05/2023] [Accepted: 10/16/2023] [Indexed: 02/12/2024] Open
Abstract
Purpose Anatomical "noise" is an important limitation of full-field digital mammography. Understanding its impact on clinical judgments is made difficult by the complexity of breast parenchyma, which results in image texture not fully captured by the power spectrum. While the number of possible parameters for characterizing anatomical noise is quite large, a specific set of local texture statistics has been shown to be visually salient, and human sensitivity to these statistics corresponds to their informativeness in natural scenes. Approach We evaluate these local texture statistics in addition to standard power-spectral measures to determine whether they have additional explanatory value for radiologists' breast density judgments. We analyzed an image database consisting of 111 disease-free mammographic screening exams (4 views each) acquired at the University of Pittsburgh Medical Center. Each exam had a breast density score assigned by the examining radiologist. Power-spectral descriptors and local image statistics were extracted from images of breast parenchyma. Model-selection criteria and accuracy were used to assess the explanatory and predictive value of local image statistics for breast density judgments. Results The model selection criteria show that adding local texture statistics to descriptors of the power spectra produce better explanatory and predictive models of radiologists' judgments of breast density. Thus, local texture statistics capture, in some form, non-Gaussian aspects of texture that radiologists are using. Conclusions Since these local texture statistics are expected to be impacted by imaging factors like modality, dose, and image processing, they suggest avenues for understanding and optimizing observer performance.
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Affiliation(s)
- Craig K. Abbey
- University of California, Santa Barbara, Department of Psychological and Brain Sciences, Santa Barbara, California, United States
| | - Margarita L. Zuley
- University of Pittsburgh Medical Center, Department of Radiology, Pittsburgh, Pennsylvania, United States
| | - Jonathan D. Victor
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, United States
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Iqbal J, Cortés Jaimes DC, Makineni P, Subramani S, Hemaida S, Thugu TR, Butt AN, Sikto JT, Kaur P, Lak MA, Augustine M, Shahzad R, Arain M. Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine. Cureus 2023; 15:e44658. [PMID: 37799217 PMCID: PMC10549955 DOI: 10.7759/cureus.44658] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI's precision and analysis regarding patients' genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI's contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI's capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI's impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.
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Affiliation(s)
| | - Diana Carolina Cortés Jaimes
- Epidemiology, Universidad Autónoma de Bucaramanga, Bucaramanga, COL
- Medicine, Pontificia Universidad Javeriana, Bogotá, COL
| | - Pallavi Makineni
- Medicine, All India Institute of Medical Sciences, Bhubaneswar, Bhubaneswar, IND
| | - Sachin Subramani
- Medicine and Surgery, Employees' State Insurance Corporation (ESIC) Medical College, Gulbarga, IND
| | - Sarah Hemaida
- Internal Medicine, Istanbul Okan University, Istanbul, TUR
| | - Thanmai Reddy Thugu
- Internal Medicine, Sri Padmavathi Medical College for Women, Sri Venkateswara Institute of Medical Sciences (SVIMS), Tirupati, IND
| | - Amna Naveed Butt
- Medicine/Internal Medicine, Allama Iqbal Medical College, Lahore, PAK
| | | | - Pareena Kaur
- Medicine, Punjab Institute of Medical Sciences, Jalandhar, IND
| | | | | | - Roheen Shahzad
- Medicine, Combined Military Hospital (CMH) Lahore Medical College and Institute of Dentistry, Lahore, PAK
| | - Mustafa Arain
- Internal Medicine, Civil Hospital Karachi, Karachi, PAK
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5
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Oiwa M, Suda N, Morita T, Takahashi Y, Sato Y, Hayashi T, Kato A, Nishimura R, Ichihara S, Endo T. Validity of computed mean compressed fibroglandular tissue thickness and breast composition for stratification of masking risk in Japanese women. Breast Cancer 2023:10.1007/s12282-023-01444-7. [PMID: 36920730 DOI: 10.1007/s12282-023-01444-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 02/23/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND The volumetric measurement system for mammographic breast density is a high-precision objective method for evaluating the percentage of fibroglandular tissue volume (FG%). Nonetheless, FG% does not precisely correlate with subjective visual estimation (SVE) and shows poor evaluation performance regarding masking risk in patients with comparatively thin compressed breast thickness (CBT), commonly found in Japanese women. We considered that the mean compressed fibroglandular tissue thickness (mCGT), which incorporates the CBT element into the evaluation of breast density, may better predict masking risk. METHODS Volumetric measurements and SVEs were performed on mammograms of 108 breast cancer patients from our center. mCGT was calculated as the product of CBT and FG%. SVE was classified using the Breast Imaging-Reporting and Data System classification, 5th edition. Subsequently, the performance of mCGT, SVE, and FG% in predicting masking risk was estimated using the AUC. RESULTS The AUC values of mCGT and SVE were 0.84 (95% confidence interval, 0.71-0.92) and 0.78 (0.66-0.86), respectively (P = 0.16). The AUC of the FG% was 0.65 (0.52-0.77), which was significantly lower than that of mCGT (P < 0.001). The sensitivity and specificity of mCGT in predicting negative detection were 89% and 71%, respectively; of SVE 83% and 61% (versus 72% and 57% with FG%), suggesting that mCGT was superior to FG% in both sensitivity and specificity, and comparable with SVE. CONCLUSIONS Objective mCGT calculated from the volumetric measurement system will highly likely be useful in evaluating breast density and supporting visual assessment for masking risk stratification.
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Affiliation(s)
- Mikinao Oiwa
- Department of Radiology, National Hospital Organization Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan.
| | - Namiko Suda
- Department of Breast Surgery, National Hospital Organization Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
| | - Takako Morita
- Department of Breast Surgery, National Hospital Organization Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
| | - Yuko Takahashi
- Department of Breast Surgery, National Hospital Organization Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
| | - Yasuyuki Sato
- Department of Breast Surgery, National Hospital Organization Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
| | - Takako Hayashi
- Department of Breast Surgery, National Hospital Organization Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
| | - Aya Kato
- Department of Breast Surgery, National Hospital Organization Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
| | - Rieko Nishimura
- Department of Advanced Diagnosis, National Hospital Organization Division of Pathology, Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
| | - Shu Ichihara
- Department of Advanced Diagnosis, National Hospital Organization Division of Pathology, Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
| | - Tokiko Endo
- Department of Radiology, National Hospital Organization Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
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Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review. Diagnostics (Basel) 2022; 12:diagnostics12123111. [PMID: 36553119 PMCID: PMC9777253 DOI: 10.3390/diagnostics12123111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.
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7
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Artificial intelligence and machine learning in cancer imaging. COMMUNICATIONS MEDICINE 2022; 2:133. [PMID: 36310650 PMCID: PMC9613681 DOI: 10.1038/s43856-022-00199-0] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 10/06/2022] [Indexed: 11/16/2022] Open
Abstract
An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.
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Mansour S, Soliman S, Kansakar A, Marey A, Hunold C, Hanafy MM. Strengths and challenges of the artificial intelligence in the assessment of dense breasts. BJR Open 2022; 4:20220018. [PMID: 38525169 PMCID: PMC10958665 DOI: 10.1259/bjro.20220018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 06/27/2022] [Accepted: 07/20/2022] [Indexed: 11/05/2022] Open
Abstract
Objectives High breast density is a risk factor for breast cancer and overlapping of glandular tissue can mask lesions thus lowering mammographic sensitivity. Also, dense breasts are more vulnerable to increase recall rate and false-positive results. New generations of artificial intelligence (AI) have been introduced to the realm of mammography. We aimed to assess the strengths and challenges of adopting artificial intelligence in reading mammograms of dense breasts. Methods This study included 6600 mammograms of dense patterns "c" and "d" and presented 4061 breast abnormalities. All the patients were subjected to full-field digital mammography, breast ultrasound, and their mammographic images were scanned by AI software (Lunit INSIGHT MMG). Results Diagnostic indices of the sono-mammography: a sensitivity of 98.71%, a specificity of 88.04%, a positive-predictive value of 80.16%, a negative-predictive value of 99.29%, and a diagnostic accuracy of 91.5%. AI-aided mammograms presented sensitivity of 88.29%, a specificity of 96.34%, a positive-predictive value of 92.2%, a negative-predictive value of 94.4%, and a diagnostic accuracy of 94.5% in its ability to read dense mammograms. Conclusion Dense breasts scanned with AI showed a notable reduction of mammographic misdiagnosis. Knowledge of such software challenges would enhance its application as a decision support tool to mammography in the diagnosis of cancer. Advances in knowledge Dense breast is challenging for radiologists and renders low sensitivity mammogram. Mammogram scanned by AI could be used to overcome such limitation, enhance the discrimination between benign and malignant breast abnormalities and the early detection of breast cancer.
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Affiliation(s)
| | - Somia Soliman
- Pathology, Kasr ElAiny Hospital, Cairo University, Cairo, Egypt
| | - Abisha Kansakar
- Radiology, Kasr ElAiny Hospital, Cairo University, Cairo, Egypt
| | - Ahmed Marey
- FUJIFILM Medical Imaging and IT Solutions MEA, Dubai, United Arab Emirates
| | - Christiane Hunold
- FUJIFILM Medical Imaging and IT Solutions MEA, Dubai, United Arab Emirates
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Elezaby MA. Impact of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice: A Methodologic Framework for Clinical Testing of Artificial Intelligence Tools. J Am Coll Radiol 2022; 19:1031-1033. [PMID: 35690078 DOI: 10.1016/j.jacr.2022.05.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 05/04/2022] [Indexed: 10/18/2022]
Affiliation(s)
- Mai A Elezaby
- Associate Section Chief, Breast Imaging and Intervention Section, Associate Program Director, Breast Imaging Fellowship, and Associate Program Director, Diagnostic Radiology Residency, Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
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Naik N, Hameed BMZ, Shetty DK, Swain D, Shah M, Paul R, Aggarwal K, Ibrahim S, Patil V, Smriti K, Shetty S, Rai BP, Chlosta P, Somani BK. Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility? Front Surg 2022; 9:862322. [PMID: 35360424 PMCID: PMC8963864 DOI: 10.3389/fsurg.2022.862322] [Citation(s) in RCA: 125] [Impact Index Per Article: 62.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 02/18/2022] [Indexed: 01/04/2023] Open
Abstract
The legal and ethical issues that confront society due to Artificial Intelligence (AI) include privacy and surveillance, bias or discrimination, and potentially the philosophical challenge is the role of human judgment. Concerns about newer digital technologies becoming a new source of inaccuracy and data breaches have arisen as a result of its use. Mistakes in the procedure or protocol in the field of healthcare can have devastating consequences for the patient who is the victim of the error. Because patients come into contact with physicians at moments in their lives when they are most vulnerable, it is crucial to remember this. Currently, there are no well-defined regulations in place to address the legal and ethical issues that may arise due to the use of artificial intelligence in healthcare settings. This review attempts to address these pertinent issues highlighting the need for algorithmic transparency, privacy, and protection of all the beneficiaries involved and cybersecurity of associated vulnerabilities.
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Affiliation(s)
- Nithesh Naik
- Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
- International Training and Research in Uro-Oncology and Endourology Group, Manipal, India
| | - B. M. Zeeshan Hameed
- International Training and Research in Uro-Oncology and Endourology Group, Manipal, India
- Department of Urology, Father Muller Medical College, Mangalore, India
| | - Dasharathraj K. Shetty
- Department of Humanities and Management, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Dishant Swain
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Milap Shah
- International Training and Research in Uro-Oncology and Endourology Group, Manipal, India
- Robotics and Urooncology, Max Hospital and Max Institute of Cancer Care, New Delhi, India
| | - Rahul Paul
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Kaivalya Aggarwal
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Sufyan Ibrahim
- International Training and Research in Uro-Oncology and Endourology Group, Manipal, India
- Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India
| | - Vathsala Patil
- Department of Oral Medicine and Radiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal, India
| | - Komal Smriti
- Department of Oral Medicine and Radiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal, India
| | - Suyog Shetty
- Department of Urology, Father Muller Medical College, Mangalore, India
| | - Bhavan Prasad Rai
- International Training and Research in Uro-Oncology and Endourology Group, Manipal, India
- Department of Urology, Freeman Hospital, Newcastle upon Tyne, United Kingdom
| | - Piotr Chlosta
- Department of Urology, Jagiellonian University in Krakow, Kraków, Poland
| | - Bhaskar K. Somani
- International Training and Research in Uro-Oncology and Endourology Group, Manipal, India
- Department of Urology, University Hospital Southampton National Health Service (NHS) Trust, Southampton, United Kingdom
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11
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Clift AK, Dodwell D, Lord S, Petrou S, Brady SM, Collins GS, Hippisley-Cox J. The current status of risk-stratified breast screening. Br J Cancer 2022; 126:533-550. [PMID: 34703006 PMCID: PMC8854575 DOI: 10.1038/s41416-021-01550-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 08/25/2021] [Accepted: 09/14/2021] [Indexed: 12/23/2022] Open
Abstract
Apart from high-risk scenarios such as the presence of highly penetrant genetic mutations, breast screening typically comprises mammography or tomosynthesis strategies defined by age. However, age-based screening ignores the range of breast cancer risks that individual women may possess and is antithetical to the ambitions of personalised early detection. Whilst screening mammography reduces breast cancer mortality, this is at the risk of potentially significant harms including overdiagnosis with overtreatment, and psychological morbidity associated with false positives. In risk-stratified screening, individualised risk assessment may inform screening intensity/interval, starting age, imaging modality used, or even decisions not to screen. However, clear evidence for its benefits and harms needs to be established. In this scoping review, the authors summarise the established and emerging evidence regarding several critical dependencies for successful risk-stratified breast screening: risk prediction model performance, epidemiological studies, retrospective clinical evaluations, health economic evaluations and qualitative research on feasibility and acceptability. Family history, breast density or reproductive factors are not on their own suitable for precisely estimating risk and risk prediction models increasingly incorporate combinations of demographic, clinical, genetic and imaging-related parameters. Clinical evaluations of risk-stratified screening are currently limited. Epidemiological evidence is sparse, and randomised trials only began in recent years.
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Affiliation(s)
- Ash Kieran Clift
- Cancer Research UK Oxford Centre, Department of Oncology, University of Oxford, Oxford, UK.
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
| | - David Dodwell
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Simon Lord
- Department of Oncology, University of Oxford, Oxford, UK
| | - Stavros Petrou
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Julia Hippisley-Cox
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
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12
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Lei YM, Yin M, Yu MH, Yu J, Zeng SE, Lv WZ, Li J, Ye HR, Cui XW, Dietrich CF. Artificial Intelligence in Medical Imaging of the Breast. Front Oncol 2021; 11:600557. [PMID: 34367938 PMCID: PMC8339920 DOI: 10.3389/fonc.2021.600557] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 07/07/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, in vitro diagnosis, intelligent rehabilitation, and prognosis. Breast cancer is one of the common malignant tumors in women and seriously threatens women’s physical and mental health. Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. AI has shown excellent performance in image recognition tasks and has been widely studied in breast cancer screening. This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast density assessment; and breast cancer risk assessment. In addition, we also discuss the challenges and future perspectives of the application of AI in medical imaging of the breast.
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Affiliation(s)
- Yu-Meng Lei
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Miao Yin
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Mei-Hui Yu
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Jing Yu
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Shu-E Zeng
- Department of Medical Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wen-Zhi Lv
- Department of Artificial Intelligence, Julei Technology, Wuhan, China
| | - Jun Li
- Department of Medical Ultrasound, The First Affiliated Hospital of Medical College, Shihezi University, Xinjiang, China
| | - Hua-Rong Ye
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Christoph F Dietrich
- Department Allgemeine Innere Medizin (DAIM), Kliniken Beau Site, Salem und Permanence, Bern, Switzerland
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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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14
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Lennartz S, Dratsch T, Zopfs D, Persigehl T, Maintz D, Große Hokamp N, Pinto Dos Santos D. Use and Control of Artificial Intelligence in Patients Across the Medical Workflow: Single-Center Questionnaire Study of Patient Perspectives. J Med Internet Res 2021; 23:e24221. [PMID: 33595451 PMCID: PMC7929746 DOI: 10.2196/24221] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 11/01/2020] [Accepted: 11/30/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) is gaining increasing importance in many medical specialties, yet data on patients' opinions on the use of AI in medicine are scarce. OBJECTIVE This study aimed to investigate patients' opinions on the use of AI in different aspects of the medical workflow and the level of control and supervision under which they would deem the application of AI in medicine acceptable. METHODS Patients scheduled for computed tomography or magnetic resonance imaging voluntarily participated in an anonymized questionnaire between February 10, 2020, and May 24, 2020. Patient information, confidence in physicians vs AI in different clinical tasks, opinions on the control of AI, preference in cases of disagreement between AI and physicians, and acceptance of the use of AI for diagnosing and treating diseases of different severity were recorded. RESULTS In total, 229 patients participated. Patients favored physicians over AI for all clinical tasks except for treatment planning based on current scientific evidence. In case of disagreement between physicians and AI regarding diagnosis and treatment planning, most patients preferred the physician's opinion to AI (96.2% [153/159] vs 3.8% [6/159] and 94.8% [146/154] vs 5.2% [8/154], respectively; P=.001). AI supervised by a physician was considered more acceptable than AI without physician supervision at diagnosis (confidence rating 3.90 [SD 1.20] vs 1.64 [SD 1.03], respectively; P=.001) and therapy (3.77 [SD 1.18] vs 1.57 [SD 0.96], respectively; P=.001). CONCLUSIONS Patients favored physicians over AI in most clinical tasks and strongly preferred an application of AI with physician supervision. However, patients acknowledged that AI could help physicians integrate the most recent scientific evidence into medical care. Application of AI in medicine should be disclosed and controlled to protect patient interests and meet ethical standards.
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Affiliation(s)
- Simon Lennartz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Thomas Dratsch
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - David Zopfs
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - David Maintz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nils Große Hokamp
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Daniel Pinto Dos Santos
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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15
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Imaging of Breast Cancers With Predilection for Nonmass Pattern of Growth: Invasive Lobular Carcinoma and DCIS-Does Imaging Capture It All? AJR Am J Roentgenol 2020; 215:1504-1511. [PMID: 33021831 DOI: 10.2214/ajr.19.22027] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
OBJECTIVE. Invasive lobular carcinoma (ILC) and ductal carcinoma in situ (DCIS) are distinct histopathologic entities with several commonalities: both have subtle clinical and imaging presentation, have been linked with controversy regarding optimal imaging techniques and management, and exemplify the codependence of adequate imaging evaluation and optimal treatment strategies in breast care. CONCLUSION. We review molecular mechanisms and histopathologic patterns that define the biologic behavior of both ILC and DCIS and discuss how these mechanisms translate into distinct clinical and imaging presentations that affect the staging workup and patient management algorithm.
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16
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Meyer-Bäse A, Morra L, Meyer-Bäse U, Pinker K. Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging. CONTRAST MEDIA & MOLECULAR IMAGING 2020; 2020:6805710. [PMID: 32934610 PMCID: PMC7474774 DOI: 10.1155/2020/6805710] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 04/17/2020] [Accepted: 05/28/2020] [Indexed: 12/12/2022]
Abstract
Recent advances in artificial intelligence (AI) and deep learning (DL) have impacted many scientific fields including biomedical maging. Magnetic resonance imaging (MRI) is a well-established method in breast imaging with several indications including screening, staging, and therapy monitoring. The rapid development and subsequent implementation of AI into clinical breast MRI has the potential to affect clinical decision-making, guide treatment selection, and improve patient outcomes. The goal of this review is to provide a comprehensive picture of the current status and future perspectives of AI in breast MRI. We will review DL applications and compare them to standard data-driven techniques. We will emphasize the important aspect of developing quantitative imaging biomarkers for precision medicine and the potential of breast MRI and DL in this context. Finally, we will discuss future challenges of DL applications for breast MRI and an AI-augmented clinical decision strategy.
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Affiliation(s)
- Anke Meyer-Bäse
- Department of Scientific Computing, Florida State University, Tallahassee, Florida 32310-4120, USA
| | - Lia Morra
- Dipartimento di Automatica e Informatica, Politecnico di Torino, Torino, Italy
| | - Uwe Meyer-Bäse
- Department of Electrical and Computer Engineering, Florida A&M University and Florida State University, Tallahassee, Florida 32310-4120, USA
| | - Katja Pinker
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA
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17
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Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17165993. [PMID: 32824765 PMCID: PMC7460520 DOI: 10.3390/ijerph17165993] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 08/09/2020] [Accepted: 08/12/2020] [Indexed: 12/21/2022]
Abstract
Myometrial invasion affects the prognosis of endometrial cancer. However, discrepancies exist between pre-operative magnetic resonance imaging staging and post-operative pathological staging. This study aims to validate the accuracy of artificial intelligence (AI) for detecting the depth of myometrial invasion using a deep learning technique on magnetic resonance images. We obtained 4896 contrast-enhanced T1-weighted images (T1w) and T2-weighted images (T2w) from 72 patients who were diagnosed with surgico-pathological stage I endometrial carcinoma. We used the images from 24 patients (33.3%) to train the AI. The images from the remaining 48 patients (66.7%) were used to evaluate the accuracy of the model. The AI then interpreted each of the cases and sorted them into stage IA or IB. Compared with the accuracy rate of radiologists’ diagnoses (77.8%), the accuracy rate of AI interpretation in contrast-enhanced T1w was higher (79.2%), whereas that in T2w was lower (70.8%). The diagnostic accuracy was not significantly different between radiologists and AI for both T1w and T2w. However, AI was more likely to provide incorrect interpretations in patients with coexisting benign leiomyomas or polypoid tumors. Currently, the ability of this AI technology to make an accurate diagnosis has limitations. However, in hospitals with limited resources, AI may be able to assist in reading magnetic resonance images. We believe that AI has the potential to assist radiologists or serve as a reasonable alternative for pre-operative evaluation of the myometrial invasion depth of stage I endometrial cancers.
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18
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Bahl M. Artificial Intelligence: A Primer for Breast Imaging Radiologists. JOURNAL OF BREAST IMAGING 2020; 2:304-314. [PMID: 32803154 PMCID: PMC7418877 DOI: 10.1093/jbi/wbaa033] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science dedicated to developing computer algorithms that emulate intelligent human behavior. Subfields of AI include machine learning and deep learning. Advances in AI technologies have led to techniques that could increase breast cancer detection, improve clinical efficiency in breast imaging practices, and guide decision-making regarding screening and prevention strategies. This article reviews key terminology and concepts, discusses common AI models and methods to validate and evaluate these models, describes emerging AI applications in breast imaging, and outlines challenges and future directions. Familiarity with AI terminology, concepts, methods, and applications is essential for breast imaging radiologists to critically evaluate these emerging technologies, recognize their strengths and limitations, and ultimately ensure optimal patient care.
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Affiliation(s)
- Manisha Bahl
- Massachusetts General Hospital, Department of Radiology, Boston, MA
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Morley J, Machado CCV, Burr C, Cowls J, Joshi I, Taddeo M, Floridi L. The ethics of AI in health care: A mapping review. Soc Sci Med 2020; 260:113172. [PMID: 32702587 DOI: 10.1016/j.socscimed.2020.113172] [Citation(s) in RCA: 153] [Impact Index Per Article: 38.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 06/22/2020] [Accepted: 06/23/2020] [Indexed: 02/06/2023]
Abstract
This article presents a mapping review of the literature concerning the ethics of artificial intelligence (AI) in health care. The goal of this review is to summarise current debates and identify open questions for future research. Five literature databases were searched to support the following research question: how can the primary ethical risks presented by AI-health be categorised, and what issues must policymakers, regulators and developers consider in order to be 'ethically mindful? A series of screening stages were carried out-for example, removing articles that focused on digital health in general (e.g. data sharing, data access, data privacy, surveillance/nudging, consent, ownership of health data, evidence of efficacy)-yielding a total of 156 papers that were included in the review. We find that ethical issues can be (a) epistemic, related to misguided, inconclusive or inscrutable evidence; (b) normative, related to unfair outcomes and transformative effectives; or (c) related to traceability. We further find that these ethical issues arise at six levels of abstraction: individual, interpersonal, group, institutional, and societal or sectoral. Finally, we outline a number of considerations for policymakers and regulators, mapping these to existing literature, and categorising each as epistemic, normative or traceability-related and at the relevant level of abstraction. Our goal is to inform policymakers, regulators and developers of what they must consider if they are to enable health and care systems to capitalise on the dual advantage of ethical AI; maximising the opportunities to cut costs, improve care, and improve the efficiency of health and care systems, whilst proactively avoiding the potential harms. We argue that if action is not swiftly taken in this regard, a new 'AI winter' could occur due to chilling effects related to a loss of public trust in the benefits of AI for health care.
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Affiliation(s)
- Jessica Morley
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK.
| | - Caio C V Machado
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK
| | - Christopher Burr
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK
| | - Josh Cowls
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK; Alan Turing Institute, British Library, 96 Euston Rd, London, NW1 2DB, UK
| | - Indra Joshi
- NHSX, Skipton House, 80 London Road, SE1 6LH, UK
| | - Mariarosaria Taddeo
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK; Alan Turing Institute, British Library, 96 Euston Rd, London, NW1 2DB, UK; Department of Computer Science, University of Oxford, 15 Parks Rd, Oxford, OX1 3QD, UK
| | - Luciano Floridi
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK; Alan Turing Institute, British Library, 96 Euston Rd, London, NW1 2DB, UK; Department of Computer Science, University of Oxford, 15 Parks Rd, Oxford, OX1 3QD, UK
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Sechopoulos I, Teuwen J, Mann R. Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art. Semin Cancer Biol 2020; 72:214-225. [PMID: 32531273 DOI: 10.1016/j.semcancer.2020.06.002] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 05/19/2020] [Accepted: 06/01/2020] [Indexed: 02/07/2023]
Abstract
Screening for breast cancer with mammography has been introduced in various countries over the last 30 years, initially using analog screen-film-based systems and, over the last 20 years, transitioning to the use of fully digital systems. With the introduction of digitization, the computer interpretation of images has been a subject of intense interest, resulting in the introduction of computer-aided detection (CADe) and diagnosis (CADx) algorithms in the early 2000's. Although they were introduced with high expectations, the potential improvement in the clinical realm failed to materialize, mostly due to the high number of false positive marks per analyzed image. In the last five years, the artificial intelligence (AI) revolution in computing, driven mostly by deep learning and convolutional neural networks, has also pervaded the field of automated breast cancer detection in digital mammography and digital breast tomosynthesis. Research in this area first involved comparison of its capabilities to that of conventional CADe/CADx methods, which quickly demonstrated the potential of this new technology. In the last couple of years, more mature and some commercial products have been developed, and studies of their performance compared to that of experienced breast radiologists are showing that these algorithms are on par with human-performance levels in retrospective data sets. Although additional studies, especially prospective evaluations performed in the real screening environment, are needed, it is becoming clear that AI will have an important role in the future breast cancer screening realm. Exactly how this new player will shape this field remains to be determined, but recent studies are already evaluating different options for implementation of this technology. The aim of this review is to provide an overview of the basic concepts and developments in the field AI for breast cancer detection in digital mammography and digital breast tomosynthesis. The pitfalls of conventional methods, and how these are, for the most part, avoided by this new technology, will be discussed. Importantly, studies that have evaluated the current capabilities of AI and proposals for how these capabilities should be leveraged in the clinical realm will be reviewed, while the questions that need to be answered before this vision becomes a reality are posed.
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Affiliation(s)
- Ioannis Sechopoulos
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands; Dutch Expert Centre for Screening (LRCB), Wijchenseweg 101, 6538 SW, Nijmegen, the Netherlands.
| | - Jonas Teuwen
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands; Department of Radiation Oncology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, 1066 CX, Amsterdam, the Netherlands.
| | - Ritse Mann
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands; Department of Radiology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, 1066 CX, Amsterdam, the Netherlands.
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Wójcik M. Augmented intelligence technology. The ethical and practical problems of its implementation in libraries. LIBRARY HI TECH 2020. [DOI: 10.1108/lht-02-2020-0043] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThe subject of the article is the concept of augmented intelligence, which constitutes a further stage in the development of research on artificial intelligence. This is a new phenomenon that has rarely been considered in the subject literature so far, which may be interesting for the fields of social sciences and humanities. The aim is to describe the features of this technology and determine the practical and ethical problems associated with its implementation in libraries.Design/methodology/approachThe method of literature review was used. Systematic searches according to specific questions were carried out using the Scopus and Web of Science scientific databases, as well as Google Scholar and the LISTA abstract database.FindingsThe results established that the issue of augmented intelligence has barely been discussed in the field of librarianship. Although this technology may be interesting as a new area of librarian research and as a new framework for designing innovative services, deep ethical consideration is necessary before this technology is introduced in libraries.Research limitations/implicationsThe article deals with some of the newest technologies available, and this topic is generally very rarely discussed in scientific publications in either the social sciences or humanities. Therefore, due to the limited availability of materials, the findings presented in the article are primarily of a conceptual nature. The aim is to present this topic from the perspective of librarianship and to create a starting point for further discussion on the ethical aspects of introducing new technologies in libraries.Practical implicationsThe results can be widely used in practice as a framework for the implementation of augmented intelligence in libraries.Social implicationsThe article can help to facilitate the debate on the role of implementing new technologies in libraries.Originality/valueThe problem of augmented intelligence is very rarely addressed in the subject literature in the field of library and information science.
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