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Sorantin E, Grasser MG, Hemmelmayr A, Heinze S. Let us talk about mistakes. Pediatr Radiol 2024:10.1007/s00247-024-06034-z. [PMID: 39210092 DOI: 10.1007/s00247-024-06034-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 08/11/2024] [Accepted: 08/12/2024] [Indexed: 09/04/2024]
Abstract
Unfortunately, errors and mistakes are part of life. Errors and mistakes can harm patients and incur unplanned costs. Errors may arise from various sources, which may be classified as systematic, latent, or active. Intrinsic and extrinsic factors also contribute to incorrect decisions. In addition to cognitive biases, our personality, socialization, personal chronobiology, and way of thinking (heuristic versus analytical) are influencing factors. Factors such as overload from private situations, long commuting times, and the complex environment of information technology must also be considered. The objective of this paper is to define and classify errors and mistakes in radiology, to discuss the influencing factors, and to present strategies for prevention. Hierarchical responsibilities and team "well-being" are also discussed.
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Affiliation(s)
- Erich Sorantin
- Division of Pediatric Radiology, Department of Radiology, Medical University Graz, Auenbruggerplatz 34, 8036, Graz, Austria.
| | - Michael Georg Grasser
- Division of Pediatric Radiology, Department of Radiology, Medical University Graz, Auenbruggerplatz 34, 8036, Graz, Austria
| | - Ariane Hemmelmayr
- Division of Pediatric Radiology, Department of Radiology, Medical University Graz, Auenbruggerplatz 34, 8036, Graz, Austria
| | - Sarah Heinze
- Diagnostic and Research Institute of Forensic Medicine, Medical University Graz, Neue Stiftingtalstrasse 6, 8010, Graz, Austria
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2
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Raissaki M, Stafrace S, Kozana A, Nievelstein RAJ, Papaioannou G. Collaborating with non-radiological clinical colleagues. Pediatr Radiol 2024:10.1007/s00247-024-06027-y. [PMID: 39168913 DOI: 10.1007/s00247-024-06027-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 08/05/2024] [Accepted: 08/05/2024] [Indexed: 08/23/2024]
Abstract
Paediatric radiology is a challenging and intriguing subspecialty, dealing with children, guardians and non-radiological clinical colleagues. Paediatric radiologists are routinely in contact with numerous paediatric and surgical subspecialties, all having different needs, perceptions, prioritisations and expectations. Moreover, the radiologist is part of the team of radiographers, sonographers, nurses and secretaries, assisted by appropriate equipment and electronic tools. The framework of good collaboration to ensure safety and effectiveness for the imaged child is a shared responsibility among all medical practitioners involved. Communication in routine practice has many forms and includes appropriately filled radiology requests in accordance to the patient's medical records, routine and timely production of structured, problem-solving radiology reports, face-to-face or electronic-assisted communications and discussions on a pre-defined framework, mutually-agreed and evidence-based protocols adjusted to local availability, skills and national and international guidelines. Mutual understanding of advantages and limitations of imaging is paramount. Well-meant discussions, professionalism and empathy should promote soft skills, bidirectional communication and good collaboration for the benefit of added-value paediatric radiology. International societies, health authorities, medical directors and senior consultants have the responsibility to suggest and safeguard frameworks and recommendations. Regular multidisciplinary meetings and multidisciplinary research projects under openness, honesty and transparency are pathways favouring good collaboration.
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Affiliation(s)
- Maria Raissaki
- Department of Radiology, University Hospital of Heraklion, University of Crete, Stavrakia Medical School Campus, 71110, Heraklion, Crete, Greece.
| | - Samuel Stafrace
- Department of Radiology, McMaster Children's Hospital, McMaster University, Hamilton, Ontario, Canada
| | - Androniki Kozana
- Department of Radiology, University Hospital of Heraklion, Heraklion, Crete, Greece
| | - Rutger A J Nievelstein
- Division Imaging & Oncology, Department of Radiology & Nuclear Medicine, UMC Utrecht/Wilhelmina Children's Hospital, Utrecht, The Netherlands
| | - Georgia Papaioannou
- Department of Pediatric Radiology, Mitera Maternal and Children's Hospital, Athens, Greece
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3
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Mikulić M, Vičević D, Nagy E, Napravnik M, Štajduhar I, Tschauner S, Hržić F. Balancing Performance and Interpretability in Medical Image Analysis: Case study of Osteopenia. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01194-8. [PMID: 39020155 DOI: 10.1007/s10278-024-01194-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 06/09/2024] [Accepted: 06/25/2024] [Indexed: 07/19/2024]
Abstract
Multiple studies within the medical field have highlighted the remarkable effectiveness of using convolutional neural networks for predicting medical conditions, sometimes even surpassing that of medical professionals. Despite their great performance, convolutional neural networks operate as black boxes, potentially arriving at correct conclusions for incorrect reasons or areas of focus. Our work explores the possibility of mitigating this phenomenon by identifying and occluding confounding variables within images. Specifically, we focused on the prediction of osteopenia, a serious medical condition, using the publicly available GRAZPEDWRI-DX dataset. After detection of the confounding variables in the dataset, we generated masks that occlude regions of images associated with those variables. By doing so, models were forced to focus on different parts of the images for classification. Model evaluation using F1-score, precision, and recall showed that models trained on non-occluded images typically outperformed models trained on occluded images. However, a test where radiologists had to choose a model based on the focused regions extracted by the GRAD-CAM method showcased different outcomes. The radiologists' preference shifted towards models trained on the occluded images. These results suggest that while occluding confounding variables may degrade model performance, it enhances interpretability, providing more reliable insights into the reasoning behind predictions. The code to repeat our experiment is available on the following link: https://github.com/mikulicmateo/osteopenia .
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Affiliation(s)
- Mateo Mikulić
- University of Rijeka, Faculty of Engineering, Department of Computer Engineering, Vukovarska 58, Rijeka, 51000, Croatia
| | - Dominik Vičević
- University of Rijeka, Faculty of Engineering, Department of Computer Engineering, Vukovarska 58, Rijeka, 51000, Croatia
| | - Eszter Nagy
- Medical University of Graz, Department of Radiology, Division of Pediatric Radiology, Graz, 8036, Austria
| | - Mateja Napravnik
- University of Rijeka, Faculty of Engineering, Department of Computer Engineering, Vukovarska 58, Rijeka, 51000, Croatia
| | - Ivan Štajduhar
- University of Rijeka, Faculty of Engineering, Department of Computer Engineering, Vukovarska 58, Rijeka, 51000, Croatia
- University of Rijeka, Center for Artificial Intelligence and Cybersecurity, Radmile Matejčić 2, Rijeka, 51000, Croatia
| | - Sebastian Tschauner
- Medical University of Graz, Department of Radiology, Division of Pediatric Radiology, Graz, 8036, Austria
| | - Franko Hržić
- University of Rijeka, Faculty of Engineering, Department of Computer Engineering, Vukovarska 58, Rijeka, 51000, Croatia.
- University of Rijeka, Center for Artificial Intelligence and Cybersecurity, Radmile Matejčić 2, Rijeka, 51000, Croatia.
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Kazimierczak W, Jedliński M, Issa J, Kazimierczak N, Janiszewska-Olszowska J, Dyszkiewicz-Konwińska M, Różyło-Kalinowska I, Serafin Z, Orhan K. Accuracy of Artificial Intelligence for Cervical Vertebral Maturation Assessment-A Systematic Review. J Clin Med 2024; 13:4047. [PMID: 39064087 PMCID: PMC11277636 DOI: 10.3390/jcm13144047] [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: 06/04/2024] [Revised: 07/03/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
Background/Objectives: To systematically review and summarize the existing scientific evidence on the diagnostic performance of artificial intelligence (AI) in assessing cervical vertebral maturation (CVM). This review aimed to evaluate the accuracy and reliability of AI algorithms in comparison to those of experienced clinicians. Methods: Comprehensive searches were conducted across multiple databases, including PubMed, Scopus, Web of Science, and Embase, using a combination of Boolean operators and MeSH terms. The inclusion criteria were cross-sectional studies with neural network research, reporting diagnostic accuracy, and involving human subjects. Data extraction and quality assessment were performed independently by two reviewers, with a third reviewer resolving any disagreements. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool was used for bias assessment. Results: Eighteen studies met the inclusion criteria, predominantly employing supervised learning techniques, especially convolutional neural networks (CNNs). The diagnostic accuracy of AI models for CVM assessment varied widely, ranging from 57% to 95%. The factors influencing accuracy included the type of AI model, training data, and study methods. Geographic concentration and variability in the experience of radiograph readers also impacted the results. Conclusions: AI has considerable potential for enhancing the accuracy and reliability of CVM assessments in orthodontics. However, the variability in AI performance and the limited number of high-quality studies suggest the need for further research.
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Affiliation(s)
- Wojciech Kazimierczak
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Maciej Jedliński
- Department of Interdisciplinary Dentistry, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| | - Julien Issa
- Chair of Practical Clinical Dentistry, Department of Diagnostics, Poznań University of Medical Sciences, 61-701 Poznań, Poland
| | - Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | | | - Marta Dyszkiewicz-Konwińska
- Chair of Practical Clinical Dentistry, Department of Diagnostics, Poznań University of Medical Sciences, 61-701 Poznań, Poland
| | - Ingrid Różyło-Kalinowska
- Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, 20-093 Lublin, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Kaan Orhan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06500, Turkey
- Medical Design Application and Research Center (MEDITAM), Ankara University, Ankara 06500, Turkey
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, 1088 Budapest, Hungary
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Potočnik J, Thomas E, Lawlor A, Kearney D, Heffernan EJ, Killeen RP, Foley SJ. Machine learning and deep learning for classifying the justification of brain CT referrals. Eur Radiol 2024:10.1007/s00330-024-10851-z. [PMID: 38913244 DOI: 10.1007/s00330-024-10851-z] [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: 01/25/2024] [Revised: 05/02/2024] [Accepted: 06/03/2024] [Indexed: 06/25/2024]
Abstract
OBJECTIVES To train the machine and deep learning models to automate the justification analysis of radiology referrals in accordance with iGuide categorisation, and to determine if prediction models can generalise across multiple clinical sites and outperform human experts. METHODS Adult brain computed tomography (CT) referrals from scans performed in three CT centres in Ireland in 2020 and 2021 were retrospectively collected. Two radiographers analysed the justification of 3000 randomly selected referrals using iGuide, with two consultant radiologists analysing the referrals with disagreement. Insufficient or duplicate referrals were discarded. The inter-rater agreement among radiographers and consultants was computed. A random split (4:1) was performed to apply machine learning (ML) and deep learning (DL) techniques to unstructured clinical indications to automate retrospective justification auditing with multi-class classification. The accuracy and macro-averaged F1 score of the best-performing classifier of each type on the training set were computed on the test set. RESULTS 42 referrals were ignored. 1909 (64.5%) referrals were justified, 811 (27.4%) were potentially justified, and 238 (8.1%) were unjustified. The agreement between radiographers (κ = 0.268) was lower than radiologists (κ = 0.460). The best-performing ML model was the bag-of-words-based gradient-boosting classifier achieving a 94.4% accuracy and a macro F1 of 0.94. DL models were inferior, with bi-directional long short-term memory achieving 92.3% accuracy, a macro F1 of 0.92, and outperforming multilayer perceptrons. CONCLUSION Interpreting unstructured clinical indications is challenging necessitating clinical decision support. ML and DL can generalise across multiple clinical sites, outperform human experts, and be used as an artificial intelligence-based iGuide interpreter when retrospectively vetting radiology referrals. CLINICAL RELEVANCE STATEMENT Healthcare vendors and clinical sites should consider developing and utilising artificial intelligence-enabled systems for justifying medical exposures. This would enable better implementation of imaging referral guidelines in clinical practices and reduce population dose burden, CT waiting lists, and wasteful use of resources. KEY POINTS Significant variations exist among human experts in interpreting unstructured clinical indications/patient presentations. Machine and deep learning can automate the justification analysis of radiology referrals according to iGuide categorisation. Machine and deep learning can improve retrospective and prospective justification auditing for better implementation of imaging referral guidelines.
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Affiliation(s)
- Jaka Potočnik
- University College Dublin School of Medicine, Dublin, Ireland.
| | - Edel Thomas
- University College Dublin School of Medicine, Dublin, Ireland.
| | - Aonghus Lawlor
- University College Dublin School of Computer Science, Dublin, Ireland.
- The Insight Centre for Data Analytics, Dublin, Ireland.
| | | | | | - Ronan P Killeen
- University College Dublin School of Medicine, Dublin, Ireland.
- St. Vincent's University Hospital, Dublin, Ireland.
- Royal Victoria Eye and Ear Hospital, Dublin, Ireland.
| | - Shane J Foley
- University College Dublin School of Medicine, Dublin, Ireland.
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6
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Kalanjiyam GP, Chandramohan T, Raman M, Kalyanasundaram H. Artificial intelligence: a new cutting-edge tool in spine surgery. Asian Spine J 2024; 18:458-471. [PMID: 38917854 PMCID: PMC11222879 DOI: 10.31616/asj.2023.0382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/07/2024] [Accepted: 01/11/2024] [Indexed: 06/27/2024] Open
Abstract
The purpose of this narrative review was to comprehensively elaborate the various components of artificial intelligence (AI), their applications in spine surgery, practical concerns, and future directions. Over the years, spine surgery has been continuously transformed in various aspects, including diagnostic strategies, surgical approaches, procedures, and instrumentation, to provide better-quality patient care. Surgeons have also augmented their surgical expertise with rapidly growing technological advancements. AI is an advancing field that has the potential to revolutionize many aspects of spine surgery. We performed a comprehensive narrative review of the various aspects of AI and machine learning in spine surgery. To elaborate on the current role of AI in spine surgery, a review of the literature was performed using PubMed and Google Scholar databases for articles published in English in the last 20 years. The initial search using the keywords "artificial intelligence" AND "spine," "machine learning" AND "spine," and "deep learning" AND "spine" extracted a total of 78, 60, and 37 articles and 11,500, 4,610, and 2,270 articles on PubMed and Google Scholar. After the initial screening and exclusion of unrelated articles, duplicates, and non-English articles, 405 articles were identified. After the second stage of screening, 93 articles were included in the review. Studies have shown that AI can be used to analyze patient data and provide personalized treatment recommendations in spine care. It also provides valuable insights for planning surgeries and assisting with precise surgical maneuvers and decisionmaking during the procedures. As more data become available and with further advancements, AI is likely to improve patient outcomes.
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Affiliation(s)
- Guna Pratheep Kalanjiyam
- Spine Surgery Unit, Department of Orthopaedics, Meenakshi Mission Hospital and Research Centre, Madurai,
India
| | - Thiyagarajan Chandramohan
- Department of Orthopaedics, Government Stanley Medical College, Chennai,
India
- Department of Emergency Medicine, Government Stanley Medical College, Chennai,
India
| | - Muthu Raman
- Department of Orthopaedics, Tenkasi Government Hospital, Tenkasi,
India
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7
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Bindra S, Jain R. Artificial intelligence in medical science: a review. Ir J Med Sci 2024; 193:1419-1429. [PMID: 37952245 DOI: 10.1007/s11845-023-03570-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/01/2023] [Indexed: 11/14/2023]
Abstract
Artificial intelligence (AI) is a technique to make intelligent machines, mainly by using smart computer programs. It is based on a statistical analysis of data or machine learning. Using machine learning, software algorithms are designed according to the desired application. These techniques are found to have the potential for advancement in the medical field by generating new and significant perceptions from the data generated using various types of healthcare tests. Artificial intelligence (AI) in medicine is of two types: virtual and physical. The virtual part decides the treatment using electronic health record systems using various sensors whereas the physical part assists robots to perform surgeries, implants, replacement of various organs, elderly care, etc. Using AI, a machine can examine various kinds of health care test reports in one go which could save the time, money, and increase the chances of the patient to be treated without any hassles. At present, artificial intelligence (AI) is used while deciding the treatment, and medications using various tools which could analyze X-rays, CT scans, MRIs, and any other data. During the COVID pandemic, there was a huge/massive demand for AI-supported technologies and many of those were created during that time. This study is focused on various applications of AI in healthcare.
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Affiliation(s)
- Simrata Bindra
- Department of Physics, Motilal Nehru College, Benito Juarez Road, New Delhi, 110021, India
| | - Richa Jain
- Department of Physics, Motilal Nehru College, Benito Juarez Road, New Delhi, 110021, India.
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8
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Rathmann E, Hemkemeier P, Raths S, Grothe M, Mankertz F, Hosten N, Flessa S. Changes in MRI Workflow of Multiple Sclerosis after Introduction of an AI-Software: A Qualitative Study. Healthcare (Basel) 2024; 12:978. [PMID: 38786390 PMCID: PMC11121325 DOI: 10.3390/healthcare12100978] [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: 03/25/2024] [Revised: 04/30/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
The purpose of this study was to explore the effects of the integration of machine learning into daily radiological diagnostics, using the example of the machine learning software mdbrain® (Mediaire GmbH, Germany) in the diagnostic MRI workflow of patients with multiple sclerosis at the University Medicine Greifswald. The data were assessed through expert interviews, a comparison of analysis times with and without the machine learning software, as well as a process analysis of MRI workflows. Our results indicate a reduction in the screen-reading workload, improved decision-making regarding contrast administration, an optimized workflow, reduced examination times, and facilitated report communication with colleagues and patients. Our results call for a broader and quantitative analysis.
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Affiliation(s)
- Eiko Rathmann
- Institute of Radiology and Neuroradiology, University Medicine Greifswald, 17475 Greifswald, Germany or (N.H.)
| | - Pia Hemkemeier
- Department of Business Administration and Health Care Management, Faculty of Law and Economics, University of Greifswald, 17489 Greifswald, Germany (S.F.)
| | - Susan Raths
- Department of Business Administration and Health Care Management, Faculty of Law and Economics, University of Greifswald, 17489 Greifswald, Germany (S.F.)
| | - Matthias Grothe
- Department of Neurology, University Medicine Greifswald, 17475 Greifswald, Germany;
| | - Fiona Mankertz
- Institute of Radiology and Neuroradiology, University Medicine Greifswald, 17475 Greifswald, Germany or (N.H.)
| | - Norbert Hosten
- Institute of Radiology and Neuroradiology, University Medicine Greifswald, 17475 Greifswald, Germany or (N.H.)
| | - Steffen Flessa
- Department of Business Administration and Health Care Management, Faculty of Law and Economics, University of Greifswald, 17489 Greifswald, Germany (S.F.)
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9
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Yao J, Chu LC, Patlas M. Applications of Artificial Intelligence in Acute Abdominal Imaging. Can Assoc Radiol J 2024:8465371241250197. [PMID: 38715249 DOI: 10.1177/08465371241250197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
Artificial intelligence (AI) is a rapidly growing field with significant implications for radiology. Acute abdominal pain is a common clinical presentation that can range from benign conditions to life-threatening emergencies. The critical nature of these situations renders emergent abdominal imaging an ideal candidate for AI applications. CT, radiographs, and ultrasound are the most common modalities for imaging evaluation of these patients. For each modality, numerous studies have assessed the performance of AI models for detecting common pathologies, such as appendicitis, bowel obstruction, and cholecystitis. The capabilities of these models range from simple classification to detailed severity assessment. This narrative review explores the evolution, trends, and challenges in AI applications for evaluating acute abdominal pathologies. We review implementations of AI for non-traumatic and traumatic abdominal pathologies, with discussion of potential clinical impact, challenges, and future directions for the technology.
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Affiliation(s)
- Jason Yao
- Department of Radiology, McMaster University, Hamilton, ON, Canada
| | - Linda C Chu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael Patlas
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
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10
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Farkhani S, Demnitz N, Boraxbekk CJ, Lundell H, Siebner HR, Petersen ET, Madsen KH. End-to-end volumetric segmentation of white matter hyperintensities using deep learning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 245:108008. [PMID: 38290291 DOI: 10.1016/j.cmpb.2024.108008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 12/08/2023] [Accepted: 01/03/2024] [Indexed: 02/01/2024]
Abstract
BACKGROUND AND OBJECTIVES Reliable detection of white matter hyperintensities (WMH) is crucial for studying the impact of diffuse white-matter pathology on brain health and monitoring changes in WMH load over time. However, manual annotation of 3D high-dimensional neuroimages is laborious and can be prone to biases and errors in the annotation procedure. In this study, we evaluate the performance of deep learning (DL) segmentation tools and propose a novel volumetric segmentation model incorporating self-attention via a transformer-based architecture. Ultimately, we aim to evaluate diverse factors that influence WMH segmentation, aiming for a comprehensive analysis of the state-of-the-art algorithms in a broader context. METHODS We trained state-of-the-art DL algorithms, and incorporated advanced attention mechanisms, using structural fluid-attenuated inversion recovery (FLAIR) image acquisitions. The anatomical MRI data utilized for model training was obtained from healthy individuals aged 62-70 years in the Live active Successful Aging (LISA) project. Given the potential sparsity of lesion volume among healthy aging individuals, we explored the impact of incorporating a weighted loss function and ensemble models. To assess the generalizability of the studied DL models, we applied the trained algorithm to an independent subset of data sourced from the MICCAI WMH challenge (MWSC). Notably, this subset had vastly different acquisition parameters compared to the LISA dataset used for training. RESULTS Consistently, DL approaches exhibited commendable segmentation performance, achieving the level of inter-rater agreement comparable to expert performance, ensuring superior quality segmentation outcomes. On the out of sample dataset, the ensemble models exhibited the most outstanding performance. CONCLUSIONS DL methods generally surpassed conventional approaches in our study. While all DL methods performed comparably, incorporating attention mechanisms could prove advantageous in future applications with a wider availability of training data. As expected, our experiments indicate that the use of ensemble-based models enables the superior generalization in out-of-distribution settings. We believe that introducing DL methods in the WHM annotation workflow in heathy aging cohorts is promising, not only for reducing the annotation time required, but also for eventually improving accuracy and robustness via incorporating the automatic segmentations in the evaluation procedure.
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Affiliation(s)
- Sadaf Farkhani
- Danish Research Center for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Kattegaard Alle 30, Hvidovre, Denmark.
| | - Naiara Demnitz
- Danish Research Center for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Kattegaard Alle 30, Hvidovre, Denmark
| | - Carl-Johan Boraxbekk
- Danish Research Center for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Kattegaard Alle 30, Hvidovre, Denmark; Institute for Clinical Medicine, Faculty of Medical and Health Sciences, University of Copenhagen, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark; Institute of Sports Medicine Copenhagen (ISMC), Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Henrik Lundell
- Danish Research Center for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Kattegaard Alle 30, Hvidovre, Denmark; Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Hartwig Roman Siebner
- Danish Research Center for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Kattegaard Alle 30, Hvidovre, Denmark; Institute for Clinical Medicine, Faculty of Medical and Health Sciences, University of Copenhagen, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Esben Thade Petersen
- Danish Research Center for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Kattegaard Alle 30, Hvidovre, Denmark; Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Kristoffer Hougaard Madsen
- Danish Research Center for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Kattegaard Alle 30, Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
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11
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Daher OA, Dabbousi AA, Chamroukh R, Saab AY, Al Ayoubi AR, Salameh P. Artificial Intelligence: Knowledge and Attitude Among Lebanese Medical Students. Cureus 2024; 16:e51466. [PMID: 38298326 PMCID: PMC10829838 DOI: 10.7759/cureus.51466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/01/2024] [Indexed: 02/02/2024] Open
Abstract
Background Artificial intelligence (AI) has taken on a variety of functions in the medical field, and research has proven that it can address complicated issues in various applications. It is unknown whether Lebanese medical students and residents have a detailed understanding of this concept, and little is known about their attitudes toward AI. Aim This study fills a critical gap by revealing the knowledge and attitude of Lebanese medical students toward AI. Methods A multi-centric survey targeting 365 medical students from seven medical schools across Lebanon was conducted to assess their knowledge of and attitudes toward AI in medicine. The survey consists of five sections: the first part includes socio-demographic variables, while the second comprises the 'Medical Artificial Intelligence Readiness Scale' for medical students. The third part focuses on attitudes toward AI in medicine, the fourth assesses understanding of deep learning, and the fifth targets considerations of radiology as a specialization. Results There is a notable awareness of AI among students who are eager to learn about it. Despite this interest, there exists a gap in knowledge regarding deep learning, albeit alongside a positive attitude towards it. Students who are more open to embracing AI technology tend to have a better understanding of AI concepts (p=0.001). Additionally, a higher percentage of students from Mount Lebanon (71.6%) showed an inclination towards using AI compared to Beirut (63.2%) (p=0.03). Noteworthy are the Lebanese University and Saint Joseph University, where the highest proportions of students are willing to integrate AI into the medical field (79.4% and 76.7%, respectively; p=0.001). Conclusion It was concluded that most Lebanese medical students might not necessarily comprehend the core technological ideas of AI and deep learning. This lack of understanding was evident from the substantial amount of misinformation among the students. Consequently, there appears to be a significant demand for the inclusion of AI technologies in Lebanese medical school courses.
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Affiliation(s)
- Omar A Daher
- Faculty of Medicine, Beirut Arab University, Beirut, LBN
| | | | | | | | - Amir Rabih Al Ayoubi
- Department of General Medicine, Faculty of Medical Sciences, Lebanese University, Beirut, LBN
| | - Pascale Salameh
- Department of Primary Care and Population Health, University of Nicosia Medical School, Nicosia, CYP
- Department of Public Health, Institut National de Santé Publique, d'Épidémiologie Clinique et de Toxicologie (INSPECT-LB), Beirut, LBN
- Department of Pharmacy Practice, Lebanese University, Beirut, LBN
- School of Medicine, Lebanese American University, Beirut, LBN
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12
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Yoon J, Han J, Ko J, Choi S, Park JI, Hwang JS, Han JM, Hwang DDJ. Developing and Evaluating an AI-Based Computer-Aided Diagnosis System for Retinal Disease: Diagnostic Study for Central Serous Chorioretinopathy. J Med Internet Res 2023; 25:e48142. [PMID: 38019564 PMCID: PMC10719821 DOI: 10.2196/48142] [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: 04/14/2023] [Revised: 10/29/2023] [Accepted: 11/05/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND Although previous research has made substantial progress in developing high-performance artificial intelligence (AI)-based computer-aided diagnosis (AI-CAD) systems in various medical domains, little attention has been paid to developing and evaluating AI-CAD system in ophthalmology, particularly for diagnosing retinal diseases using optical coherence tomography (OCT) images. OBJECTIVE This diagnostic study aimed to determine the usefulness of a proposed AI-CAD system in assisting ophthalmologists with the diagnosis of central serous chorioretinopathy (CSC), which is known to be difficult to diagnose, using OCT images. METHODS For the training and evaluation of the proposed deep learning model, 1693 OCT images were collected and annotated. The data set included 929 and 764 cases of acute and chronic CSC, respectively. In total, 66 ophthalmologists (2 groups: 36 retina and 30 nonretina specialists) participated in the observer performance test. To evaluate the deep learning algorithm used in the proposed AI-CAD system, the training, validation, and test sets were split in an 8:1:1 ratio. Further, 100 randomly sampled OCT images from the test set were used for the observer performance test, and the participants were instructed to select a CSC subtype for each of these images. Each image was provided under different conditions: (1) without AI assistance, (2) with AI assistance with a probability score, and (3) with AI assistance with a probability score and visual evidence heatmap. The sensitivity, specificity, and area under the receiver operating characteristic curve were used to measure the diagnostic performance of the model and ophthalmologists. RESULTS The proposed system achieved a high detection performance (99% of the area under the curve) for CSC, outperforming the 66 ophthalmologists who participated in the observer performance test. In both groups, ophthalmologists with the support of AI assistance with a probability score and visual evidence heatmap achieved the highest mean diagnostic performance compared with that of those subjected to other conditions (without AI assistance or with AI assistance with a probability score). Nonretina specialists achieved expert-level diagnostic performance with the support of the proposed AI-CAD system. CONCLUSIONS Our proposed AI-CAD system improved the diagnosis of CSC by ophthalmologists, which may support decision-making regarding retinal disease detection and alleviate the workload of ophthalmologists.
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Affiliation(s)
- Jeewoo Yoon
- Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea
- Raondata, Seoul, Republic of Korea
| | - Jinyoung Han
- Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University, Seoul, Republic of Korea
| | - Junseo Ko
- Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea
- Raondata, Seoul, Republic of Korea
| | - Seong Choi
- Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea
- Raondata, Seoul, Republic of Korea
| | - Ji In Park
- Department of Medicine, Kangwon National University School of Medicine, Kangwon National University Hospital, Chuncheon, Republic of Korea
| | | | - Jeong Mo Han
- Seoul Bombit Eye Clinic, Sejong, Republic of Korea
| | - Daniel Duck-Jin Hwang
- Department of Ophthalmology, Hangil Eye Hospital, Incheon, Republic of Korea
- Lux Mind, Incheon, Republic of Korea
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13
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Bradley D, Harrison J, Goodall M, Dobrashian R. Are Advanced Clinical Practitioners perfectly placed to re-report neuroimages to support clinical diagnosis of dementia? INTERNATIONAL JOURNAL FOR ADVANCING PRACTICE 2023; 1:146-150. [PMID: 38229770 PMCID: PMC7615529 DOI: 10.12968/ijap.2023.1.3.146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Abstract
With the ageing population, the prevalence of dementia is increasing worldwide. There is an emphasis on early, timely diagnosis and treatment options for people with a dementia yet wait times from referral to diagnosis have increased. Neuroimaging performed by radiologists is utilised to support dementia diagnosis and some patients will already have a CT scan from a pre-existing condition such as stroke. The purpose of this commentary is to consider whether ACPs who specialise in dementia, are perfectly placed to re-report on pre-existing neuroimages to support the clinical diagnosis of dementia.
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Affiliation(s)
| | - Joanna Harrison
- Synthesis Economic Evaluation and Decision Science (SEEDS) Group, University of Central Lancashire
| | - Mark Goodall
- Institute of Population Health, University of Liverpool
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14
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Davis MA, Lim N, Jordan J, Yee J, Gichoya JW, Lee R. Imaging Artificial Intelligence: A Framework for Radiologists to Address Health Equity, From the AJR Special Series on DEI. AJR Am J Roentgenol 2023; 221:302-308. [PMID: 37095660 DOI: 10.2214/ajr.22.28802] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Artificial intelligence (AI) holds promise for helping patients access new and individualized health care pathways while increasing efficiencies for health care practitioners. Radiology has been at the forefront of this technology in medicine; many radiology practices are implementing and trialing AI-focused products. AI also holds great promise for reducing health disparities and promoting health equity. Radiology is ideally positioned to help reduce disparities given its central and critical role in patient care. The purposes of this article are to discuss the potential benefits and pitfalls of deploying AI algorithms in radiology, specifically highlighting the impact of AI on health equity; to explore ways to mitigate drivers of inequity; and to enhance pathways for creating better health care for all individuals, centering on a practical framework that helps radiologists address health equity during deployment of new tools.
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Affiliation(s)
- Melissa A Davis
- Department of Diagnostic Radiology, Yale University School of Medicine, 789 Howard Ave, PO Box 20842, New Haven, CT 06520
| | | | - John Jordan
- Stanford University School of Medicine, Stanford, CA
| | - Judy Yee
- Montefiore Medical Center, Albert Einstein College of Medicine, New York, NY
| | | | - Ryan Lee
- Jefferson Health, Philadelphia, PA
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15
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Tierradentro-Garcia LO, Sotardi ST, Sammer MBK, Otero HJ. Commercially Available Artificial Intelligence Algorithms of Interest to Pediatric Radiology: The Growing Gap Between Potential Use and Data Training. J Am Coll Radiol 2023; 20:748-751. [PMID: 37390885 DOI: 10.1016/j.jacr.2023.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 03/22/2023] [Accepted: 04/06/2023] [Indexed: 07/02/2023]
Affiliation(s)
| | - Susan T Sotardi
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Marla B K Sammer
- Department of Radiology, Texas Children's Hospital, Baylor College of Medicine, and is Chair, ACR Pediatric AI Workgroup
| | - Hansel J Otero
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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16
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Tan WM, Ng WL, Ganggayah MD, Hoe VCW, Rahmat K, Zaini HS, Mohd Taib NA, Dhillon SK. Natural language processing in narrative breast radiology reporting in University Malaya Medical Centre. Health Informatics J 2023; 29:14604582231203763. [PMID: 37740904 DOI: 10.1177/14604582231203763] [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] [Indexed: 09/25/2023]
Abstract
Radiology reporting is narrative, and its content depends on the clinician's ability to interpret the images accurately. A tertiary hospital, such as anonymous institute, focuses on writing reports narratively as part of training for medical personnel. Nevertheless, free-text reports make it inconvenient to extract information for clinical audits and data mining. Therefore, we aim to convert unstructured breast radiology reports into structured formats using natural language processing (NLP) algorithm. This study used 327 de-identified breast radiology reports from the anonymous institute. The radiologist identified the significant data elements to be extracted. Our NLP algorithm achieved 97% and 94.9% accuracy in training and testing data, respectively. Henceforth, the structured information was used to build the predictive model for predicting the value of the BIRADS category. The model based on random forest generated the highest accuracy of 92%. Our study not only fulfilled the demands of clinicians by enhancing communication between medical personnel, but it also demonstrated the usefulness of mineable structured data in yielding significant insights.
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Affiliation(s)
- Wee Ming Tan
- Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Wei Lin Ng
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Mogana Darshini Ganggayah
- Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Victor Chee Wai Hoe
- Department of Social and Preventive Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Kartini Rahmat
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Hana Salwani Zaini
- Department of Information Technology, University of Malaya Medical Centre, Kuala Lumpur, Malaysia
| | - Nur Aishah Mohd Taib
- Department of Surgery, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Sarinder Kaur Dhillon
- Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
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17
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Pershin I, Mustafaev T, Ibragimova D, Ibragimov B. Changes in Radiologists' Gaze Patterns Against Lung X-rays with Different Abnormalities: a Randomized Experiment. J Digit Imaging 2023; 36:767-775. [PMID: 36622464 PMCID: PMC9838425 DOI: 10.1007/s10278-022-00760-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/23/2022] [Accepted: 12/15/2022] [Indexed: 01/10/2023] Open
Abstract
The workload of some radiologists increased dramatically in the last several, which resulted in a potentially reduced quality of diagnosis. It was demonstrated that diagnostic accuracy of radiologists significantly reduces at the end of work shifts. The study aims to investigate how radiologists cover chest X-rays with their gaze in the presence of different chest abnormalities and high workload. We designed a randomized experiment to quantitatively assess how radiologists' image reading patterns change with the radiological workload. Four radiologists read chest X-rays on a radiological workstation equipped with an eye-tracker. The lung fields on the X-rays were automatically segmented with U-Net neural network allowing to measure the lung coverage with radiologists' gaze. The images were randomly split so that each image was shown at a different time to a different radiologist. Regression models were fit to the gaze data to calculate the treads in lung coverage for individual radiologists and chest abnormalities. For the study, a database of 400 chest X-rays with reference diagnoses was assembled. The average lung coverage with gaze ranged from 55 to 65% per radiologist. For every 100 X-rays read, the lung coverage reduced from 1.3 to 7.6% for the different radiologists. The coverage reduction trends were consistent for all abnormalities ranging from 3.4% per 100 X-rays for cardiomegaly to 4.1% per 100 X-rays for atelectasis. The more image radiologists read, the smaller part of the lung fields they cover with the gaze. This pattern is very stable for all abnormality types and is not affected by the exact order the abnormalities are viewed by radiologists. The proposed randomized experiment captured and quantified consistent changes in X-ray reading for different lung abnormalities that occur due to high workload.
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Affiliation(s)
- Ilya Pershin
- Innopolis University, Republic of Tatarstan, Innopolis, Russia
| | - Tamerlan Mustafaev
- Innopolis University, Republic of Tatarstan, Innopolis, Russia
- Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
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18
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Sharma G, Enriquez JS, Armijo R, Wang M, Bhattacharya P, Pudakalakatti S. Enhancing Cancer Diagnosis with Real-Time Feedback: Tumor Metabolism through Hyperpolarized 1- 13C Pyruvate MRSI. Metabolites 2023; 13:metabo13050606. [PMID: 37233647 DOI: 10.3390/metabo13050606] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/19/2023] [Accepted: 04/23/2023] [Indexed: 05/27/2023] Open
Abstract
This review article discusses the potential of hyperpolarized (HP) 13C magnetic resonance spectroscopic imaging (MRSI) as a noninvasive technique for identifying altered metabolism in various cancer types. Hyperpolarization significantly improves the signal-to-noise ratio for the identification of 13C-labeled metabolites, enabling dynamic and real-time imaging of the conversion of [1-13C] pyruvate to [1-13C] lactate and/or [1-13C] alanine. The technique has shown promise in identifying upregulated glycolysis in most cancers, as compared to normal cells, and detecting successful treatment responses at an earlier stage than multiparametric MRI in breast and prostate cancer patients. The review provides a concise overview of the applications of HP [1-13C] pyruvate MRSI in various cancer systems, highlighting its potential for use in preclinical and clinical investigations, precision medicine, and long-term studies of therapeutic response. The article also discusses emerging frontiers in the field, such as combining multiple metabolic imaging techniques with HP MRSI for a more comprehensive view of cancer metabolism, and leveraging artificial intelligence to develop real-time, actionable biomarkers for early detection, assessing aggressiveness, and interrogating the early efficacy of therapies.
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Affiliation(s)
- Gaurav Sharma
- Department of Cardiovascular & Thoracic Surgery, UT Southwestern Medical Center, Dallas, TX 75390, USA
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - José S Enriquez
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 75390, USA
- Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 75390, USA
| | - Ryan Armijo
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 75390, USA
| | - Muxin Wang
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 75390, USA
| | - Pratip Bhattacharya
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 75390, USA
- Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 75390, USA
| | - Shivanand Pudakalakatti
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 75390, USA
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19
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Pfeuffer N, Baum L, Stammer W, Abdel-Karim BM, Schramowski P, Bucher AM, Hügel C, Rohde G, Kersting K, Hinz O. Explanatory Interactive Machine Learning. BUSINESS & INFORMATION SYSTEMS ENGINEERING 2023. [PMCID: PMC10119840 DOI: 10.1007/s12599-023-00806-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 01/17/2023] [Indexed: 11/22/2023]
Abstract
The most promising standard machine learning methods can deliver highly accurate classification results, often outperforming standard white-box methods. However, it is hardly possible for humans to fully understand the rationale behind the black-box results, and thus, these powerful methods hamper the creation of new knowledge on the part of humans and the broader acceptance of this technology. Explainable Artificial Intelligence attempts to overcome this problem by making the results more interpretable, while Interactive Machine Learning integrates humans into the process of insight discovery. The paper builds on recent successes in combining these two cutting-edge technologies and proposes how Explanatory Interactive Machine Learning (XIL) is embedded in a generalizable Action Design Research (ADR) process – called XIL-ADR. This approach can be used to analyze data, inspect models, and iteratively improve them. The paper shows the application of this process using the diagnosis of viral pneumonia, e.g., Covid-19, as an illustrative example. By these means, the paper also illustrates how XIL-ADR can help identify shortcomings of standard machine learning projects, gain new insights on the part of the human user, and thereby can help to unlock the full potential of AI-based systems for organizations and research.
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Affiliation(s)
- Nicolas Pfeuffer
- Information Systems and Information Management, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Lorenz Baum
- Information Systems and Information Management, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Wolfgang Stammer
- Machine Learning Group, Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany
| | - Benjamin M. Abdel-Karim
- Information Systems and Information Management, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Patrick Schramowski
- Machine Learning Group, Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany
| | - Andreas M. Bucher
- Diagnostic and Interventional Radiology, Center of Radiology, Hospital of the Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Christian Hügel
- Pneumology and Allergology, Center of Internal Medicine, Hospital of the Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Gernot Rohde
- Pneumology and Allergology, Center of Internal Medicine, Hospital of the Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Kristian Kersting
- Machine Learning Group, Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany
| | - Oliver Hinz
- Information Systems and Information Management, Goethe University Frankfurt, Frankfurt am Main, Germany
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20
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Hochhegger B, Pasini R, Roncally Carvalho A, Rodrigues R, Altmayer S, Kayat Bittencourt L, Marchiori E, Forghani R. Artificial Intelligence for Cardiothoracic Imaging: Overview of Current and Emerging Applications. Semin Roentgenol 2023; 58:184-195. [PMID: 37087139 DOI: 10.1053/j.ro.2023.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 03/07/2023]
Abstract
Artificial intelligence algorithms can learn by assimilating information from large datasets in order to decipher complex associations, identify previously undiscovered pathophysiological states, and construct prediction models. There has been tremendous interest and increased incorporation of artificial intelligence into various industries, including healthcare. As a result, there has been an exponential rise in the number of research articles and industry participants producing models intended for a variety of applications in medical imaging, which can be challenging to navigate for radiologists. In thoracic imaging, multiple applications are being evaluated for chest radiography and computed tomography and include applications for lung nodule evaluation and cancer imaging, quantifying diffuse lung disorders, and cardiac imaging, to name a few. This review aims to provide an overview of current clinical AI models, focusing on the most common clinical applications of AI in cardiothoracic imaging.
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21
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Müller L, Kloeckner R, Mildenberger P, Pinto Dos Santos D. [Validation and implementation of artificial intelligence in radiology : Quo vadis in 2022?]. RADIOLOGIE (HEIDELBERG, GERMANY) 2022; 63:381-386. [PMID: 36510007 DOI: 10.1007/s00117-022-01097-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/17/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND The hype around artificial intelligence (AI) in radiology continues and the number of approved AI tools is growing steadily. Despite the great potential, integration into clinical routine in radiology remains limited. In addition, the large number of individual applications poses a challenge for clinical routine, as individual applications have to be selected for different questions and organ systems, which increases the complexity and time required. OBJECTIVES This review will discuss the current status of validation and implementation of AI tools in clinical routine, and identify possible approaches for an improved assessment of the generalizability of results of AI tools. MATERIALS AND METHODS A literature search in various literature and product databases as well as publications, position papers, and reports from various stakeholders was conducted for this review. RESULTS Scientific evidence and independent validation studies are available for only a few commercial AI tools and the generalizability of the results often remains questionable. CONCLUSIONS One challenge is the multitude of offerings for individual, specific application areas by a large number of manufacturers, making integration into the existing site-specific IT infrastructure more difficult. Furthermore, remuneration for the use of AI tools in clinical routine by health insurance companies in Germany is lacking. But in order for reimbursement to be granted, the clinical utility of new applications must first be proven. Such proof, however, is lacking for most applications.
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Affiliation(s)
- Lukas Müller
- Klinik und Poliklinik für Diagnostische und Interventionelle Radiologie, Universitätsmedizin Mainz, Langenbeckstr. 1, 55131, Mainz, Deutschland.
| | - Roman Kloeckner
- Institut für Interventionelle Radiologie, Universitätsklinikum Schleswig-Holstein - Campus Lübeck, Lübeck, Deutschland
| | - Peter Mildenberger
- Klinik und Poliklinik für Diagnostische und Interventionelle Radiologie, Universitätsmedizin Mainz, Langenbeckstr. 1, 55131, Mainz, Deutschland
| | - Daniel Pinto Dos Santos
- Institut für Diagnostische und Interventionelle Radiologie, Uniklinik Köln, Köln, Deutschland.,Institut für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Frankfurt, Frankfurt am Main, Deutschland
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22
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Khosravi B, Rouzrokh P, Faghani S, Moassefi M, Vahdati S, Mahmoudi E, Chalian H, Erickson BJ. Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review. Diagnostics (Basel) 2022; 12:2512. [PMID: 36292201 PMCID: PMC9600598 DOI: 10.3390/diagnostics12102512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/14/2022] [Accepted: 10/15/2022] [Indexed: 01/17/2023] Open
Abstract
Machine-learning (ML) and deep-learning (DL) algorithms are part of a group of modeling algorithms that grasp the hidden patterns in data based on a training process, enabling them to extract complex information from the input data. In the past decade, these algorithms have been increasingly used for image processing, specifically in the medical domain. Cardiothoracic imaging is one of the early adopters of ML/DL research, and the COVID-19 pandemic resulted in more research focus on the feasibility and applications of ML/DL in cardiothoracic imaging. In this scoping review, we systematically searched available peer-reviewed medical literature on cardiothoracic imaging and quantitatively extracted key data elements in order to get a big picture of how ML/DL have been used in the rapidly evolving cardiothoracic imaging field. During this report, we provide insights on different applications of ML/DL and some nuances pertaining to this specific field of research. Finally, we provide general suggestions on how researchers can make their research more than just a proof-of-concept and move toward clinical adoption.
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Affiliation(s)
- Bardia Khosravi
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Pouria Rouzrokh
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Shahriar Faghani
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Mana Moassefi
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Sanaz Vahdati
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Elham Mahmoudi
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Hamid Chalian
- Department of Radiology, Cardiothoracic Imaging, University of Washington, Seattle, WA 98195, USA
| | - Bradley J. Erickson
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
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23
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The natural language processing of radiology requests and reports of chest imaging: Comparing five transformer models’ multilabel classification and a proof-of-concept study. Health Informatics J 2022; 28:14604582221131198. [DOI: 10.1177/14604582221131198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Background Radiology requests and reports contain valuable information about diagnostic findings and indications, and transformer-based language models are promising for more accurate text classification. Methods In a retrospective study, 2256 radiologist-annotated radiology requests (8 classes) and reports (10 classes) were divided into training and testing datasets (90% and 10%, respectively) and used to train 32 models. Performance metrics were compared by model type (LSTM, Bertje, RobBERT, BERT-clinical, BERT-multilingual, BERT-base), text length, data prevalence, and training strategy. The best models were used to predict the remaining 40,873 cases’ categories of the datasets of requests and reports. Results The RobBERT model performed the best after 4000 training iterations, resulting in AUC values ranging from 0.808 [95% CI (0.757–0.859)] to 0.976 [95% CI (0.956–0.996)] for the requests and 0.746 [95% CI (0.689–0.802)] to 1.0 [95% CI (1.0–1.0)] for the reports. The AUC for the classification of normal reports was 0.95 [95% CI (0.922–0.979)]. The predicted data demonstrated variability of both diagnostic yield for various request classes and request patterns related to COVID-19 hospital admission data. Conclusion Transformer-based natural language processing is feasible for the multilabel classification of chest imaging request and report items. Diagnostic yield varies with the information in the requests.
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Yildirim E, Cicioğlu M, Çalhan A. Real-time internet of medical things framework for early detection of Covid-19. Neural Comput Appl 2022; 34:20365-20378. [PMID: 35912366 PMCID: PMC9308898 DOI: 10.1007/s00521-022-07582-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 06/28/2022] [Indexed: 01/19/2023]
Abstract
The Covid-19 pandemic is a deadly epidemic and continues to affect all world. This situation dragged the countries into a global crisis and caused the collapse of some health systems. Therefore, many technologies are needed to slow down the spread of the Covid-19 epidemic and produce solutions. In this context, some developments have been made with artificial intelligence, machine learning and deep learning support systems in order to alleviate the burden on the health system. In this study, a new Internet of Medical Things (IoMT) framework is proposed for the detection and early prevention of Covid-19 infection. In the proposed IoMT framework, a Covid-19 scenario consisting of various numbers of sensors is created in the Riverbed Modeler simulation software. The health data produced in this scenario are analyzed in real time with Apache Spark technology, and disease prediction is made. In order to provide more accurate results for Covid-19 disease prediction, Random Forest and Gradient Boosted Tree (GBT) Ensemble Learning classifiers, which are formed by Decision Tree classifiers, are compared for the performance evaluation. In addition, throughput, end-to-end delay results and Apache Spark data processing performance of heterogeneous nodes with different priorities are analyzed in the Covid-19 scenario. The MongoDB NoSQL database is used in the IoMT framework to store big health data produced in real time and use it in subsequent processes. The proposed IoMT framework experimental results show that the GBTs classifier has the best performance with 95.70% training, 95.30% test accuracy and 0.970 area under the curve (AUC) values. Moreover, the promising real-time performances of wireless body area network (WBAN) simulation scenario and Apache Spark show that they can be used for the early detection of Covid-19 disease.
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Affiliation(s)
- Emre Yildirim
- Computer Technology Department, Osmaniye Korkut Ata University, Osmaniye, Turkey
| | - Murtaza Cicioğlu
- Computer Engineering Department, Bursa Uludağ University, Bursa, Turkey
| | - Ali Çalhan
- Computer Engineering Department, Düzce University, Düzce, Turkey
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25
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Holzinger A, Saranti A, Angerschmid A, Retzlaff CO, Gronauer A, Pejakovic V, Medel-Jimenez F, Krexner T, Gollob C, Stampfer K. Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions. SENSORS (BASEL, SWITZERLAND) 2022; 22:3043. [PMID: 35459028 PMCID: PMC9029836 DOI: 10.3390/s22083043] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/06/2022] [Accepted: 04/13/2022] [Indexed: 02/01/2023]
Abstract
The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline-no AI can do this. Consequently, human-centered AI (HCAI) is a combination of "artificial intelligence" and "natural intelligence" to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art.
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Affiliation(s)
- Andreas Holzinger
- Human-Centered AI Lab, Institute of Forest Engineering, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences Vienna, 1190 Wien, Austria; (A.S.); (A.A.); (C.O.R.)
- xAI Lab, Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB T5J 3B1, Canada
| | - Anna Saranti
- Human-Centered AI Lab, Institute of Forest Engineering, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences Vienna, 1190 Wien, Austria; (A.S.); (A.A.); (C.O.R.)
| | - Alessa Angerschmid
- Human-Centered AI Lab, Institute of Forest Engineering, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences Vienna, 1190 Wien, Austria; (A.S.); (A.A.); (C.O.R.)
| | - Carl Orge Retzlaff
- Human-Centered AI Lab, Institute of Forest Engineering, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences Vienna, 1190 Wien, Austria; (A.S.); (A.A.); (C.O.R.)
- DAI Lab, Technical University Berlin, 10623 Berlin, Germany
| | - Andreas Gronauer
- Institute of Agricultural Engineering, Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences Vienna, 1180 Wien, Austria; (A.G.); (V.P.); (F.M.-J.); (T.K.)
| | - Vladimir Pejakovic
- Institute of Agricultural Engineering, Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences Vienna, 1180 Wien, Austria; (A.G.); (V.P.); (F.M.-J.); (T.K.)
| | - Francisco Medel-Jimenez
- Institute of Agricultural Engineering, Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences Vienna, 1180 Wien, Austria; (A.G.); (V.P.); (F.M.-J.); (T.K.)
| | - Theresa Krexner
- Institute of Agricultural Engineering, Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences Vienna, 1180 Wien, Austria; (A.G.); (V.P.); (F.M.-J.); (T.K.)
| | - Christoph Gollob
- Institute of Forest Growth, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences Vienna, 1180 Wien, Austria;
| | - Karl Stampfer
- Institute of Forest Engineering, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences Vienna, 1180 Wien, Austria;
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26
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Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112412122] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Breast cancer detection using mammogram images at an early stage is an important step in disease diagnostics. We propose a new method for the classification of benign or malignant breast cancer from mammogram images. Hybrid thresholding and the machine learning method are used to derive the region of interest (ROI). The derived ROI is then separated into five different blocks. The wavelet transform is applied to suppress noise from each produced block based on BayesShrink soft thresholding by capturing high and low frequencies within different sub-bands. An improved fractal dimension (FD) approach, called multi-FD (M-FD), is proposed to extract multiple features from each denoised block. The number of features extracted is then reduced by a genetic algorithm. Five classifiers are trained and used with the artificial neural network (ANN) to classify the extracted features from each block. Lastly, the fusion process is performed on the results of five blocks to obtain the final decision. The proposed approach is tested and evaluated on four benchmark mammogram image datasets (MIAS, DDSM, INbreast, and BCDR). We present the results of single- and double-dataset evaluations. Only one dataset is used for training and testing in the single-dataset evaluation, whereas two datasets (one for training, and one for testing) are used in the double-dataset evaluation. The experiment results show that the proposed method yields better results on the INbreast dataset in the single-dataset evaluation, whilst better results are obtained on the remaining datasets in the double-dataset evaluation. The proposed approach outperforms other state-of-the-art models on the Mini-MIAS dataset.
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