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Fraunhoffer N, Hammel P, Conroy T, Nicolle R, Bachet JB, Harlé A, Rebours V, Turpin A, Abdelghani MB, Mitry E, Biagi J, Chanez B, Bigonnet M, Lopez A, Evesque L, Lecomte T, Assenat E, Bouché O, Renouf D, Lambert A, Monard L, Mauduit M, Cros J, Iovanna J, Dusetti N. Development and validation of AI-assisted transcriptomic signatures to personalize adjuvant chemotherapy in patients with pancreatic ductal adenocarcinoma. Ann Oncol 2024:S0923-7534(24)00741-5. [PMID: 38906254 DOI: 10.1016/j.annonc.2024.06.010] [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: 02/26/2024] [Revised: 05/14/2024] [Accepted: 06/10/2024] [Indexed: 06/23/2024] Open
Abstract
BACKGROUND After surgical resection of pancreatic ductal adenocarcinoma (PDAC), patients are predominantly treated with adjuvant chemotherapy, commonly consisting of gemcitabine-based regimens or the modified FOLFIRINOX regimen (mFFX). While mFFX has been shown to be more effective than gemcitabine-based regimens, it is also associated with higher toxicity. Current treatment decisions are based on patient performance status rather than on the molecular characteristics of the tumor. To address this gap, the goal of this study was to develop drug-specific transcriptomic signatures for personalized chemotherapy treatment. PATIENTS AND METHODS We used PDAC datasets from preclinical models, encompassing chemotherapy response profiles for the mFFX-regimen components. From them we identified specific gene transcripts associated with chemotherapy response. Three transcriptomic AI-signatures were obtained by combining Independent Component Analysis, Least Absolute Shrinkage and the Selection Operator-Random Forest approach. We integrated a previously developed gemcitabine signature with three newly developed ones. The machine learning strategy employed to enhance these signatures incorporates transcriptomic features from the tumor microenvironment, leading to the development of the Pancreas-View tool ultimately clinically validated in a cohort of 343 patients from the PRODIGE-24/CCTG PA6 trial. RESULTS Patients who were predicted to be sensitive to the administered drugs (n=164; 47.8%) had longer disease-free survival (DFS) than the other patients. The median DFS in the mFFX sensitive group treated with mFFX was 50.0 months (stratified HR: 0.31; 95% CI, 0.21-0.44; p<0.001) and 33.7 months (stratified HR: 0.40; 95% CI, 0.17-0.59; p<0.001) in the gemcitabine sensitive group when treated with gemcitabine. Comparatively patients with signature predictions unmatched with the treatments (n=86; 25.1%) or those resistant to all drugs (n=93; 27.1%) had shorter DFS (10.6 and 10.8 months, respectively). CONCLUSIONS This study presents a transcriptome-based tool that was developed using preclinical models and machine learning to accurately predict sensitivity to mFFX and gemcitabine.
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Affiliation(s)
- N Fraunhoffer
- Centre de Recherche en Cancérologie de Marseille (CRCM), INSERM U1068, CNRS UMR 7258, Aix- Marseille Université and Institut Paoli-Calmettes; Parc Scientifique et Technologique de Luminy, Marseille, France
| | - P Hammel
- Digestive and Medical Oncology, Paul Brousse Hospital, Assistance Publique - Hôpitaux de Paris (AP- HP), Université of Paris-Saclay, Villejuif, France
| | - T Conroy
- Medical Oncology department, Institut de cancérologie de Lorraine, Vandoeuvre-lès-Nancy, France and Université de Lorraine, APEMAC, équipe MICS, Nancy, France
| | - R Nicolle
- Université Paris Cité, Centre de Recherche sur l'Inflammation (CRI), INSERM, U1149, CNRS, ERL 8252, F-75018 Paris, France
| | - J-B Bachet
- Service d'Hépato - Gastro - Entérologie, Hôpital Pitié Salpêtrière, Assistance Publique - Hôpitaux de Paris (APHP), Sorbonne Université, Paris, France
| | - A Harlé
- Service de Biopathologie, Institut de Cancérologie de Lorraine, Université de Lorraine, CNRS UMR 7039 CRAN, 54519 Vandœuvre-lès-Nancy CEDEX, France
| | - V Rebours
- Pancreatology and Digestive Oncology Department, Beaujon Hospital - APHP, Clichy- INSERM - UMR 1149 - Université Paris-Cité, France
| | - A Turpin
- Department of Oncology, Lille University Hospital; CNRS UMR9020, INSERM UMR1277, University of Lille, Institut Pasteur, Lille, France
| | - M B Abdelghani
- Institut de Cancérologie Strasbourg Europe, Strasbourg, France
| | - E Mitry
- Centre de Recherche en Cancérologie de Marseille (CRCM), INSERM U1068, CNRS UMR 7258, Aix- Marseille Université and Institut Paoli-Calmettes; Parc Scientifique et Technologique de Luminy, Marseille, France; Department of Medical Oncology, Paoli-Calmettes Institute, Marseille, France
| | - J Biagi
- Department of Oncology, Queen's University, Canada
| | - B Chanez
- Centre de Recherche en Cancérologie de Marseille (CRCM), INSERM U1068, CNRS UMR 7258, Aix- Marseille Université and Institut Paoli-Calmettes; Parc Scientifique et Technologique de Luminy, Marseille, France; Department of Medical Oncology, Paoli-Calmettes Institute, Marseille, France
| | - M Bigonnet
- PredictingMed, Luminy Science and Technology Park, Marseille, France
| | - A Lopez
- Hepatogastroenterology department, University Hospital, Nancy, France
| | - L Evesque
- Department of Medical Oncology, Antoine Lacassagne Center, Nice
| | - T Lecomte
- Hepatogastroenterology department, Hôpital Trousseau, Tours, France and INSERM UMR 1069, Tours University, Tours, France
| | - E Assenat
- Medical oncology department, Centre Hospitalier Universitaire de Saint-Eloi, Montpellier, France
| | - O Bouché
- Digestive oncology department, Centre Hospitalier Universitaire Robert Debré, Reims, France
| | - D Renouf
- Division of Medical Oncology, BC Cancer, Vancouver, British Columbia, Canada; Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - A Lambert
- Medical Oncology department, Institut de cancérologie de Lorraine, Vandoeuvre-lès-Nancy, France and Université de Lorraine, APEMAC, équipe MICS, Nancy, France
| | | | | | - J Cros
- Université Paris Cité, Centre de Recherche sur l'Inflammation (CRI), INSERM, U1149, CNRS, ERL 8252, F-75018 Paris, France; Université Paris Cité, Department of Pathology, FHU MOSAIC, Beaujon/Bichat University Hospital (APHP), Clichy/Paris, France
| | - J Iovanna
- Centre de Recherche en Cancérologie de Marseille (CRCM), INSERM U1068, CNRS UMR 7258, Aix- Marseille Université and Institut Paoli-Calmettes; Parc Scientifique et Technologique de Luminy, Marseille, France; Hospital de Alta Complejidad El Cruce, Florencio Varela, Buenos Aires, Argentina; University Arturo Jauretche, Florencio Varela, Buenos Aires, Argentina.
| | - N Dusetti
- Centre de Recherche en Cancérologie de Marseille (CRCM), INSERM U1068, CNRS UMR 7258, Aix- Marseille Université and Institut Paoli-Calmettes; Parc Scientifique et Technologique de Luminy, Marseille, France.
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Armoundas AA, Narayan SM, Arnett DK, Spector-Bagdady K, Bennett DA, Celi LA, Friedman PA, Gollob MH, Hall JL, Kwitek AE, Lett E, Menon BK, Sheehan KA, Al-Zaiti SS. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e1028-e1050. [PMID: 38415358 PMCID: PMC11042786 DOI: 10.1161/cir.0000000000001201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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3
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DelPozo-Banos M, Stewart R, John A. Machine learning in mental health and its relationship with epidemiological practice. Front Psychiatry 2024; 15:1347100. [PMID: 38528983 PMCID: PMC10961376 DOI: 10.3389/fpsyt.2024.1347100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 02/22/2024] [Indexed: 03/27/2024] Open
Affiliation(s)
| | - Robert Stewart
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
- South London and Maudsley National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Ann John
- Swansea University Medical School, Swansea, United Kingdom
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Ciobanu-Caraus O, Aicher A, Kernbach JM, Regli L, Serra C, Staartjes VE. A critical moment in machine learning in medicine: on reproducible and interpretable learning. Acta Neurochir (Wien) 2024; 166:14. [PMID: 38227273 DOI: 10.1007/s00701-024-05892-8] [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: 11/21/2023] [Accepted: 12/14/2023] [Indexed: 01/17/2024]
Abstract
Over the past two decades, advances in computational power and data availability combined with increased accessibility to pre-trained models have led to an exponential rise in machine learning (ML) publications. While ML may have the potential to transform healthcare, this sharp increase in ML research output without focus on methodological rigor and standard reporting guidelines has fueled a reproducibility crisis. In addition, the rapidly growing complexity of these models compromises their interpretability, which currently impedes their successful and widespread clinical adoption. In medicine, where failure of such models may have severe implications for patients' health, the high requirements for accuracy, robustness, and interpretability confront ML researchers with a unique set of challenges. In this review, we discuss the semantics of reproducibility and interpretability, as well as related issues and challenges, and outline possible solutions to counteracting the "black box". To foster reproducibility, standard reporting guidelines need to be further developed and data or code sharing encouraged. Editors and reviewers may equally play a critical role by establishing high methodological standards and thus preventing the dissemination of low-quality ML publications. To foster interpretable learning, the use of simpler models more suitable for medical data can inform the clinician how results are generated based on input data. Model-agnostic explanation tools, sensitivity analysis, and hidden layer representations constitute further promising approaches to increase interpretability. Balancing model performance and interpretability are important to ensure clinical applicability. We have now reached a critical moment for ML in medicine, where addressing these issues and implementing appropriate solutions will be vital for the future evolution of the field.
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Affiliation(s)
- Olga Ciobanu-Caraus
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Anatol Aicher
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Julius M Kernbach
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Luca Regli
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Carlo Serra
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Victor E Staartjes
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
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Maynard S, Farrington J, Alimam S, Evans H, Li K, Wong WK, Stanworth SJ. Machine learning in transfusion medicine: A scoping review. Transfusion 2024; 64:162-184. [PMID: 37950535 DOI: 10.1111/trf.17582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 11/12/2023]
Affiliation(s)
- Suzanne Maynard
- Medical Sciences Division, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NHSBT and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Joseph Farrington
- Institute of Health Informatics, University College London, London, UK
| | - Samah Alimam
- Haematology Department, University College London Hospitals NHS Foundation Trust, London, UK
| | - Hayley Evans
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Kezhi Li
- Institute of Health Informatics, University College London, London, UK
| | - Wai Keong Wong
- Director of Digital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Simon J Stanworth
- Medical Sciences Division, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NHSBT and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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Li C, Liu X, Shen P, Sun Y, Zhou T, Chen W, Chen Q, Lin H, Tang X, Gao P. Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:30-40. [PMID: 38264696 PMCID: PMC10802828 DOI: 10.1093/ehjdh/ztad058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/03/2023] [Accepted: 09/19/2023] [Indexed: 01/25/2024]
Abstract
Aims Existing electronic health records (EHRs) often consist of abundant but irregular longitudinal measurements of risk factors. In this study, we aim to leverage such data to improve the risk prediction of atherosclerotic cardiovascular disease (ASCVD) by applying machine learning (ML) algorithms, which can allow automatic screening of the population. Methods and results A total of 215 744 Chinese adults aged between 40 and 79 without a history of cardiovascular disease were included (6081 cases) from an EHR-based longitudinal cohort study. To allow interpretability of the model, the predictors of demographic characteristics, medication treatment, and repeatedly measured records of lipids, glycaemia, obesity, blood pressure, and renal function were used. The primary outcome was ASCVD, defined as non-fatal acute myocardial infarction, coronary heart disease death, or fatal and non-fatal stroke. The eXtreme Gradient boosting (XGBoost) algorithm and Least Absolute Shrinkage and Selection Operator (LASSO) regression models were derived to predict the 5-year ASCVD risk. In the validation set, compared with the refitted Chinese guideline-recommended Cox model (i.e. the China-PAR), the XGBoost model had a significantly higher C-statistic of 0.792, (the differences in the C-statistics: 0.011, 0.006-0.017, P < 0.001), with similar results reported for LASSO regression (the differences in the C-statistics: 0.008, 0.005-0.011, P < 0.001). The XGBoost model demonstrated the best calibration performance (men: Dx = 0.598, P = 0.75; women: Dx = 1.867, P = 0.08). Moreover, the risk distribution of the ML algorithms differed from that of the conventional model. The net reclassification improvement rates of XGBoost and LASSO over the Cox model were 3.9% (1.4-6.4%) and 2.8% (0.7-4.9%), respectively. Conclusion Machine learning algorithms with irregular, repeated real-world data could improve cardiovascular risk prediction. They demonstrated significantly better performance for reclassification to identify the high-risk population correctly.
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Affiliation(s)
- Chaiquan Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
| | - Xiaofei Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
| | - Peng Shen
- Yinzhou District Center for Disease Control and Prevention, No. 1221 Xueshi Road, Yinzhou District, 315199 Ningbo, China
| | - Yexiang Sun
- Yinzhou District Center for Disease Control and Prevention, No. 1221 Xueshi Road, Yinzhou District, 315199 Ningbo, China
| | - Tianjing Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
| | - Weiye Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
| | - Qi Chen
- Yinzhou District Center for Disease Control and Prevention, No. 1221 Xueshi Road, Yinzhou District, 315199 Ningbo, China
| | - Hongbo Lin
- Yinzhou District Center for Disease Control and Prevention, No. 1221 Xueshi Road, Yinzhou District, 315199 Ningbo, China
| | - Xun Tang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
| | - Pei Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
- Center for Real-world Evidence Evaluation, Peking University Clinical Research Institute, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
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Drukker K, Chen W, Gichoya J, Gruszauskas N, Kalpathy-Cramer J, Koyejo S, Myers K, Sá RC, Sahiner B, Whitney H, Zhang Z, Giger M. Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment. J Med Imaging (Bellingham) 2023; 10:061104. [PMID: 37125409 PMCID: PMC10129875 DOI: 10.1117/1.jmi.10.6.061104] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 04/03/2023] [Indexed: 05/02/2023] Open
Abstract
Purpose To recognize and address various sources of bias essential for algorithmic fairness and trustworthiness and to contribute to a just and equitable deployment of AI in medical imaging, there is an increasing interest in developing medical imaging-based machine learning methods, also known as medical imaging artificial intelligence (AI), for the detection, diagnosis, prognosis, and risk assessment of disease with the goal of clinical implementation. These tools are intended to help improve traditional human decision-making in medical imaging. However, biases introduced in the steps toward clinical deployment may impede their intended function, potentially exacerbating inequities. Specifically, medical imaging AI can propagate or amplify biases introduced in the many steps from model inception to deployment, resulting in a systematic difference in the treatment of different groups. Approach Our multi-institutional team included medical physicists, medical imaging artificial intelligence/machine learning (AI/ML) researchers, experts in AI/ML bias, statisticians, physicians, and scientists from regulatory bodies. We identified sources of bias in AI/ML, mitigation strategies for these biases, and developed recommendations for best practices in medical imaging AI/ML development. Results Five main steps along the roadmap of medical imaging AI/ML were identified: (1) data collection, (2) data preparation and annotation, (3) model development, (4) model evaluation, and (5) model deployment. Within these steps, or bias categories, we identified 29 sources of potential bias, many of which can impact multiple steps, as well as mitigation strategies. Conclusions Our findings provide a valuable resource to researchers, clinicians, and the public at large.
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Affiliation(s)
- Karen Drukker
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Weijie Chen
- US Food and Drug Administration, Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Silver Spring, Maryland, United States
| | - Judy Gichoya
- Emory University, Department of Radiology, Atlanta, Georgia, United States
| | - Nicholas Gruszauskas
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | | | - Sanmi Koyejo
- Stanford University, Department of Computer Science, Stanford, California, United States
| | - Kyle Myers
- Puente Solutions LLC, Phoenix, Arizona, United States
| | - Rui C. Sá
- National Institutes of Health, Bethesda, Maryland, United States
- University of California, San Diego, La Jolla, California, United States
| | - Berkman Sahiner
- US Food and Drug Administration, Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Silver Spring, Maryland, United States
| | - Heather Whitney
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Zi Zhang
- Jefferson Health, Philadelphia, Pennsylvania, United States
| | - Maryellen Giger
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
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Al-Zaiti SS, Bond RR. Explainable-by-design: Challenges, pitfalls, and opportunities for the clinical adoption of AI-enabled ECG. J Electrocardiol 2023; 81:292-294. [PMID: 37635030 DOI: 10.1016/j.jelectrocard.2023.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 08/10/2023] [Indexed: 08/29/2023]
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Diba SF, Sari DCR, Supriatna Y, Ardiyanto I, Bintoro BS. Artificial intelligence in detecting dentomaxillofacial fractures in diagnostic imaging: a scoping review protocol. BMJ Open 2023; 13:e071324. [PMID: 37553193 PMCID: PMC10414106 DOI: 10.1136/bmjopen-2022-071324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 07/18/2023] [Indexed: 08/10/2023] Open
Abstract
INTRODUCTION The dentomaxillofacial (DMF) area, which includes the teeth, maxilla, mandible, zygomaticum, orbits and midface, plays a crucial role in the maintenance of the physiological functions despite its susceptibility to fractures, which are mostly caused by mechanical trauma. As a diagnostic tool, radiographic imaging helps clinicians establish a diagnosis and determine a treatment plan; however, the presence of human factors in image interpretation can result in missed detection of fractures. Therefore, an artificial intelligence (AI) computing system with the potential to help detect abnormalities on radiographic images is currently being developed. This scoping review summarises the literature and assesses the current status of AI in DMF fracture detection in diagnostic imaging. METHODS AND ANALYSIS This proposed scoping review will be conducted using the framework of Arksey and O'Malley, with each step incorporating the recommendations of Levac et al. By using relevant keywords based on the research questions. PubMed, Science Direct, Scopus, Cochrane Library, Springerlink, Institute of Electrical and Electronics Engineers, and ProQuest will be the databases used in this study. The included studies are published in English between 1 January 2000 and 30 June 2023. Two independent reviewers will screen titles and abstracts, followed by full-text screening and data extraction, which will comprise three components: research study characteristics, comparator and AI characteristics. ETHICS AND DISSEMINATION This study does not require ethical approval because it analyses primary research articles. The research findings will be distributed through international conferences and peer-reviewed publications.
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Affiliation(s)
- Silviana Farrah Diba
- Doctorate Program of Medical and Health Science, Gadjah Mada University Faculty of Medicine Public Health and Nursing, Yogyakarta, Indonesia
- Department of Dentomaxillofacial Radiology, Gadjah Mada University Faculty of Dentistry, Yogyakarta, Indonesia
| | - Dwi Cahyani Ratna Sari
- Department of Anatomy, Gadjah Mada University Faculty of Medicine Public Health and Nursing, Yogyakarta, Indonesia
| | - Yana Supriatna
- Department of Radiology, Gadjah Mada University Faculty of Medicine Public Health and Nursing, Yogyakarta, Indonesia
- Radiological Installation, Public Hospital Dr Sardjito, Yogyakarta, Indonesia
| | - Igi Ardiyanto
- Department of Electrical Engineering and Information Technology, Gadjah Mada University Faculty of Engineering, Yogyakarta, Indonesia
| | - Bagas Suryo Bintoro
- Department of Health Behaviour, Environment, and Social Medicine, Gadjah Mada University Faculty of Medicine Public Health and Nursing, Yogyakarta, Indonesia
- Center of Health Behavior and Promotion, Gadjah Mada University Faculty of Medicine Public Health and Nursing, Yogyakarta, Indonesia
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Al-Zaiti SS, Martin-Gill C, Zègre-Hemsey JK, Bouzid Z, Faramand Z, Alrawashdeh MO, Gregg RE, Helman S, Riek NT, Kraevsky-Phillips K, Clermont G, Akcakaya M, Sereika SM, Van Dam P, Smith SW, Birnbaum Y, Saba S, Sejdic E, Callaway CW. Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Nat Med 2023; 29:1804-1813. [PMID: 37386246 PMCID: PMC10353937 DOI: 10.1038/s41591-023-02396-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 05/11/2023] [Indexed: 07/01/2023]
Abstract
Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.
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Affiliation(s)
- Salah S Al-Zaiti
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Division of Cardiology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Christian Martin-Gill
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | | | - Zeineb Bouzid
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ziad Faramand
- Department of Emergency Medicine, Northeast Georgia Health System, Gainesville, GA, USA
| | - Mohammad O Alrawashdeh
- School of Nursing, Jordan University of Science and Technology, Irbid, Jordan
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Richard E Gregg
- Advanced Algorithm Development Center, Philips Healthcare, Cambridge, MA, USA
| | - Stephanie Helman
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nathan T Riek
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Gilles Clermont
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Murat Akcakaya
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Susan M Sereika
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Peter Van Dam
- Division of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Stephen W Smith
- Department of Emergency Medicine, Hennepin Healthcare, Minneapolis, MN, USA
- Department of Emergency Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Yochai Birnbaum
- Division of Cardiology, Baylor College of Medicine, Houston, TX, USA
| | - Samir Saba
- Division of Cardiology, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Ervin Sejdic
- Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada
- Artificial Intelligence for Health Outcomes at Research & Innovation, North York General Hospital, Toronto, ON, Canada
| | - Clifton W Callaway
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
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11
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Harris CS, Pozzar RA, Conley Y, Eicher M, Hammer MJ, Kober KM, Miaskowski C, Colomer-Lahiguera S. Big Data in Oncology Nursing Research: State of the Science. Semin Oncol Nurs 2023; 39:151428. [PMID: 37085404 PMCID: PMC11225574 DOI: 10.1016/j.soncn.2023.151428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 03/21/2023] [Indexed: 04/23/2023]
Abstract
OBJECTIVE To review the state of oncology nursing science as it pertains to big data. The authors aim to define and characterize big data, describe key considerations for accessing and analyzing big data, provide examples of analyses of big data in oncology nursing science, and highlight ethical considerations related to the collection and analysis of big data. DATA SOURCES Peer-reviewed articles published by investigators specializing in oncology, nursing, and related disciplines. CONCLUSION Big data is defined as data that are high in volume, velocity, and variety. To date, oncology nurse scientists have used big data to predict patient outcomes from clinician notes, identify distinct symptom phenotypes, and identify predictors of chemotherapy toxicity, among other applications. Although the emergence of big data and advances in computational methods provide new and exciting opportunities to advance oncology nursing science, several challenges are associated with accessing and using big data. Data security, research participant privacy, and the underrepresentation of minoritized individuals in big data are important concerns. IMPLICATIONS FOR NURSING PRACTICE With their unique focus on the interplay between the whole person, the environment, and health, nurses bring an indispensable perspective to the interpretation and application of big data research findings. Given the increasing ubiquity of passive data collection, all nurses should be taught the definition, characteristics, applications, and limitations of big data. Nurses who are trained in big data and advanced computational methods will be poised to contribute to guidelines and policies that preserve the rights of human research participants.
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Affiliation(s)
- Carolyn S Harris
- Postdoctoral Scholar, School of Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Rachel A Pozzar
- Nurse Scientist at Phyllis F. Cantor Center for Research in Nursing and Patient Care Services, Dana-Farber Cancer Institute, Boston, Massachusetts, USA and Instructor at Harvard Medical School, Boston, Massachusetts, USA
| | - Yvette Conley
- Professor, School of Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Manuela Eicher
- Associate Professor and Director of the Institute of Higher Education and Research in Healthcare (IUFRS), Faculty of Biology and Medicine, University of Lausanne, and Lausanne University Hospital, Lausanne, Switzerland
| | - Marilyn J Hammer
- Director, The Phyllis F. Cantor Center for Research in Nursing and Patient Care Services, Dana-Farber Cancer Institute, Boston, Massachusetts, USA and Lecturer at Harvard Medical School, Boston, Massachusetts, USA
| | - Kord M Kober
- Associate Professor, School of Nursing, University of California, San Francisco, California, USA
| | - Christine Miaskowski
- Professor, Schools of Medicine and Nursing, University of California, San Francisco, California, USA
| | - Sara Colomer-Lahiguera
- Senior Nurse Scientist and Junior Lecturer, Institute of Higher Education and Research in Healthcare (IUFRS), Faculty of Biology and Medicine, University of Lausanne, and Lausanne University Hospital, Lausanne, Switzerland.
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12
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Al-Zaiti S, Martin-Gill C, Zégre-Hemsey J, Bouzid Z, Faramand Z, Alrawashdeh M, Gregg R, Helman S, Riek N, Kraevsky-Phillips K, Clermont G, Akcakaya M, Sereika S, Van Dam P, Smith S, Birnbaum Y, Saba S, Sejdic E, Callaway C. Machine Learning for the ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction at First Medical Contact. RESEARCH SQUARE 2023:rs.3.rs-2510930. [PMID: 36778371 PMCID: PMC9915770 DOI: 10.21203/rs.3.rs-2510930/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting ECG are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but we currently have no accurate tools to identify them during initial triage. Herein, we report the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, significantly boosting both precision and sensitivity. Our derived OMI risk score provided superior rule-in and rule-out accuracy compared to routine care, and when combined with the clinical judgment of trained emergency personnel, this score helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.
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13
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Monfredi OJ, Moore CC, Sullivan BA, Keim-Malpass J, Fairchild KD, Loftus TJ, Bihorac A, Krahn KN, Dubrawski A, Lake DE, Moorman JR, Clermont G. Continuous ECG monitoring should be the heart of bedside AI-based predictive analytics monitoring for early detection of clinical deterioration. J Electrocardiol 2023; 76:35-38. [PMID: 36434848 PMCID: PMC10061545 DOI: 10.1016/j.jelectrocard.2022.10.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 10/01/2022] [Accepted: 10/22/2022] [Indexed: 11/24/2022]
Abstract
The idea that we can detect subacute potentially catastrophic illness earlier by using statistical models trained on clinical data is now well-established. We review evidence that supports the role of continuous cardiorespiratory monitoring in these predictive analytics monitoring tools. In particular, we review how continuous ECG monitoring reflects the patient and not the clinician, is less likely to be biased, is unaffected by changes in practice patterns, captures signatures of illnesses that are interpretable by clinicians, and is an underappreciated and underutilized source of detailed information for new mathematical methods to reveal.
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Affiliation(s)
- Oliver J Monfredi
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - Christopher C Moore
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - Brynne A Sullivan
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Pediatrics, University of Virginia, United States of America
| | - Jessica Keim-Malpass
- Center for Advanced Medical Analytics, University of Virginia, United States of America; School of Nursing, University of Virginia, United States of America
| | - Karen D Fairchild
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Pediatrics, University of Virginia, United States of America
| | - Tyler J Loftus
- Department of Surgery, University of Florida, United States of America
| | - Azra Bihorac
- Department of Medicine, University of Florida, United States of America
| | - Katherine N Krahn
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - Artur Dubrawski
- Robotics Institute, Carnegie Mellon University, United States of America
| | - Douglas E Lake
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - J Randall Moorman
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America.
| | - Gilles Clermont
- Department of Critical Care, University of Pittsburgh, United States of America
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14
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Al-Zaiti S, Macleod R, Dam PV, Smith SW, Birnbaum Y. Emerging ECG methods for acute coronary syndrome detection: Recommendations & future opportunities. J Electrocardiol 2022; 74:65-72. [PMID: 36027675 DOI: 10.1016/j.jelectrocard.2022.08.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 08/01/2022] [Accepted: 08/11/2022] [Indexed: 12/13/2022]
Abstract
Despite being the mainstay for the initial noninvasive assessment of patients with symptomatic coronary artery disease, the 12‑lead ECG remains a suboptimal diagnostic tool for myocardial ischemia detection with only acceptable sensitivity and specificity scores. Although myocardial ischemia affects the configuration of the QRS complex and the STT waveform, current guidelines primarily focus on ST segment amplitude, which constitutes a missed opportunity and may explain the suboptimal diagnostic performance of the ECG. This possible opportunity and the low cost and ease of use of the ECG provide compelling motivation to enhance the diagnostic accuracy of the ECG to ischemia detection. This paper describes numerous computational ECG methods and approaches that have been shown to dramatically increase ECG sensitivity to ischemia detection. Briefly, these emerging approaches can be conceptually grouped into one of the following four approaches: (1) leveraging novel ECG waveform features and signatures indicative of ischemic injury other than the classical ST-T amplitude measures; (2) applying body surface potentials mapping (BSPM)-based approaches to enhance the spatial coverage of the surface ECG to detecting ischemia; (3) developing an inverse ECG solution to reconstruct anatomical models of activation and recovery pathways to detect and localize injury currents; and (4) exploring artificial intelligence (AI)-based techniques to harvest ECG waveform signatures of ischemia. We present recent advances, shortcomings, and future opportunities for each of these emerging ECG methods. Future research should focus on the prospective clinical testing of these approaches to establish clinical utility and to expedite potential translation into clinical practice.
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Affiliation(s)
- Salah Al-Zaiti
- Department of Acute & Tertiary Care, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Robert Macleod
- Department of Biomedical Engineering, University of Utah, Salt Lake, UT, USA
| | - Peter Van Dam
- Department of Cardiology, University Medical Center Utrecht, the Netherlands
| | - Stephen W Smith
- Department of Emergency Medicine, Hennepin Healthcare and University of Minnesota, Minneapolis, MN, USA
| | - Yochai Birnbaum
- Division of Cardiology, Baylor College of Medicine, Houston, TX, USA
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