1
|
Wahid KA, Cardenas CE, Marquez B, Netherton TJ, Kann BH, Court LE, He R, Naser MA, Moreno AC, Fuller CD, Fuentes D. Evolving Horizons in Radiation Therapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification. Adv Radiat Oncol 2024; 9:101521. [PMID: 38799110 PMCID: PMC11111585 DOI: 10.1016/j.adro.2024.101521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/26/2024] [Indexed: 05/29/2024] Open
Affiliation(s)
- Kareem A. Wahid
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Carlos E. Cardenas
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Barbara Marquez
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Tucker J. Netherton
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Benjamin H. Kann
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Laurence E. Court
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Renjie He
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Mohamed A. Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Amy C. Moreno
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - David Fuentes
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| |
Collapse
|
2
|
Scott IA, van der Vegt A, Lane P, McPhail S, Magrabi F. Achieving large-scale clinician adoption of AI-enabled decision support. BMJ Health Care Inform 2024; 31:e100971. [PMID: 38816209 PMCID: PMC11141172 DOI: 10.1136/bmjhci-2023-100971] [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: 11/19/2023] [Accepted: 05/15/2024] [Indexed: 06/01/2024] Open
Abstract
Computerised decision support (CDS) tools enabled by artificial intelligence (AI) seek to enhance accuracy and efficiency of clinician decision-making at the point of care. Statistical models developed using machine learning (ML) underpin most current tools. However, despite thousands of models and hundreds of regulator-approved tools internationally, large-scale uptake into routine clinical practice has proved elusive. While underdeveloped system readiness and investment in AI/ML within Australia and perhaps other countries are impediments, clinician ambivalence towards adopting these tools at scale could be a major inhibitor. We propose a set of principles and several strategic enablers for obtaining broad clinician acceptance of AI/ML-enabled CDS tools.
Collapse
Affiliation(s)
- Ian A Scott
- Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, Queensland, Australia
- Centre for Health Services Research, The University of Queensland Faculty of Medicine and Biomedical Sciences, Brisbane, Queensland, Australia
| | - Anton van der Vegt
- Digital Health Centre, The University of Queensland Faculty of Medicine and Biomedical Sciences, Herston, Queensland, Australia
| | - Paul Lane
- Safety, Quality and Innovation, The Prince Charles Hospital, Brisbane, Queensland, Australia
| | - Steven McPhail
- Australian Centre for Health Services Innovation, Queensland University of Technology Faculty of Health, Brisbane, Queensland, Australia
| | - Farah Magrabi
- Macquarie University, Sydney, New South Wales, Australia
| |
Collapse
|
3
|
Wahid KA, Cardenas CE, Marquez B, Netherton TJ, Kann BH, Court LE, He R, Naser MA, Moreno AC, Fuller CD, Fuentes D. Evolving Horizons in Radiotherapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification. ARXIV 2023:arXiv:2310.10867v1. [PMID: 37904737 PMCID: PMC10614971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Affiliation(s)
- Kareem A. Wahid
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Carlos E. Cardenas
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Barbara Marquez
- UT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tucker J. Netherton
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Benjamin H. Kann
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Laurence E. Court
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Renjie He
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mohamed A. Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Amy C. Moreno
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David Fuentes
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| |
Collapse
|
4
|
Chavez JM, Tang W. A Vision-Based System for Stage Classification of Parkinsonian Gait Using Machine Learning and Synthetic Data. SENSORS (BASEL, SWITZERLAND) 2022; 22:4463. [PMID: 35746246 PMCID: PMC9229496 DOI: 10.3390/s22124463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 06/15/2023]
Abstract
Parkinson's disease is characterized by abnormal gait, which worsens as the condition progresses. Although several methods have been able to classify this feature through pose-estimation algorithms and machine-learning classifiers, few studies have been able to analyze its progression to perform stage classification of the disease. Moreover, despite the increasing popularity of these systems for gait analysis, the amount of available gait-related data can often be limited, thereby, hindering the progress of the implementation of this technology in the medical field. As such, creating a quantitative prognosis method that can identify the severity levels of a Parkinsonian gait with little data could help facilitate the study of the Parkinsonian gait for rehabilitation. In this contribution, we propose a vision-based system to analyze the Parkinsonian gait at various stages using linear interpolation of Parkinsonian gait models. We present a comparison between the performance of a k-nearest neighbors algorithm (KNN), support-vector machine (SVM) and gradient boosting (GB) algorithms in classifying well-established gait features. Our results show that the proposed system achieved 96-99% accuracy in evaluating the prognosis of Parkinsonian gaits.
Collapse
Affiliation(s)
| | - Wei Tang
- Klipsch School of Electrical Engineering, New Mexico State University, Las Cruces, NM 88003, USA
| |
Collapse
|
5
|
Silva F, Pereira T, Neves I, Morgado J, Freitas C, Malafaia M, Sousa J, Fonseca J, Negrão E, Flor de Lima B, Correia da Silva M, Madureira AJ, Ramos I, Costa JL, Hespanhol V, Cunha A, Oliveira HP. Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges. J Pers Med 2022; 12:jpm12030480. [PMID: 35330479 PMCID: PMC8950137 DOI: 10.3390/jpm12030480] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 02/28/2022] [Accepted: 03/10/2022] [Indexed: 12/15/2022] Open
Abstract
Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and “motivate” the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers.
Collapse
Affiliation(s)
- Francisco Silva
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- FCUP—Faculty of Science, University of Porto, 4169-007 Porto, Portugal
- Correspondence: (F.S.); (T.P.)
| | - Tania Pereira
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- Correspondence: (F.S.); (T.P.)
| | - Inês Neves
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- ICBAS—Abel Salazar Biomedical Sciences Institute, University of Porto, 4050-313 Porto, Portugal
| | - Joana Morgado
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
| | - Cláudia Freitas
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
| | - Mafalda Malafaia
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- FEUP—Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Joana Sousa
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
| | - João Fonseca
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- FEUP—Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Eduardo Negrão
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Beatriz Flor de Lima
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Miguel Correia da Silva
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - António J. Madureira
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
| | - Isabel Ramos
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
| | - José Luis Costa
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal
- IPATIMUP—Institute of Molecular Pathology and Immunology of the University of Porto, 4200-135 Porto, Portugal
| | - Venceslau Hespanhol
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
| | - António Cunha
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- UTAD—University of Trás-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal
| | - Hélder P. Oliveira
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- FCUP—Faculty of Science, University of Porto, 4169-007 Porto, Portugal
| |
Collapse
|