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Anupindi SA, Dillman JR. Body MRI in pediatrics: where we are and what the future holds. Pediatr Radiol 2024:10.1007/s00247-024-05984-8. [PMID: 38981906 DOI: 10.1007/s00247-024-05984-8] [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: 05/27/2024] [Revised: 06/21/2024] [Accepted: 06/24/2024] [Indexed: 07/11/2024]
Abstract
Body magnetic resonance imaging (MRI) is increasingly used for disease diagnosis, characterization, and monitoring in children of all ages. MRI has numerous advantages when compared to other imaging modalities, including a lack of ionizing radiation, superior soft tissue image contrast, and ability to provide objective, quantitative assessments. As MRI continues to evolve, pediatric body MRI examinations of the future will certainly be different than our current and past protocols. In this review article, we will discuss the present and likely future states of pediatric body MRI, including the increasing application of quantitative MRI methods, faster imaging techniques and implementation of abbreviated targeted protocols, and the growing use of artificial intelligence methods.
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Affiliation(s)
- Sudha A Anupindi
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jonathan R Dillman
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
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Wang Y, Fu W, Zhang Y, Wang D, Gu Y, Wang W, Xu H, Ge X, Ye C, Fang J, Su L, Wang J, He W, Zhang X, Feng R. Constructing and implementing a performance evaluation indicator set for artificial intelligence decision support systems in pediatric outpatient clinics: an observational study. Sci Rep 2024; 14:14482. [PMID: 38914707 PMCID: PMC11196575 DOI: 10.1038/s41598-024-64893-w] [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: 12/01/2023] [Accepted: 06/13/2024] [Indexed: 06/26/2024] Open
Abstract
Artificial intelligence (AI) decision support systems in pediatric healthcare have a complex application background. As an AI decision support system (AI-DSS) can be costly, once applied, it is crucial to focus on its performance, interpret its success, and then monitor and update it to ensure ongoing success consistently. Therefore, a set of evaluation indicators was explicitly developed for AI-DSS in pediatric healthcare, enabling continuous and systematic performance monitoring. The study unfolded in two stages. The first stage encompassed establishing the evaluation indicator set through a literature review, a focus group interview, and expert consultation using the Delphi method. In the second stage, weight analysis was conducted. Subjective weights were calculated based on expert opinions through analytic hierarchy process, while objective weights were determined using the entropy weight method. Subsequently, subject and object weights were synthesized to form the combined weight. In the two rounds of expert consultation, the authority coefficients were 0.834 and 0.846, Kendall's coordination coefficient was 0.135 in Round 1 and 0.312 in Round 2. The final evaluation indicator set has three first-class indicators, fifteen second-class indicators, and forty-seven third-class indicators. Indicator I-1(Organizational performance) carries the highest weight, followed by Indicator I-2(Societal performance) and Indicator I-3(User experience performance) in the objective and combined weights. Conversely, 'Societal performance' holds the most weight among the subjective weights, followed by 'Organizational performance' and 'User experience performance'. In this study, a comprehensive and specialized set of evaluation indicators for the AI-DSS in the pediatric outpatient clinic was established, and then implemented. Continuous evaluation still requires long-term data collection to optimize the weight proportions of the established indicators.
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Affiliation(s)
- Yingwen Wang
- Nursing Department, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Weijia Fu
- Medical Information Center, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Yuejie Zhang
- School of Computer Science, Fudan University, Shanghai, 200438, China
| | - Daoyang Wang
- School of Public, Health Fudan University, Shanghai, 200032, China
| | - Ying Gu
- Nursing Department, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Weibing Wang
- School of Public, Health Fudan University, Shanghai, 200032, China
| | - Hong Xu
- Nephrology Department, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Xiaoling Ge
- Statistical and Data Management Center, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Chengjie Ye
- Medical Information Center, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Jinwu Fang
- School of Public, Health Fudan University, Shanghai, 200032, China
| | - Ling Su
- Statistical and Data Management Center, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Jiayu Wang
- National Health Commission Key Laboratory of Neonatal Diseases (Fudan University), Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Wen He
- Respiratory Department, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Xiaobo Zhang
- Respiratory Department, Children's Hospital of Fudan University, Shanghai, 201102, China.
| | - Rui Feng
- School of Computer Science, Fudan University, Shanghai, 200438, China.
- School of Computer Science, Fudan University, 2005 Songhu Road, Shanghai, 200438, China.
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Alvarez-Lozada LA, Arrambide-Garza FJ, Quiroga-Garza A, Huerta-Sanchez MC, Escobar-Luna A, Sada-Treviño MA, Ramos-Proaño CE, Elizondo-Omaña RE. Underdiagnosis of umbilical hernias in CT scans in a multicenter study - the radiologically neglected pathology and its surgical implications. Hernia 2024:10.1007/s10029-024-03079-9. [PMID: 38837076 DOI: 10.1007/s10029-024-03079-9] [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: 04/12/2024] [Accepted: 05/19/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE Umbilical hernias (UH) have a higher prevalence than previously considered. With the high workload radiologists must endure, UH can be missed when interpreting a computed tomography scan (CT). The clinical implications of its misdiagnosis are yet to be determined. Unreporting could lead to content lesions in surgical approaches and other potential complications. The aim was to determine the prevalence of UH using CT scans, and the incidence of radiological reporting. METHODS A multicenter, cross-sectional study was performed in four tertiary-level hospitals. CT scans were reviewed for abdominal wall defects at the umbilicus, and radiological reports were examined to compare findings. In the case of UH, transversal, anteroposterior, and craniocaudal lengths were obtained. RESULTS A total of 1557 CTs were included, from which 971 (62.4%, 95% CI 0.59-0.64) had UH. Out of those, 629 (64.8%, 95% CI 0.61-0.67) of the defects were not included in the radiological report. Smaller UH (x̄: 7.7 × 6.0 mm) were more frequently missed. Of the reported UH, 187 (54.7%) included at least one axis measurement, 289 (84.5%) content description, and 146 (42.7%) whether or not there were complication signs. CONCLUSION There is a high prevalence of UH, and a high incidence of under-reporting. This raises the question of whether this is a population-based finding or the norm worldwide. The reason of under-reporting and the clinical implications of these must be addressed in further studies.
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Affiliation(s)
- Luis Adrian Alvarez-Lozada
- Clinical-Surgical Research Group (GICQx), Human Anatomy Research Group (GIA), Human Anatomy Department, School of Medicine, Universidad Autonoma de Nuevo Leon, Francisco I. Madero y Aguirre Pequeño sin número, Colonia Mitras Centro, Monterrey, Monterrey, Nuevo León, C.P. 64460, México
| | - Francisco Javier Arrambide-Garza
- Clinical-Surgical Research Group (GICQx), Human Anatomy Research Group (GIA), Human Anatomy Department, School of Medicine, Universidad Autonoma de Nuevo Leon, Francisco I. Madero y Aguirre Pequeño sin número, Colonia Mitras Centro, Monterrey, Monterrey, Nuevo León, C.P. 64460, México
| | - Alejandro Quiroga-Garza
- Clinical-Surgical Research Group (GICQx), Human Anatomy Research Group (GIA), Human Anatomy Department, School of Medicine, Universidad Autonoma de Nuevo Leon, Francisco I. Madero y Aguirre Pequeño sin número, Colonia Mitras Centro, Monterrey, Monterrey, Nuevo León, C.P. 64460, México.
- Servicio de Cirugía General, Hospital de Traumatología y Ortopedia No.21, Instituto Mexicano del Seguro Social, Monterrey, Nuevo Leon, Mexico.
| | - Monica Catalina Huerta-Sanchez
- Department of Radiology, School of Medicine, Universidad Autonoma de Nuevo Leon, University Hospital "Dr. Jose Eleuterio Gonzalez", Monterrey, Mexico
| | - Ana Escobar-Luna
- Department of Radiology, Instituto Tecnológico y de Estudios Superiores de Monterrey, Hospital San José Tec Salud, Monterrey, Mexico
| | | | - Carlos Enrique Ramos-Proaño
- Department of Radiology, Instituto Tecnológico y de Estudios Superiores de Monterrey, Hospital San José Tec Salud, Monterrey, Mexico
| | - Rodrigo Enrique Elizondo-Omaña
- Clinical-Surgical Research Group (GICQx), Human Anatomy Research Group (GIA), Human Anatomy Department, School of Medicine, Universidad Autonoma de Nuevo Leon, Francisco I. Madero y Aguirre Pequeño sin número, Colonia Mitras Centro, Monterrey, Monterrey, Nuevo León, C.P. 64460, México.
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Wang Y, Fu W, Gu Y, Fang W, Zhang Y, Jin C, Yin J, Wang W, Xu H, Ge X, Ye C, Tang L, Fang J, Wang D, Su L, Wang J, Zhang X, Feng R. Comparative survey among paediatricians, nurses and health information technicians on ethics implementation knowledge of and attitude towards social experiments based on medical artificial intelligence at children's hospitals in Shanghai: a cross-sectional study. BMJ Open 2023; 13:e071288. [PMID: 37989373 PMCID: PMC10668289 DOI: 10.1136/bmjopen-2022-071288] [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] [Received: 01/16/2023] [Accepted: 11/05/2023] [Indexed: 11/23/2023] Open
Abstract
OBJECTIVES Implementing ethics is crucial to prevent harm and promote widespread benefits in social experiments based on medical artificial intelligence (MAI). However, insufficient information is available concerning this within the paediatric healthcare sector. We aimed to conduct a comparative survey among paediatricians, nurses and health information technicians regarding ethics implementation knowledge of and attitude towards MAI social experiments at children's hospitals in Shanghai. DESIGN AND SETTING A cross-sectional electronic questionnaire was administered from 1 July 2022 to 31 July 2022, at tertiary children's hospitals in Shanghai. PARTICIPANTS All the eligible individuals were recruited. The inclusion criteria were as follows: (1) should be a paediatrician, nurse and health information technician, (2) should have been engaged in or currently participating in social experiments based on MAI, and (3) voluntary participation in the survey. PRIMARY OUTCOME Ethics implementation knowledge of and attitude to MAI social experiments among paediatricians, nurses and health information technicians. RESULTS There were 137 paediatricians, 135 nurses and 60 health information technicians who responded to the questionnaire at tertiary children's hospitals. 2.4-9.6% of participants were familiar with ethics implementation knowledge of MAI social experiments. 31.9-86.1% of participants held an 'agree' ethics implementation attitude. Health information technicians accounted for the highest proportion of the participants who were familiar with the knowledge of implementing ethics, and paediatricians or nurses accounted for the highest proportion among those who held 'agree' attitudes. CONCLUSIONS There is a significant knowledge gap and variations in attitudes among paediatricians, nurses and health information technicians, which underscore the urgent need for individualised education and training programmes to enhance MAI ethics implementation in paediatric healthcare.
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Affiliation(s)
- Yingwen Wang
- Nursing Department, Children's Hospital of Fudan University, Shanghai, China
| | - Weijia Fu
- Medical Information Center, Children's Hospital of Fudan University, Shanghai, China
| | - Ying Gu
- Nursing Department, Children's Hospital of Fudan University, Shanghai, China
| | - Weihan Fang
- Shanghai Pinghe Bilingual School, Shanghai, China
| | - Yuejie Zhang
- School of Computer Science, Fudan University, Shanghai, China
| | - Cheng Jin
- School of Computer Science, Fudan University, Shanghai, China
| | - Jie Yin
- School of Philosophy, Fudan University, Shanghai, China
| | - Weibing Wang
- School of Public Health, Fudan University, Shanghai, China
| | - Hong Xu
- Nephrology Department, Children's Hospital of Fudan University, Shanghai, China
| | - Xiaoling Ge
- Statistical and Data Management Center, Children's Hospital of Fudan University, Shanghai, China
| | - Chengjie Ye
- Medical Information Center, Children's Hospital of Fudan University, Shanghai, China
| | - Liangfeng Tang
- Medical Information Center, Children's Hospital of Fudan University, Shanghai, China
| | - Jinwu Fang
- School of Public Health, Fudan University, Shanghai, China
| | - Daoyang Wang
- School of Computer Science, Fudan University, Shanghai, China
| | - Ling Su
- Children's Hospital of Fudan University, Shanghai, China
| | - Jiayu Wang
- Medical Information Center, Children's Hospital of Fudan University, Shanghai, China
| | - Xiaobo Zhang
- Respiratory Department, Children's Hospital of Fudan University, Shanghai, China
| | - Rui Feng
- Medical Information Center, Children's Hospital of Fudan University, Shanghai, China
- School of Computer Science, Fudan University, Shanghai, China
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Tabatabaian F, Vora SR, Mirabbasi S. Applications, functions, and accuracy of artificial intelligence in restorative dentistry: A literature review. J ESTHET RESTOR DENT 2023; 35:842-859. [PMID: 37522291 DOI: 10.1111/jerd.13079] [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] [Received: 02/01/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 08/01/2023]
Abstract
OBJECTIVE The applications of artificial intelligence (AI) are increasing in restorative dentistry; however, the AI performance is unclear for dental professionals. The purpose of this narrative review was to evaluate the applications, functions, and accuracy of AI in diverse aspects of restorative dentistry including caries detection, tooth preparation margin detection, tooth restoration design, metal structure casting, dental restoration/implant detection, removable partial denture design, and tooth shade determination. OVERVIEW An electronic search was performed on Medline/PubMed, Embase, Web of Science, Cochrane, Scopus, and Google Scholar databases. English-language articles, published from January 1, 2000, to March 1, 2022, relevant to the aforementioned aspects were selected using the key terms of artificial intelligence, machine learning, deep learning, artificial neural networks, convolutional neural networks, clustering, soft computing, automated planning, computational learning, computer vision, and automated reasoning as inclusion criteria. A manual search was also performed. Therefore, 157 articles were included, reviewed, and discussed. CONCLUSIONS Based on the current literature, the AI models have shown promising performance in the mentioned aspects when being compared with traditional approaches in terms of accuracy; however, as these models are still in development, more studies are required to validate their accuracy and apply them to routine clinical practice. CLINICAL SIGNIFICANCE AI with its specific functions has shown successful applications with acceptable accuracy in diverse aspects of restorative dentistry. The understanding of these functions may lead to novel applications with optimal accuracy for AI in restorative dentistry.
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Affiliation(s)
- Farhad Tabatabaian
- Department of Oral Health Sciences, Faculty of Dentistry, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Siddharth R Vora
- Department of Oral Health Sciences, Faculty of Dentistry, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Shahriar Mirabbasi
- Department of Electrical and Computer Engineering, Faculty of Applied Science, The University of British Columbia, Vancouver, British Columbia, Canada
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Ma Z, Wang H, Shan S, Zhu K, Yuan L. Effect of metformin on type 2 diabetes mellitus based on the volume of thyroid nodules tracked by artificial intelligence. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2023. [DOI: 10.1016/j.jrras.2023.100566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Laborie LB, Naidoo J, Pace E, Ciet P, Eade C, Wagner MW, Huisman TAGM, Shelmerdine SC. European Society of Paediatric Radiology Artificial Intelligence taskforce: a new taskforce for the digital age. Pediatr Radiol 2023; 53:576-580. [PMID: 35731260 PMCID: PMC9214669 DOI: 10.1007/s00247-022-05426-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/26/2022] [Accepted: 06/03/2022] [Indexed: 11/08/2022]
Abstract
A new task force dedicated to artificial intelligence (AI) with respect to paediatric radiology was created in 2021 at the International Paediatric Radiology (IPR) meeting in Rome, Italy (a joint society meeting by the European Society of Pediatric Radiology [ESPR] and the Society for Pediatric Radiology [SPR]). The concept of a separate task force dedicated to AI was borne from an ESPR-led international survey of health care professionals' opinions, expectations and concerns regarding AI integration within children's imaging departments. In this survey, the majority (> 80%) of ESPR respondents supported the creation of a task force and helped define our key objectives. These include providing educational content about AI relevant for paediatric radiologists, brainstorming ideas for future projects and collaborating on AI-related studies with respect to collating data sets, de-identifying images and engaging in multi-case, multi-reader studies. This manuscript outlines the starting point of the ESPR AI task force and where we wish to go.
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Affiliation(s)
- Lene Bjerke Laborie
- grid.412008.f0000 0000 9753 1393Department of Radiology, Section for Paediatrics, Haukeland University Hospital, Bergen, Norway
- grid.7914.b0000 0004 1936 7443Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Jaishree Naidoo
- Paediatric Diagnostic Imaging and Envisionit Deep AI, Johannesburg, South Africa
| | - Erika Pace
- grid.5072.00000 0001 0304 893XDepartment of Diagnostic Radiology, The Royal Marsden NHS Foundation Trust, London, UK
| | - Pierluigi Ciet
- grid.5645.2000000040459992XDepartment of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- grid.5645.2000000040459992XDepartment of Pediatric Pulmonology and Allergology, Erasmus MC, Sophia’s Children’s Hospital, Rotterdam, The Netherlands
| | - Christine Eade
- grid.8391.30000 0004 1936 8024University of Exeter Medical School, Exeter, UK
| | - Matthias W. Wagner
- grid.42327.300000 0004 0473 9646Department of Diagnostic Imaging, Division of Neuroradiology, The Hospital for Sick Children, Toronto, Canada
- grid.17063.330000 0001 2157 2938Department of Medical Imaging, University of Toronto, Toronto, Ontario Canada
| | - Thierry A. G. M. Huisman
- grid.39382.330000 0001 2160 926XEdward B. Singleton Department of Radiology, Texas Children’s Hospital, Baylor College of Medicine, Houston, Texas USA
| | - Susan C. Shelmerdine
- grid.424537.30000 0004 5902 9895Department of Clinical Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, Great Ormond Street, WC1H 3JH London, UK
- grid.83440.3b0000000121901201UCL Great Ormond Street Institute of Child Health, London, UK
- grid.451056.30000 0001 2116 3923NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK
- grid.464688.00000 0001 2300 7844Department of Clinical Radiology, St. George’s Hospital, London, UK
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Sun X, Niwa T, Okazaki T, Kameda S, Shibukawa S, Horie T, Kazama T, Uchiyama A, Hashimoto J. Automatic detection of punctate white matter lesions in infants using deep learning of composite images from two cases. Sci Rep 2023; 13:4426. [PMID: 36932141 PMCID: PMC10023755 DOI: 10.1038/s41598-023-31403-3] [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: 06/30/2022] [Accepted: 03/11/2023] [Indexed: 03/19/2023] Open
Abstract
Punctate white matter lesions (PWMLs) in infants may be related to neurodevelopmental outcomes based on the location or number of lesions. This study aimed to assess the automatic detectability of PWMLs in infants on deep learning using composite images created from several cases. To create the initial composite images, magnetic resonance (MR) images of two infants with the most PWMLs were used; their PWMLs were extracted and pasted onto MR images of infants without abnormality, creating many composite PWML images. Deep learning models based on a convolutional neural network, You Only Look Once v3 (YOLOv3), were constructed using the training set of 600, 1200, 2400, and 3600 composite images. As a result, a threshold of detection probability of 20% and 30% for all deep learning model sets yielded a relatively high sensitivity for automatic PWML detection (0.908-0.957). Although relatively high false-positive detections occurred with the lower threshold of detection probability, primarily, in the partial volume of the cerebral cortex (≥ 85.8%), those can be easily distinguished from the white matter lesions. Relatively highly sensitive automatic detection of PWMLs was achieved by creating composite images from two cases using deep learning.
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Affiliation(s)
- Xuyang Sun
- Department of Radiology, Tokai University School of Medicine, 143 Shimokasuka, Isehara, 259-1193, Japan
| | - Tetsu Niwa
- Department of Radiology, Tokai University School of Medicine, 143 Shimokasuka, Isehara, 259-1193, Japan.
| | - Takashi Okazaki
- Department of Radiology, Tokai University School of Medicine, 143 Shimokasuka, Isehara, 259-1193, Japan
| | - Sadanori Kameda
- Department of Radiology, Tokai University School of Medicine, 143 Shimokasuka, Isehara, 259-1193, Japan
| | - Shuhei Shibukawa
- Department of Radiology, Tokai University School of Medicine, 143 Shimokasuka, Isehara, 259-1193, Japan
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, Bunkyo-Ku, Tokyo, Japan
| | - Tomohiko Horie
- Department of Radiology, Tokai University Hospital, Isehara, Japan
| | - Toshiki Kazama
- Department of Radiology, Tokai University School of Medicine, 143 Shimokasuka, Isehara, 259-1193, Japan
| | - Atsushi Uchiyama
- Department of Pediatrics, Tokai University School of Medicine, Isehara, Japan
| | - Jun Hashimoto
- Department of Radiology, Tokai University School of Medicine, 143 Shimokasuka, Isehara, 259-1193, Japan
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Foo LL, Ng WY, Lim GYS, Tan TE, Ang M, Ting DSW. Artificial intelligence in myopia: current and future trends. Curr Opin Ophthalmol 2021; 32:413-424. [PMID: 34310401 DOI: 10.1097/icu.0000000000000791] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PURPOSE OF REVIEW Myopia is one of the leading causes of visual impairment, with a projected increase in prevalence globally. One potential approach to address myopia and its complications is early detection and treatment. However, current healthcare systems may not be able to cope with the growing burden. Digital technological solutions such as artificial intelligence (AI) have emerged as a potential adjunct for myopia management. RECENT FINDINGS There are currently four significant domains of AI in myopia, including machine learning (ML), deep learning (DL), genetics and natural language processing (NLP). ML has been demonstrated to be a useful adjunctive for myopia prediction and biometry for cataract surgery in highly myopic individuals. DL techniques, particularly convoluted neural networks, have been applied to various image-related diagnostic and predictive solutions. Applications of AI in genomics and NLP appear to be at a nascent stage. SUMMARY Current AI research is mainly focused on disease classification and prediction in myopia. Through greater collaborative research, we envision AI will play an increasingly critical role in big data analysis by aggregating a greater variety of parameters including genomics and environmental factors. This may enable the development of generalizable adjunctive DL systems that could help realize predictive and individualized precision medicine for myopic patients.
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Affiliation(s)
- Li Lian Foo
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Wei Yan Ng
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | | | - Tien-En Tan
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Marcus Ang
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Daniel Shu Wei Ting
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
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Li Z, Zhang X, Ding L, Du K, Yan J, Chan MTV, Wu WKK, Li S. Deep learning approach for guiding three-dimensional computed tomography reconstruction of lower limbs for robotically-assisted total knee arthroplasty. Int J Med Robot 2021; 17:e2300. [PMID: 34109730 DOI: 10.1002/rcs.2300] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 06/04/2021] [Accepted: 06/08/2021] [Indexed: 01/09/2023]
Abstract
BACKGROUND Robotic-assisted total knee arthroplasty (TKA) was performed to promote the accuracy of bone resection and mechanical alignment. Among these TKA system procedures, 3D reconstruction of CT data of lower limbs consumes significant manpower. Artificial intelligence (AI) algorithms applying deep learning has been proved efficient in automated identification and visual processing. METHODS CT data of a total of 200 lower limbs scanning were used for AI-based 3D model construction and CT data of 20 lower limbs scanning were utilised for verification. RESULTS We showed that the performance of an AI-guided 3D reconstruction of CT data of lower limbs for robotic-assisted TKA was similar to that of the operator-based approach. The time of 3D lower limb model construction using AI was 4.7 min. AI-based 3D models can be used for surgical planning. CONCLUSION AI was used for the first time to guide the 3D reconstruction of CT data of lower limbs for facilitating robotic-assisted TKA. Incorporation of AI in 3D model reconstruction before TKA might reduce the workload of radiologists.
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Affiliation(s)
- Zheng Li
- Department of Orthopaedic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaofeng Zhang
- BEIJING HURWA-ROBOT Medical Technology Co. Ltd, Beijing, China
| | - Lele Ding
- BEIJING HURWA-ROBOT Medical Technology Co. Ltd, Beijing, China
| | - Kebin Du
- BEIJING HURWA-ROBOT Medical Technology Co. Ltd, Beijing, China
| | - Jun Yan
- BEIJING HURWA-ROBOT Medical Technology Co. Ltd, Beijing, China
| | - Matthew T V Chan
- Department of Anaesthesia and Intensive Care and Peter Hung Pain Research Institute, The Chinese University of Hong Kong, Hong Kong
| | - William K K Wu
- Department of Anaesthesia and Intensive Care and Peter Hung Pain Research Institute, The Chinese University of Hong Kong, Hong Kong.,State Key Laboratory of Digestive Diseases, Centre for Gut Microbiota Research, Institute of Digestive Diseases and LKS Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong
| | - Shugang Li
- Department of Orthopaedic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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