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Spielberg DR, Weinman J, DeBoer EM. Advancements in imaging in ChILD. Pediatr Pulmonol 2024; 59:2276-2285. [PMID: 37222402 DOI: 10.1002/ppul.26487] [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: 11/30/2022] [Revised: 05/08/2023] [Accepted: 05/09/2023] [Indexed: 05/25/2023]
Abstract
Interstitial and diffuse lung diseases in children constitute a range of congenital and acquired disorders. These disorders present with signs and symptoms of respiratory disease accompanied by diffuse radiographic changes. In many cases, radiographic findings are nonspecific, while in other disorders, chest computed tomography (CT) is diagnostic in the appropriate context. Regardless, chest imaging remains central in the evaluation of the patient with suspected childhood interstitial lung disease (chILD). Several newly described chILD entities, spanning both genetic and acquired etiologies, have imaging that aid in their diagnoses. Advances in CT scanning technology and CT analysis techniques continue to improve scan quality as well as expand use of chest CT as a research tool. Finally, ongoing research is expanding use of imaging modalities without ionizing radiation. Magnetic resonance imaging is being applied to investigate pulmonary structure and function, and ultrasound of the lung and pleura is a novel technique with an emerging role in chILD disorders. This review describes the current state of imaging in chILD including recently described diagnoses, advances in conventional imaging techniques and applications, and evolving new imaging modalities that expand the clinical and research roles for imaging in these disorders.
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Affiliation(s)
- David R Spielberg
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Jason Weinman
- Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Emily M DeBoer
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
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Muralidharan V, Schamroth J, Youssef A, Celi LA, Daneshjou R. Applied artificial intelligence for global child health: Addressing biases and barriers. PLOS DIGITAL HEALTH 2024; 3:e0000583. [PMID: 39172772 PMCID: PMC11340888 DOI: 10.1371/journal.pdig.0000583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
Given the potential benefits of artificial intelligence and machine learning (AI/ML) within healthcare, it is critical to consider how these technologies can be deployed in pediatric research and practice. Currently, healthcare AI/ML has not yet adapted to the specific technical considerations related to pediatric data nor adequately addressed the specific vulnerabilities of children and young people (CYP) in relation to AI. While the greatest burden of disease in CYP is firmly concentrated in lower and middle-income countries (LMICs), existing applied pediatric AI/ML efforts are concentrated in a small number of high-income countries (HICs). In LMICs, use-cases remain primarily in the proof-of-concept stage. This narrative review identifies a number of intersecting challenges that pose barriers to effective AI/ML for CYP globally and explores the shifts needed to make progress across multiple domains. Child-specific technical considerations throughout the AI/ML lifecycle have been largely overlooked thus far, yet these can be critical to model effectiveness. Governance concerns are paramount, with suitable national and international frameworks and guidance required to enable the safe and responsible deployment of advanced technologies impacting the care of CYP and using their data. An ambitious vision for child health demands that the potential benefits of AI/Ml are realized universally through greater international collaboration, capacity building, strong oversight, and ultimately diffusing the AI/ML locus of power to empower researchers and clinicians globally. In order that AI/ML systems that do not exacerbate inequalities in pediatric care, teams researching and developing these technologies in LMICs must ensure that AI/ML research is inclusive of the needs and concerns of CYP and their caregivers. A broad, interdisciplinary, and human-centered approach to AI/ML is essential for developing tools for healthcare workers delivering care, such that the creation and deployment of ML is grounded in local systems, cultures, and clinical practice. Decisions to invest in developing and testing pediatric AI/ML in resource-constrained settings must always be part of a broader evaluation of the overall needs of a healthcare system, considering the critical building blocks underpinning effective, sustainable, and cost-efficient healthcare delivery for CYP.
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Affiliation(s)
- Vijaytha Muralidharan
- Department of Dermatology, Stanford University, Stanford, California, United States of America
| | - Joel Schamroth
- Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Alaa Youssef
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Department of Radiology, Stanford University, Stanford, California, United States of America
| | - Leo A. Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Roxana Daneshjou
- Department of Dermatology, Stanford University, Stanford, California, United States of America
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
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Demirbaş KC, Yıldız M, Saygılı S, Canpolat N, Kasapçopur Ö. Artificial Intelligence in Pediatrics: Learning to Walk Together. Turk Arch Pediatr 2024; 59:121-130. [PMID: 38454219 PMCID: PMC11059951 DOI: 10.5152/turkarchpediatr.2024.24002] [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: 01/03/2024] [Accepted: 02/02/2024] [Indexed: 03/09/2024]
Abstract
In this era of rapidly advancing technology, artificial intelligence (AI) has emerged as a transformative force, even being called the Fourth Industrial Revolution, along with gene editing and robotics. While it has undoubtedly become an increasingly important part of our daily lives, it must be recognized that it is not an additional tool, but rather a complex concept that poses a variety of challenges. AI, with considerable potential, has found its place in both medical care and clinical research. Within the vast field of pediatrics, it stands out as a particularly promising advancement. As pediatricians, we are indeed witnessing the impactful integration of AI-based applications into our daily clinical practice and research efforts. These tools are being used for simple to more complex tasks such as diagnosing clinically challenging conditions, predicting disease outcomes, creating treatment plans, educating both patients and healthcare professionals, and generating accurate medical records or scientific papers. In conclusion, the multifaceted applications of AI in pediatrics will increase efficiency and improve the quality of healthcare and research. However, there are certain risks and threats accompanying this advancement including the biases that may contribute to health disparities and, inaccuracies. Therefore, it is crucial to recognize and address the technical, ethical, and legal challenges as well as explore the benefits in both clinical and research fields.
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Affiliation(s)
- Kaan Can Demirbaş
- İstanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, İstanbul, Turkey
| | - Mehmet Yıldız
- Department of Pediatric Rheumatology, İstanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, İstanbul, Turkey
| | - Seha Saygılı
- Department of Pediatric Nephrology, İstanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, İstanbul, Turkey
| | - Nur Canpolat
- Department of Pediatric Nephrology, İstanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, İstanbul, Turkey
| | - Özgür Kasapçopur
- Department of Pediatric Rheumatology, İstanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, İstanbul, Turkey
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Yu HR, Hsu JH. Editorial: Emerging pneumonia and acute lower respiratory infections in children, volume II. Front Pediatr 2024; 12:1372533. [PMID: 38361999 PMCID: PMC10867324 DOI: 10.3389/fped.2024.1372533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 02/17/2024] Open
Affiliation(s)
- Hong-Ren Yu
- Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Jong-Hau Hsu
- Department of Pediatrics, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Pediatrics, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
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Naidoo J, Shelmerdine SC, -Charcape CFU, Sodhi AS. Artificial Intelligence in Paediatric Tuberculosis. Pediatr Radiol 2023; 53:1733-1745. [PMID: 36707428 PMCID: PMC9883137 DOI: 10.1007/s00247-023-05606-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/07/2022] [Accepted: 01/13/2023] [Indexed: 01/29/2023]
Abstract
Tuberculosis (TB) continues to be a leading cause of death in children despite global efforts focused on early diagnosis and interventions to limit the spread of the disease. This challenge has been made more complex in the context of the coronavirus pandemic, which has disrupted the "End TB Strategy" and framework set out by the World Health Organization (WHO). Since the inception of artificial intelligence (AI) more than 60 years ago, the interest in AI has risen and more recently we have seen the emergence of multiple real-world applications, many of which relate to medical imaging. Nonetheless, real-world AI applications and clinical studies are limited in the niche area of paediatric imaging. This review article will focus on how AI, or more specifically deep learning, can be applied to TB diagnosis and management in children. We describe how deep learning can be utilised in chest imaging to provide computer-assisted diagnosis to augment workflow and screening efforts. We also review examples of recent AI applications for TB screening in resource constrained environments and we explore some of the challenges and the future directions of AI in paediatric TB.
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Affiliation(s)
- Jaishree Naidoo
- Envisionit Deep AI LTD, Coveham House, Downside Bridge Road, Cobham, KT11 3 EP, UK.
| | - Susan Cheng Shelmerdine
- Department of Clinical Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
- Great Ormond Street Hospital for Children, UCL Great Ormond Street Institute of Child Health, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK
| | - Carlos F Ugas -Charcape
- Department of Diagnostic Imaging, Instituto Nacional de Salud del Niño San Borja, Lima, Peru
| | - Arhanjit Singh Sodhi
- Department of Computer Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
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Balla Y, Tirunagari S, Windridge D. Pediatrics in Artificial Intelligence Era: A Systematic Review on Challenges, Opportunities, and Explainability. Indian Pediatr 2023; 60:561-569. [PMID: 37424120 DOI: 10.1007/s13312-023-2936-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2023]
Abstract
BACKGROUND The emergence of artificial intelligence (AI) tools such as ChatGPT and Bard is disrupting a broad swathe of fields, including medicine. In pediatric medicine, AI is also increasingly being used across multiple subspecialties. However, the practical application of AI still faces a number of key challenges. Consequently, there is a requirement for a concise overview of the roles of AI across the multiple domains of pediatric medicine, which the current study seeks to address. AIM To systematically assess the challenges, opportunities, and explainability of AI in pediatric medicine. METHODOLOGY A systematic search was carried out on peer-reviewed databases, PubMed Central, Europe PubMed Central, and grey literature using search terms related to machine learning (ML) and AI for the years 2016 to 2022 in the English language. A total of 210 articles were retrieved that were screened with PRISMA for abstract, year, language, context, and proximal relevance to research aims. A thematic analysis was carried out to extract findings from the included studies. RESULTS Twenty articles were selected for data abstraction and analysis, with three consistent themes emerging from these articles. In particular, eleven articles address the current state-of-the-art application of AI in diagnosing and predicting health conditions such as behavioral and mental health, cancer, syndromic and metabolic diseases. Five articles highlight the specific challenges of AI deployment in pediatric medicines: data security, handling, authentication, and validation. Four articles set out future opportunities for AI to be adapted: the incorporation of Big Data, cloud computing, precision medicine, and clinical decision support systems. These studies collectively critically evaluate the potential of AI in overcoming current barriers to adoption. CONCLUSION AI is proving disruptive within pediatric medicine and is presently associated with challenges, opportunities, and the need for explainability. AI should be viewed as a tool to enhance and support clinical decision-making rather than a substitute for human judgement and expertise. Future research should consequently focus on obtaining comprehensive data to ensure the generalizability of research findings.
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Affiliation(s)
- Yashaswini Balla
- Neurosciences Department, Alder Hey Children's NHS Foundation Trust, Liverpool, United Kingdom
| | - Santosh Tirunagari
- Department of Psychology, Middlesex University, London, United Kingdom. Correspondence to: Dr Santosh Tirunagari, Department of Psychology, Middlesex University, London, United Kingdom.
| | - David Windridge
- Department of Computer Science, Middlesex University, London, United Kingdom
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Sugibayashi T, Walston SL, Matsumoto T, Mitsuyama Y, Miki Y, Ueda D. Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis. Eur Respir Rev 2023; 32:32/168/220259. [PMID: 37286217 DOI: 10.1183/16000617.0259-2022] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/16/2023] [Indexed: 06/09/2023] Open
Abstract
BACKGROUND Deep learning (DL), a subset of artificial intelligence (AI), has been applied to pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been performed. METHODS A search of multiple electronic databases through September 2022 was performed to identify studies that applied DL for pneumothorax diagnosis using imaging. Meta-analysis via a hierarchical model to calculate the summary area under the curve (AUC) and pooled sensitivity and specificity for both DL and physicians was performed. Risk of bias was assessed using a modified Prediction Model Study Risk of Bias Assessment Tool. RESULTS In 56 of the 63 primary studies, pneumothorax was identified from chest radiography. The total AUC was 0.97 (95% CI 0.96-0.98) for both DL and physicians. The total pooled sensitivity was 84% (95% CI 79-89%) for DL and 85% (95% CI 73-92%) for physicians and the pooled specificity was 96% (95% CI 94-98%) for DL and 98% (95% CI 95-99%) for physicians. More than half of the original studies (57%) had a high risk of bias. CONCLUSIONS Our review found the diagnostic performance of DL models was similar to that of physicians, although the majority of studies had a high risk of bias. Further pneumothorax AI research is needed.
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Affiliation(s)
- Takahiro Sugibayashi
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Shannon L Walston
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Toshimasa Matsumoto
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
- Smart Life Science Lab, Center for Health Science Innovation, Osaka Metropolitan University, Osaka, Japan
| | - Yasuhito Mitsuyama
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
- Smart Life Science Lab, Center for Health Science Innovation, Osaka Metropolitan University, Osaka, Japan
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Data harnessing to nurture the human mind for a tailored approach to the child. Pediatr Res 2023; 93:357-365. [PMID: 36180585 DOI: 10.1038/s41390-022-02320-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 07/06/2022] [Accepted: 09/12/2022] [Indexed: 11/08/2022]
Abstract
Big data in pediatrics is an ocean of structured and unstructured data. Big data analysis helps to dive into the ocean of data to filter out information that can guide pediatricians in their decision making, precision diagnosis, and targeted therapy. In addition, big data and its analysis have helped in the surveillance, prevention, and performance of the health system. There has been a considerable amount of work in pediatrics that we have tried to highlight in this review and some of it has been already incorporated into the health system. Work in specialties of pediatrics is still forthcoming with the creation of a common data model and amalgamation of the huge "omics" database. The physicians entrusted with the care of children must be aware of the outcome so that they can play a role to ensure that big data algorithms have a clinically relevant effect in improving the health of their patients. They will apply the outcome of big data and its analysis in patient care through clinical algorithms or with the help of embedded clinical support alerts from the electronic medical records. IMPACT: Big data in pediatrics include structured, unstructured data, waveform data, biological, and social data. Big data analytics has unraveled significant information from these databases. This is changing how pediatricians will look at the body of available evidence and translate it into their clinical practice. Data harnessed so far is implemented in certain fields while in others it is in the process of development to become a clinical adjunct to the physician. Common databases are being prepared for future work. Diagnostic and prediction models when incorporated into the health system will guide the pediatrician to a targeted approach to diagnosis and therapy.
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Prediction of Bronchopneumonia Inpatients' Total Hospitalization Expenses Based on BP Neural Network and Support Vector Machine Models. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9275801. [PMID: 35633928 PMCID: PMC9132643 DOI: 10.1155/2022/9275801] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/13/2022] [Accepted: 05/05/2022] [Indexed: 01/09/2023]
Abstract
Objective BP neural network (BPNN) model and support vector machine (SVM) model were used to predict the total hospitalization expenses of patients with bronchopneumonia. Methods A total of 355 patients with bronchopneumonia from January 2018 to December 2020 were collected and sorted out. The data set was randomly divided into a training set (n = 249) and a test set (n = 106) according to 7 : 3. The BPNN model and SVM model were constructed to analyze the predictors of total hospitalization expenses. The effectiveness was compared between these two prediction models. Results The top three influencing factors and their importance for predicting total hospitalization cost by the BPNN model were hospitalization days (0.477), age (0.154), and discharge department (0.083). The top 3 factors predicted by the SVM model were hospitalization days (0.215), age (0.196), and marital status (0.172). The area under the curve of these two models is 0.838 (95% CI: 0.755~0.921) and 0.889 (95% CI: 0.819~0.959), respectively. Conclusion Both the BPNN model and SVM model can predict the total hospitalization expenses of patients with bronchopneumonia, but the prediction effect of the SVM model is better than the BPNN model.
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Vaezipour N, Fritschi N, Brasier N, Bélard S, Domínguez J, Tebruegge M, Portevin D, Ritz N. Towards Accurate Point-of-Care Tests for Tuberculosis in Children. Pathogens 2022; 11:pathogens11030327. [PMID: 35335651 PMCID: PMC8949489 DOI: 10.3390/pathogens11030327] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/02/2022] [Accepted: 03/03/2022] [Indexed: 12/20/2022] Open
Abstract
In childhood tuberculosis (TB), with an estimated 69% of missed cases in children under 5 years of age, the case detection gap is larger than in other age groups, mainly due to its paucibacillary nature and children’s difficulties in delivering sputum specimens. Accurate and accessible point-of-care tests (POCTs) are needed to detect TB disease in children and, in turn, reduce TB-related morbidity and mortality in this vulnerable population. In recent years, several POCTs for TB have been developed. These include new tools to improve the detection of TB in respiratory and gastric samples, such as molecular detection of Mycobacterium tuberculosis using loop-mediated isothermal amplification (LAMP) and portable polymerase chain reaction (PCR)-based GeneXpert. In addition, the urine-based detection of lipoarabinomannan (LAM), as well as imaging modalities through point-of-care ultrasonography (POCUS), are currently the POCTs in use. Further to this, artificial intelligence-based interpretation of ultrasound imaging and radiography is now integrated into computer-aided detection products. In the future, portable radiography may become more widely available, and robotics-supported ultrasound imaging is currently being trialed. Finally, novel blood-based tests evaluating the immune response using “omic-“techniques are underway. This approach, including transcriptomics, metabolomic, proteomics, lipidomics and genomics, is still distant from being translated into POCT formats, but the digital development may rapidly enhance innovation in this field. Despite these significant advances, TB-POCT development and implementation remains challenged by the lack of standard ways to access non-sputum-based samples, the need to differentiate TB infection from disease and to gain acceptance for novel testing strategies specific to the conditions and settings of use.
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Affiliation(s)
- Nina Vaezipour
- Mycobacterial and Migrant Health Research Group, University Children’s Hospital Basel, Department for Clinical Research, University of Basel, 4056 Basel, Switzerland; (N.V.); (N.F.)
- Infectious Disease and Vaccinology Unit, University Children’s Hospital Basel, University of Basel, 4056 Basel, Switzerland
| | - Nora Fritschi
- Mycobacterial and Migrant Health Research Group, University Children’s Hospital Basel, Department for Clinical Research, University of Basel, 4056 Basel, Switzerland; (N.V.); (N.F.)
| | - Noé Brasier
- Department of Health Sciences and Technology, Institute for Translational Medicine, ETH Zurich, 8093 Zurich, Switzerland;
- Department of Digitalization & ICT, University Hospital Basel, 4031 Basel, Switzerland
| | - Sabine Bélard
- Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité–Universitätsmedizin Berlin, 13353 Berlin, Germany;
- Institute of Tropical Medicine and International Health, Charité–Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - José Domínguez
- Institute for Health Science Research Germans Trias i Pujol. CIBER Enfermedades Respiratorias, Universitat Autònoma de Barcelona, 08916 Barcelona, Spain;
| | - Marc Tebruegge
- Department of Infection, Immunity and Inflammation, UCL Great Ormond Street Institute of Child Health, University College London, London WCN1 1EH, UK;
- Department of Pediatrics, The Royal Children’s Hospital Melbourne, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Damien Portevin
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, 4123 Allschwil, Switzerland;
- University of Basel, 4001 Basel, Switzerland
| | - Nicole Ritz
- Mycobacterial and Migrant Health Research Group, University Children’s Hospital Basel, Department for Clinical Research, University of Basel, 4056 Basel, Switzerland; (N.V.); (N.F.)
- Department of Pediatrics, The Royal Children’s Hospital Melbourne, The University of Melbourne, Parkville, VIC 3052, Australia
- Department of Paediatrics and Paediatric Infectious Diseases, Children’s Hospital, Lucerne Cantonal Hospital, 6000 Lucerne, Switzerland
- Correspondence: ; Tel.: +41-61-704-1212
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Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective. Pediatr Radiol 2022; 52:2120-2130. [PMID: 34471961 PMCID: PMC8409695 DOI: 10.1007/s00247-021-05146-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/22/2021] [Accepted: 06/28/2021] [Indexed: 12/19/2022]
Abstract
Artificial intelligence (AI) applications for chest radiography and chest CT are among the most developed applications in radiology. More than 40 certified AI products are available for chest radiography or chest CT. These AI products cover a wide range of abnormalities, including pneumonia, pneumothorax and lung cancer. Most applications are aimed at detecting disease, complemented by products that characterize or quantify tissue. At present, none of the thoracic AI products is specifically designed for the pediatric population. However, some products developed to detect tuberculosis in adults are also applicable to children. Software is under development to detect early changes of cystic fibrosis on chest CT, which could be an interesting application for pediatric radiology. In this review, we give an overview of current AI products in thoracic radiology and cover recent literature about AI in chest radiography, with a focus on pediatric radiology. We also discuss possible pediatric applications.
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Artificial intelligence in paediatric radiology: international survey of health care professionals' opinions. Pediatr Radiol 2022; 52:30-41. [PMID: 34642789 DOI: 10.1007/s00247-021-05195-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 07/04/2021] [Accepted: 08/18/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND The nature of paediatric radiology work poses several challenges for developing and implementing artificial intelligence (AI) tools, but opinions of those working in the field are currently unknown. OBJECTIVE To evaluate the attitudes and perceptions toward AI amongst health care professionals working within children's imaging services. MATERIALS AND METHODS A web-based questionnaire was distributed to the membership of several paediatric and general radiological societies over a 4-month period (1 Feb - 31 May 2020). Survey questions covered attitudes toward AI in general, future impacts and suggested areas for development specifically within paediatric imaging. RESULTS Two hundred and forty responses were collected with the majority being from radiologists (159/240, 66.3%; 95% confidence interval [CI] 59.8-72.2%) or allied health care professionals (72/240, 31.3%; 95% CI 25.4-37.5%). Respondents agreed that AI could potentially alert radiologists to imaging abnormalities (148/240, 61.7%; 95% CI 55.2-67.9%) but preferred that results were checked by a human (200/240, 83.3%; 95% CI 78.0-87.8%). The majority did not believe jobs in paediatric radiology would be replaced by AI (205/240, 85.4%; 95% CI 80.3-89.6%) and that the development of AI tools should focus on improved diagnostic accuracy (77/240, 32.1%; 95% CI 26.2-38.4%), workflow efficiencies (60/240, 25.0%; 95% CI 19.7-30.9%) and patient safety (54/240, 22.5%; 95% CI 17.4-28.3%). The majority of European Society of Paediatric Radiology (ESPR) members (67/81, 82.7%; 95% CI 72.7-90.2%) welcomed the idea of a dedicated paediatric radiology AI task force with emphasis on educational events and anonymised dataset curation. CONCLUSION Imaging health care professionals working with children had a positive outlook regarding the use of AI in paediatric radiology, and did not feel their jobs were threatened. Future AI tools would be most beneficial for easily automated tasks and most practitioners welcomed the opportunity for further AI educational activities.
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Wang B, Zhang W. MARnet: multi-scale adaptive residual neural network for chest X-ray images recognition of lung diseases. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:331-350. [PMID: 34902994 DOI: 10.3934/mbe.2022017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Chest X-ray image is an important clinical diagnostic reference to lung diseases that is a serious threat to human health. At present, with the rapid development of computer vision and deep learning technology, many scholars have carried out the fruitful research on how to build a valid model for chest X-ray images recognition of lung diseases. While some efforts are still expected to improve the performance of the recognition model and enhance the interpretability of the recognition results. In this paper, we construct a multi-scale adaptive residual neural network (MARnet) to identify chest X-ray images of lung diseases. To make the model better extract image features, we cross-transfer the information extracted by residual block and the information extracted by adaptive structure to different layer, avoiding the reduction effect of residual structure on adaptive function. We compare MARnet with some classical neural networks, and the results show that MARnet achieves accuracy (ACC) of 83.3% and the area under ROC curve (AUC) of 0.97 in the identification of 4 kinds of typical lung X-ray images including nodules, atelectasis, normal and infection, which are higher than those of other methods. Moreover, to avoid the randomness of the train-test-split method, 5-fold cross-validation method is used to verify the generalization ability of the MARnet model and the results are satisfactory. Finally, the technique called Gradient-weighted Class Activation Mapping (Grad-CAM), is adopted to display significantly the discriminative regions of the images in the form of the heat map, which provides an explainable and more direct clinical diagnostic reference to lung diseases.
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Affiliation(s)
- Boyang Wang
- School of Computer Science and Software Engineering, University of Science and Technology, Liaoning 114044, China
| | - Wenyu Zhang
- School of Computer Science and Software Engineering, University of Science and Technology, Liaoning 114044, China
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14
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Steele RW. Pediatric quality measures: The leap from process to outcomes. Curr Probl Pediatr Adolesc Health Care 2021; 51:101065. [PMID: 34518131 DOI: 10.1016/j.cppeds.2021.101065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Value-based reimbursement arrangements tie financial incentives to achieving quality measures to ensure savings are not from withholding care. For patients and their families, the delivery of high-quality care is simply the expectation. Defining and measuring pediatric quality, however, is not standardized which has led to a large proliferation of metrics across multiple stakeholders. The majority of these measures are process rather than outcomes metrics often chosen for the ease at which the data can be obtained. In order to drive greater value, outcomes measures should be preferentially selected. However, measuring outcomes in children presents multiple unique challenges. Compared to adults, children are generally healthier, their outcomes may take more time to manifest, and their clinical variability is greater. Another challenge is the amount of healthcare data being generated by providers, provider networks, payors, government agencies, and many others. This should help in understanding pediatric quality outcomes, but the massive volume of data requires new analytic tools. Artificial intelligence techniques such as machine learning offer faster, more precise, and larger scale evaluation of quality outcomes. Its implementation necessitates identifying expertise in the way of data scientists as well as additional infrastructure components to evaluate data governance, security, regulatory compliance, and ethics. Despite these prerequisites, much progress is being made in outcome insights that drive value benefiting children and families.
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Affiliation(s)
- Robert W Steele
- EVP/Chief Strategy and Innovation Officer, Children's Mercy Kansas City, United States.
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15
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Çallı E, Sogancioglu E, van Ginneken B, van Leeuwen KG, Murphy K. Deep learning for chest X-ray analysis: A survey. Med Image Anal 2021; 72:102125. [PMID: 34171622 DOI: 10.1016/j.media.2021.102125] [Citation(s) in RCA: 106] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/17/2021] [Accepted: 05/27/2021] [Indexed: 12/14/2022]
Abstract
Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a particularly important modality for which a variety of applications have been researched. The release of multiple, large, publicly available chest X-ray datasets in recent years has encouraged research interest and boosted the number of publications. In this paper, we review all studies using deep learning on chest radiographs published before March 2021, categorizing works by task: image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation. Detailed descriptions of all publicly available datasets are included and commercial systems in the field are described. A comprehensive discussion of the current state of the art is provided, including caveats on the use of public datasets, the requirements of clinically useful systems and gaps in the current literature.
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Affiliation(s)
- Erdi Çallı
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands.
| | - Ecem Sogancioglu
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands
| | - Bram van Ginneken
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands
| | - Kicky G van Leeuwen
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands
| | - Keelin Murphy
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands
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16
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Hlabangana LT, Elsingergy M, Ahmed A, Boschoff PE, Goodier M, Bove M, Andronikou S. Inter-rater reliability in quality assurance (QA) of pediatric chest X-rays. J Med Imaging Radiat Sci 2021; 52:427-434. [PMID: 33958315 DOI: 10.1016/j.jmir.2021.04.002] [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: 12/12/2020] [Revised: 03/25/2021] [Accepted: 04/09/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE The goal of the study is to determine the inter-rater agreement on multiple factors that were utilized to evaluate the quality of pediatric chest X-ray exams from different levels of healthcare provision in an African setting. METHODS The image quality of pediatric chest X-rays from 3 South African medical centers of varying level of healthcare service were retrospectively assessed by 3 raters for 12 quality factors including: (1) absent body parts; (2) under inspiration; (3) patient rotation; (4) scapula in the way; (5) patient kyphosis/lordosis; (6) artefact/foreign body; (7) central vessel visualization; (8) peripheral vessels visualization; (9) poor collimation; and (10) trachea and bronchi visualization; (11) post-cardiac vessel visualization; and (12) absent or wrong image orientation. Analysis was performed using the Brennan--Prediger coefficient of agreement for inter-rater reliability and Cochran's Q statistic and McNemar's test for inter-rater bias. RESULTS 1077 X-rays were reviewed. The least difference between observers in the frequency of the errors was noticed for factors (1) absent body parts and (12) absent or wrong image orientation with almost perfect agreement between raters. κ score for these two factors among all raters and between each pair of raters was more than 0.95 with no significant inter-rater bias. Conversely, there was poor agreement for the remaining factors with the least agreed on being factor (3) patient rotation with a κ score of 0.23. This was followed by factors (2) under inspiration (κ score of 0.32) and factors (4) scapula in the way (κ score of 0.35) respectively. There was significant inter-rater bias for all these three factors. CONCLUSION Many of the factors used to assess the quality of a chest X-ray in children demonstrate poor reliability despite mitigation against variations in training, standard quality definitions and level of healthcare service provision. New definitions, objective measures and recording tools for assessing pediatric chest radiographic quality are required.
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Affiliation(s)
- Linda Tebogo Hlabangana
- University of the Witwatersrand School of Clinical Medicine, Faculty of Health Sciences, Johannesburg, South Africa
| | - Mohamed Elsingergy
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Aadil Ahmed
- Bayradiology Private Practice, St George's Hospital, Porte Elizabeth, Eastern Cape Province, South Africa
| | - Peter Ernst Boschoff
- Wits Donald Gordon Medical Center, TJ Nel Radiologists Inc., Johannesburg, Gauteng Province, South Africa
| | - Matthew Goodier
- University of KwaZulu-Natal, Greys Hospital, Pietermaritzburg, KwaZulu-Natal Province, South Africa
| | - Michele Bove
- Burger Radiologists Inc., Arwyp Medical Center, Johannesburg, Gauteng Province, South Africa
| | - Savvas Andronikou
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, USA; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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