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Marino MJ, Sousa-Pinto B, Lal D. The Application of mHealth and Artificial Intelligence to Chronic Rhinitis. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. IN PRACTICE 2024; 12:1490-1492. [PMID: 38641130 DOI: 10.1016/j.jaip.2024.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 04/10/2024] [Indexed: 04/21/2024]
Affiliation(s)
- Michael J Marino
- Department of Otorhinolaryngology-Head and Neck Surgery, Mayo Clinic Arizona, Phoenix, Ariz
| | - Bernardo Sousa-Pinto
- MEDCIDS-Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal; CINTESIS-Centre for Health Technologies and Services Research, University of Porto, Porto, Portugal
| | - Devyani Lal
- Department of Otorhinolaryngology-Head and Neck Surgery, Mayo Clinic Arizona, Phoenix, Ariz.
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Roberto G, Barberi S, Marseglia GL, Licari A. What's new in pediatric asthma and rhinitis phenotypes and endotypes? Curr Opin Allergy Clin Immunol 2024; 24:73-78. [PMID: 38295125 DOI: 10.1097/aci.0000000000000970] [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: 02/02/2024]
Abstract
PURPOSE OF REVIEW This review explores the evolving landscape of pediatric asthma and rhinitis, focusing on identifying and characterizing different subtypes. RECENT FINDINGS Childhood asthma and rhinitis are prevalent respiratory conditions frequently occurring together. To address the need for a precise definition of these diseases, an unbiased and comprehensive phenotyping approach has been undertaken with hypothesis-free analysis of extensive datasets to uncover new relationships among clinical, environmental, and biological characteristics. On the other hand, the concept of endotype is elaborate and multifaceted, representing distinct pathophysiological mechanisms underlying the clinical presentation and requires the identification of reliable biomarkers. The recognition of multiple inflammatory endotypes underscores the need for in-depth characterization, which could revolutionize the treatment landscape. SUMMARY Comprehending phenotypes and endotypes is crucial for customizing effective and personalized management approaches for children with asthma and rhinitis. More precise and efficient care can be administered through recognition and detailed characterization, ultimately enhancing patients' quality of life.
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Affiliation(s)
- Giulia Roberto
- Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia
- Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia
| | - Salvatore Barberi
- Pediatric Unit, Rho and Garbagnate Milanese Hospital, ASST-Rhodense, Milan, Italy
| | - Gian Luigi Marseglia
- Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia
- Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia
| | - Amelia Licari
- Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia
- Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia
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van Breugel M, Fehrmann RSN, Bügel M, Rezwan FI, Holloway JW, Nawijn MC, Fontanella S, Custovic A, Koppelman GH. Current state and prospects of artificial intelligence in allergy. Allergy 2023; 78:2623-2643. [PMID: 37584170 DOI: 10.1111/all.15849] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/08/2023] [Accepted: 07/31/2023] [Indexed: 08/17/2023]
Abstract
The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.
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Affiliation(s)
- Merlijn van Breugel
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- MIcompany, Amsterdam, the Netherlands
| | - Rudolf S N Fehrmann
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | - Faisal I Rezwan
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- Department of Computer Science, Aberystwyth University, Aberystwyth, UK
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospitals Southampton NHS Foundation Trust, Southampton, UK
| | - Martijn C Nawijn
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Department of Pathology and Medical Biology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Sara Fontanella
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Adnan Custovic
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Gerard H Koppelman
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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Eigenmann P, Akenroye A, Atanaskovic Markovic M, Candotti F, Ebisawa M, Genuneit J, Kalayci Ö, Kollmann D, Leung ASY, Peters RL, Riggioni C. Pediatric Allergy and Immunology (PAI) is for polishing with artificial intelligence, but careful use. Pediatr Allergy Immunol 2023; 34:e14023. [PMID: 37747752 DOI: 10.1111/pai.14023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 08/28/2023] [Indexed: 09/26/2023]
Affiliation(s)
- Philippe Eigenmann
- Pediatric Allergy Unit, Department of Pediatrics, Gynecology and Obstetrics, University Hospitals of Geneva, Geneva, Switzerland
| | - Ayobami Akenroye
- Division of Allergy & Clinical Immunology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | | | - Fabio Candotti
- Division of Allergy and Clinical Immunology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Motohiro Ebisawa
- Clinical Research Center for Allergy and Rheumatology, National Hospital Organization Sagamihara National Hospital, Kanagawa, Japan
| | - Jon Genuneit
- Pediatric Epidemiology, Department of Pediatrics, Medical Faculty, Leipzig University, Leipzig, Germany
| | - Ömer Kalayci
- Department of Pediatric Allergy and Asthma, Hacettepe University School of Medicine, Ankara, Turkey
| | | | - Agnes Sze Yin Leung
- Department of Paediatrics, Faculty of Medicine, The Chinese University of Hong Kong, China
| | - Rachel L Peters
- Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Pediatrics, The University of Melbourne, Parkville, Victoria, Australia
| | - Carmen Riggioni
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore City, Singapore
- Khoo Teck Puat-National University Children's Medical Institute, National University Hospital, National University Health System, Singapore City, Singapore
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关 舒, 刘 殿, 张 庆. [Pediatric oral maxillofacial management and artificial intelligence]. LIN CHUANG ER BI YAN HOU TOU JING WAI KE ZA ZHI = JOURNAL OF CLINICAL OTORHINOLARYNGOLOGY, HEAD, AND NECK SURGERY 2023; 37:658-661. [PMID: 37551576 PMCID: PMC10645521 DOI: 10.13201/j.issn.2096-7993.2023.08.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Indexed: 08/09/2023]
Abstract
With the enhancement of aesthetic awareness of children's oral maxillofacial development, multi-disciplinary doctors pay attention to children's oral maxillofacial management. Artificial intelligence (AI) technology has been gradually applied to all fields of children's oral maxillofacial management because of its outstanding advantages in medical screening and auxiliary decision-making. This article reviews the application of AI technology in the screening, diagnosis, treatment and follow-up of oral maxillofacial management in children.
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Affiliation(s)
- 舒文 关
- 深圳大学总医院 深圳大学临床医学科学院 耳鼻咽喉头颈外科(广东深圳,518055)Department of Otorhinolaryngology Head and Neck Surgery, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, 518055, China
| | - 殿全 刘
- 深圳大学总医院 深圳大学临床医学科学院 耳鼻咽喉头颈外科(广东深圳,518055)Department of Otorhinolaryngology Head and Neck Surgery, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, 518055, China
| | - 庆丰 张
- 深圳大学总医院 深圳大学临床医学科学院 耳鼻咽喉头颈外科(广东深圳,518055)Department of Otorhinolaryngology Head and Neck Surgery, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, 518055, China
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Stafie CS, Sufaru IG, Ghiciuc CM, Stafie II, Sufaru EC, Solomon SM, Hancianu M. Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review. Diagnostics (Basel) 2023; 13:1995. [PMID: 37370890 DOI: 10.3390/diagnostics13121995] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.
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Affiliation(s)
- Celina Silvia Stafie
- Department of Preventive Medicine and Interdisciplinarity, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Irina-Georgeta Sufaru
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Cristina Mihaela Ghiciuc
- Department of Morpho-Functional Sciences II-Pharmacology and Clinical Pharmacology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Ingrid-Ioana Stafie
- Endocrinology Residency Program, Sf. Spiridon Clinical Emergency Hospital, Independentei 1, 700111 Iasi, Romania
| | | | - Sorina Mihaela Solomon
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Monica Hancianu
- Pharmacognosy-Phytotherapy, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
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Pang B, Wang Q, Yang M, Xue M, Zhang Y, Deng X, Zhang Z, Niu W. Identification and Optimization of Contributing Factors for Precocious Puberty by Machine/Deep Learning Methods in Chinese Girls. Front Endocrinol (Lausanne) 2022; 13:892005. [PMID: 35846287 PMCID: PMC9279618 DOI: 10.3389/fendo.2022.892005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/27/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND AND OBJECTIVES As the worldwide secular trends are toward earlier puberty, identification of contributing factors for precocious puberty is critical. We aimed to identify and optimize contributing factors responsible for onset of precocious puberty via machine learning/deep learning algorithms in girls. METHODS A cross-sectional study was performed among girls aged 6-16 years from 26 schools in Beijing based on a cluster sampling method. Information was gleaned online via questionnaires. Machine/deep learning algorithms were performed using Python language (v3.7.6) on PyCharm platform. RESULTS Of 11308 students enrolled, there are 5527 girls, and 408 of them had experienced precocious puberty. Training 13 machine learning algorithms revealed that gradient boosting machine (GBM) performed best in predicting precocious puberty. By comparison, six top factors including maternal age at menarche, paternal body mass index (BMI), waist-to-height ratio, maternal BMI, screen time, and physical activity were sufficient in prediction performance, with accuracy of 0.9530, precision of 0.9818, and area under the receiver operating characteristic curve (AUROC) of 0.7861. The performance of the top six factors was further validated by deep learning sequential model, with accuracy reaching 92.9%. CONCLUSIONS We identified six important factors from both parents and girls that can help predict the onset of precocious puberty among Chinese girls.
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Affiliation(s)
- Bo Pang
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
| | - Qiong Wang
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
| | - Min Yang
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
| | - Mei Xue
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
| | - Yicheng Zhang
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
| | - Xiangling Deng
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
| | - Zhixin Zhang
- International Medical Services, China-Japan Friendship Hospital, Beijing, China
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
- *Correspondence: Wenquan Niu, ; Zhixin Zhang,
| | - Wenquan Niu
- Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
- *Correspondence: Wenquan Niu, ; Zhixin Zhang,
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