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Dimitri P, Savage MO. Artificial intelligence in paediatric endocrinology: conflict or cooperation. J Pediatr Endocrinol Metab 2024; 37:209-221. [PMID: 38183676 DOI: 10.1515/jpem-2023-0554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 12/18/2023] [Indexed: 01/08/2024]
Abstract
Artificial intelligence (AI) in medicine is transforming healthcare by automating system tasks, assisting in diagnostics, predicting patient outcomes and personalising patient care, founded on the ability to analyse vast datasets. In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; bone age assessment and thyroid nodule screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome, acromegaly, congenital adrenal hyperplasia and Turner syndrome. AI can also predict those most at risk from childhood obesity by stratifying future interventions to modify lifestyle. AI will facilitate personalised healthcare by integrating data from 'omics' analysis, lifestyle tracking, medical history, laboratory and imaging, therapy response and treatment adherence from multiple sources. As data acquisition and processing becomes fundamental, data privacy and protecting children's health data is crucial. Minimising algorithmic bias generated by AI analysis for rare conditions seen in paediatric endocrinology is an important determinant of AI validity in clinical practice. AI cannot create the patient-doctor relationship or assess the wider holistic determinants of care. Children have individual needs and vulnerabilities and are considered in the context of family relationships and dynamics. Importantly, whilst AI provides value through augmenting efficiency and accuracy, it must not be used to replace clinical skills.
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Affiliation(s)
- Paul Dimitri
- Department of Paediatric Endocrinology, Sheffield Children's NHS Foundation Trust, Sheffield, UK
| | - Martin O Savage
- Centre for Endocrinology, William Harvey Research Institute, Barts and the London School of Medicine & Dentistry, Queen Mary University of London, London, UK
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Wang S, Liang S, Chang Q, Zhang L, Gong B, Bai Y, Zuo F, Wang Y, Xie X, Gu Y. STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning. Diagnostics (Basel) 2024; 14:497. [PMID: 38472969 DOI: 10.3390/diagnostics14050497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/25/2024] [Accepted: 02/01/2024] [Indexed: 03/14/2024] Open
Abstract
Accurate tooth segmentation and numbering are the cornerstones of efficient automatic dental diagnosis and treatment. In this paper, a multitask learning architecture has been proposed for accurate tooth segmentation and numbering in panoramic X-ray images. A graph convolution network was applied for the automatic annotation of the target region, a modified convolutional neural network-based detection subnetwork (DSN) was used for tooth recognition and boundary regression, and an effective region segmentation subnetwork (RSSN) was used for region segmentation. The features extracted using RSSN and DSN were fused to optimize the quality of boundary regression, which provided impressive results for multiple evaluation metrics. Specifically, the proposed framework achieved a top F1 score of 0.9849, a top Dice metric score of 0.9629, and an mAP (IOU = 0.5) score of 0.9810. This framework holds great promise for enhancing the clinical efficiency of dentists in tooth segmentation and numbering tasks.
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Affiliation(s)
- Shaofeng Wang
- Department of Orthodontics, Beijing Stomatological Hospital, Capital Medical University, Beijing 100050, China
| | - Shuang Liang
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
- Laboratory for Clinical Medicine, Capital Medical University, Beijing 100069, China
- Beijing Key Laboratory of Fundamicationental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing 100069, China
| | - Qiao Chang
- Department of Orthodontics, Beijing Stomatological Hospital, Capital Medical University, Beijing 100050, China
| | - Li Zhang
- Department of Orthodontics, Beijing Stomatological Hospital, Capital Medical University, Beijing 100050, China
| | - Beiwen Gong
- Department of Orthodontics, Beijing Stomatological Hospital, Capital Medical University, Beijing 100050, China
| | - Yuxing Bai
- Department of Orthodontics, Beijing Stomatological Hospital, Capital Medical University, Beijing 100050, China
- Laboratory for Clinical Medicine, Capital Medical University, Beijing 100069, China
| | - Feifei Zuo
- LargeV Instrument Corp., Ltd., Beijing 100084, China
| | - Yajie Wang
- LargeV Instrument Corp., Ltd., Beijing 100084, China
| | - Xianju Xie
- Department of Orthodontics, Beijing Stomatological Hospital, Capital Medical University, Beijing 100050, China
- Laboratory for Clinical Medicine, Capital Medical University, Beijing 100069, China
| | - Yu Gu
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
- Laboratory for Clinical Medicine, Capital Medical University, Beijing 100069, China
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