Multimodal AI System for the Rapid Diagnosis and Surgical Prediction of Necrotizing Enterocolitis

The rapid diagnosis and surgical prediction of necrotizing enterocolitis (NEC) remain a challenge because its complex pathogenesis has not been completely elucidated, and no single medical examination is specific for diagnosing NEC. Artificial intelligence (AI) has proven the robustness of multivari...

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Autores principales:	Wenjing Gao, Yuanyuan Pei, Huiying Liang, Junjian Lv, Jiale Chen, Wei Zhong
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Abdominal x-ray AI diagnosis multimodal necrotizing enterocolitis surgery Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/665e8ba208514539b0fe4badd670bc03
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spelling	oai:doaj.org-article:665e8ba208514539b0fe4badd670bc032021-11-10T00:00:38ZMultimodal AI System for the Rapid Diagnosis and Surgical Prediction of Necrotizing Enterocolitis2169-353610.1109/ACCESS.2021.3069191https://doaj.org/article/665e8ba208514539b0fe4badd670bc032021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9388699/https://doaj.org/toc/2169-3536The rapid diagnosis and surgical prediction of necrotizing enterocolitis (NEC) remain a challenge because its complex pathogenesis has not been completely elucidated, and no single medical examination is specific for diagnosing NEC. Artificial intelligence (AI) has proven the robustness of multivariate analysis and been widely used in the diagnosis of complex diseases in the past decade. In this paper, a new multimodal AI system including feature engineering, machine learning (ML), and deep learning (DL) was constructed based on abdominal radiographs (ARs) and clinical data. A total of 4,535 ARs from 1,823 suspected NEC patients were analyzed by transfer learning, and then medical images and clinical parameters from 827 suspected NEC patients were used to train, validate, and test the AI system. Our results demonstrated that the system was effective in diagnosing NEC. In addition, the clinical datasets obtained one week before surgery from 379 NEC patients were studied by the multimodal AI system, and the results showed that it was capable of predicting which NEC patients required surgery. We compared the results in external test sets with those made by clinicians and found that the diagnostic and surgical predictive ability of the AI system was equivalent to that of experienced clinicians. This multimodal AI system can help clinicians improve diagnostic efficiency, reduce the number of missed diagnoses, and facilitate early diagnosis and treatment to prevent disease progression or even death.Wenjing GaoYuanyuan PeiHuiying LiangJunjian LvJiale ChenWei ZhongIEEEarticleAbdominal x-rayAIdiagnosismultimodalnecrotizing enterocolitissurgeryElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 51050-51064 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Abdominal x-ray AI diagnosis multimodal necrotizing enterocolitis surgery Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Abdominal x-ray AI diagnosis multimodal necrotizing enterocolitis surgery Electrical engineering. Electronics. Nuclear engineering TK1-9971 Wenjing Gao Yuanyuan Pei Huiying Liang Junjian Lv Jiale Chen Wei Zhong Multimodal AI System for the Rapid Diagnosis and Surgical Prediction of Necrotizing Enterocolitis
description	The rapid diagnosis and surgical prediction of necrotizing enterocolitis (NEC) remain a challenge because its complex pathogenesis has not been completely elucidated, and no single medical examination is specific for diagnosing NEC. Artificial intelligence (AI) has proven the robustness of multivariate analysis and been widely used in the diagnosis of complex diseases in the past decade. In this paper, a new multimodal AI system including feature engineering, machine learning (ML), and deep learning (DL) was constructed based on abdominal radiographs (ARs) and clinical data. A total of 4,535 ARs from 1,823 suspected NEC patients were analyzed by transfer learning, and then medical images and clinical parameters from 827 suspected NEC patients were used to train, validate, and test the AI system. Our results demonstrated that the system was effective in diagnosing NEC. In addition, the clinical datasets obtained one week before surgery from 379 NEC patients were studied by the multimodal AI system, and the results showed that it was capable of predicting which NEC patients required surgery. We compared the results in external test sets with those made by clinicians and found that the diagnostic and surgical predictive ability of the AI system was equivalent to that of experienced clinicians. This multimodal AI system can help clinicians improve diagnostic efficiency, reduce the number of missed diagnoses, and facilitate early diagnosis and treatment to prevent disease progression or even death.
format	article
author	Wenjing Gao Yuanyuan Pei Huiying Liang Junjian Lv Jiale Chen Wei Zhong
author_facet	Wenjing Gao Yuanyuan Pei Huiying Liang Junjian Lv Jiale Chen Wei Zhong
author_sort	Wenjing Gao
title	Multimodal AI System for the Rapid Diagnosis and Surgical Prediction of Necrotizing Enterocolitis
title_short	Multimodal AI System for the Rapid Diagnosis and Surgical Prediction of Necrotizing Enterocolitis
title_full	Multimodal AI System for the Rapid Diagnosis and Surgical Prediction of Necrotizing Enterocolitis
title_fullStr	Multimodal AI System for the Rapid Diagnosis and Surgical Prediction of Necrotizing Enterocolitis
title_full_unstemmed	Multimodal AI System for the Rapid Diagnosis and Surgical Prediction of Necrotizing Enterocolitis
title_sort	multimodal ai system for the rapid diagnosis and surgical prediction of necrotizing enterocolitis
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/665e8ba208514539b0fe4badd670bc03
work_keys_str_mv	AT wenjinggao multimodalaisystemfortherapiddiagnosisandsurgicalpredictionofnecrotizingenterocolitis AT yuanyuanpei multimodalaisystemfortherapiddiagnosisandsurgicalpredictionofnecrotizingenterocolitis AT huiyingliang multimodalaisystemfortherapiddiagnosisandsurgicalpredictionofnecrotizingenterocolitis AT junjianlv multimodalaisystemfortherapiddiagnosisandsurgicalpredictionofnecrotizingenterocolitis AT jialechen multimodalaisystemfortherapiddiagnosisandsurgicalpredictionofnecrotizingenterocolitis AT weizhong multimodalaisystemfortherapiddiagnosisandsurgicalpredictionofnecrotizingenterocolitis
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Multimodal AI System for the Rapid Diagnosis and Surgical Prediction of Necrotizing Enterocolitis

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