Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities

Hasani, Navid; Farhadi, Faraz; Morris, Michael; Nikpanah, Moozhan; Rhamim, Arman; Xu, Yanji; Pariser, Anne; Collins, Michael T.; Summers, Ronald M.; Siegel, Eliot L.; Saboury, Babak

doi:10.1016/j.cpet.2021.09.009

Cited by 25 publications

(27 citation statements)

References 86 publications

(89 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Medical imaging is an equipment-dependent discipline. Medical imaging equipment, especially CT and MR equipment, is driven by the most rapidly developing cutting-edge technology in natural science and is developing at a veritable speed [ 2 – 4 ]. At each stage of the development of medical imaging, it will have an important and sometimes epoch-making impact on other clinical disciplines closely related to it.…”

Section: Introductionmentioning

confidence: 99%

Sports Medical Image Modeling of Injury Prevention in Dance Learning and Sports Training

Fei

2022

Scanning

View full text Add to dashboard Cite

In order to effectively prevent injuries in dance learning and sports training, this paper proposes a method based on sports medical image modeling. This method solves the problem of injury prevention in dance learning by studying the association analysis algorithm, medical image information system, and CT technology and analyzing the role of data mining technology in the medical image information system. The experimental results show that the average prediction error of CT and US is about 5%, which can be considered that the model can predict accurately. The error of MR is as high as 28.2%, and the prediction is relatively inaccurate. Conclusion. the model can effectively prevent the injury in training.

show abstract

Section: Introductionmentioning

confidence: 99%

Sports Medical Image Modeling of Injury Prevention in Dance Learning and Sports Training

Fei

2022

Scanning

View full text Add to dashboard Cite

show abstract

“…To standardize reporting, minimize human bias, expedite management, and most importantly improve outcomes, the use of artificial intelligence (AI) has gained significant prominence in imaging analysis [ 1 – 4 ]. AI-driven methods have gained prominence in diagnosis (as exemplified by Computer-Aided Detection (CADe) systems and Computer-Aided Diagnosis (CADx) systems) for tuberculosis [ 5 , 6 ], lung cancer [ 7 , 8 ] and metastatic disease to the brain[ 9 ] and also been applied to multiple other areas of clinical need [ 4 ] including notably infectious diseases as described in the context of COVID-19 [ 10 – 12 ], internal medicine[ 13 ], diabetic retinopathy [ 14 ]. In oncology, AI methods are being explored in most cancer types including prominently in lung [ 8 , 15 ], breast [ 16 , 17 ], prostate [ 18 ], central nervous system cancers [ 13 , 19 – 26 ], and other malignancies [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

“…AI-driven methods have gained prominence in diagnosis (as exemplified by Computer-Aided Detection (CADe) systems and Computer-Aided Diagnosis (CADx) systems) for tuberculosis [ 5 , 6 ], lung cancer [ 7 , 8 ] and metastatic disease to the brain[ 9 ] and also been applied to multiple other areas of clinical need [ 4 ] including notably infectious diseases as described in the context of COVID-19 [ 10 – 12 ], internal medicine[ 13 ], diabetic retinopathy [ 14 ]. In oncology, AI methods are being explored in most cancer types including prominently in lung [ 8 , 15 ], breast [ 16 , 17 ], prostate [ 18 ], central nervous system cancers [ 13 , 19 – 26 ], and other malignancies [ 27 ]. There is ongoing progress in employing AI methods towards imaging for oncology treatment e.g., generating radiation therapy volumes [ 28 ], assessing treatment response [ 29 ], and understanding and communicating prognosis [ 19 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models

AV¹,

Zhuge²,

Zhao³

et al. 2022

J Biotechnol Biomed

View full text Add to dashboard Cite

The interpretation of imaging in medicine in general and in oncology specifically remains problematic due to several limitations which include the need to incorporate detailed clinical history, patient and disease-specific history, clinical exam features, previous and ongoing treatment, and account for the dependency on reproducible human interpretation of multiple factors with incomplete data linkage. To standardize reporting, minimize bias, expedite management, and improve outcomes, the use of Artificial Intelligence (AI) has gained significant prominence in imaging analysis. In oncology, AI methods have as a result been explored in most cancer types with ongoing progress in employing AI towards imaging for oncology treatment, assessing treatment response, and understanding and communicating prognosis. Challenges remain with limited available data sets, variability in imaging changes over time augmented by a growing heterogeneity in analysis approaches. We review the imaging analysis workflow and examine how hand-crafted features also referred to as traditional Machine Learning (ML), Deep Learning (DL) approaches, and hybrid analyses, are being employed in AI-driven imaging analysis in central nervous system tumors. ML, DL, and hybrid approaches coexist, and their combination may produce superior results although data in this space is as yet novel, and conclusions and pitfalls have yet to be fully explored. We note the growing technical complexities that may become increasingly separated from the clinic and enforce the acute need for clinician engagement to guide progress and ensure that conclusions derived from AI-driven imaging analysis reflect that same level of scrutiny lent to other avenues of clinical research.

show abstract

“…Nesse sentido, indo de encontro com o propósito dessa pesquisa, Hasani et al, (2022) afirmam que a Inteligência Artificial (IA) pode abrir portas para compreensão de uma grande quantidade de dados de pacientes, desempenhando um papel na tomada de decisões clínicas.…”

Section: As Principais Causasunclassified

“…Esse argumento também apoia a noção de que as informações sobre o uso de dados do paciente por terceiros, devem fazer parte do formulário de consentimento informado. Formulários de consentimento livre e esclarecido publicamente disponíveis, podem fornecer benefícios substanciais e proteção legal (Hasani et al, 2022).…”

Section: Introductionunclassified

Análise visual aplicada em processos de judicialização de doenças raras do estado de São Paulo

Dias¹

View full text Add to dashboard Cite

AGRADECIMENTOSA Deus, pelo dom da vida. Aos meus pais, Ivani Camilo Dias e Izabel de Souza Dias, pelo apoio incondicional. Ao meu orientador, Professor Doutor Evandro Marcos Saidel Ribeiro, por seu apoio, dedicação, competência e atenção especial na construção e revisão do trabalho, os quais foram fundamentais para a execução e conclusão dessa tese e para meu crescimento como discente, docente e pesquisadora.

show abstract

Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities

Cited by 25 publications

References 86 publications

Sports Medical Image Modeling of Injury Prevention in Dance Learning and Sports Training

Sports Medical Image Modeling of Injury Prevention in Dance Learning and Sports Training

AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models

Análise visual aplicada em processos de judicialização de doenças raras do estado de São Paulo

Contact Info

Product

Resources

About