Your browser doesn't support javascript.
loading
Artificial intelligence in COPD CT images: identification, staging, and quantitation.
Wu, Yanan; Xia, Shuyue; Liang, Zhenyu; Chen, Rongchang; Qi, Shouliang.
Afiliación
  • Wu Y; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
  • Xia S; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
  • Liang Z; Respiratory Department, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China.
  • Chen R; Key Laboratory of Medicine and Engineering for Chronic Obstructive Pulmonary Disease in Liaoning Province, Shenyang, China.
  • Qi S; State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Respir Res ; 25(1): 319, 2024 Aug 22.
Article en En | MEDLINE | ID: mdl-39174978
ABSTRACT
Chronic obstructive pulmonary disease (COPD) stands as a significant global health challenge, with its intricate pathophysiological manifestations often demanding advanced diagnostic strategies. The recent applications of artificial intelligence (AI) within the realm of medical imaging, especially in computed tomography, present a promising avenue for transformative changes in COPD diagnosis and management. This review delves deep into the capabilities and advancements of AI, particularly focusing on machine learning and deep learning, and their applications in COPD identification, staging, and imaging phenotypes. Emphasis is laid on the AI-powered insights into emphysema, airway dynamics, and vascular structures. The challenges linked with data intricacies and the integration of AI in the clinical landscape are discussed. Lastly, the review casts a forward-looking perspective, highlighting emerging innovations in AI for COPD imaging and the potential of interdisciplinary collaborations, hinting at a future where AI doesn't just support but pioneers breakthroughs in COPD care. Through this review, we aim to provide a comprehensive understanding of the current state and future potential of AI in shaping the landscape of COPD diagnosis and management.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Tomografía Computarizada por Rayos X / Enfermedad Pulmonar Obstructiva Crónica Límite: Humans Idioma: En Revista: Respir Res Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Tomografía Computarizada por Rayos X / Enfermedad Pulmonar Obstructiva Crónica Límite: Humans Idioma: En Revista: Respir Res Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido