Article
Radiology, Nuclear Medicine & Medical Imaging
Kyung-Sik Ahn, Byeonguk Bae, Woo Young Jang, Jin Hyuck Lee, Saelin Oh, Baek Hyun Kim, Si Wook Lee, Hae Woon Jung, Jae Won Lee, Jinkyeong Sung, Kyu-Hwan Jung, Chang Ho Kang, Soon Hyuck Lee
Summary: The study aimed to evaluate the performance of a deep neural network model in assessing rapidly advancing bone age during puberty using elbow radiographs, and found that the model's bone age estimation results were similar to those of experts.
EUROPEAN RADIOLOGY
(2021)
Review
Radiology, Nuclear Medicine & Medical Imaging
Femke C. R. Staal, Else A. Aalbersberg, Daphne van der Velden, Erica A. Wilthagen, Margot E. T. Tesselaar, Regina G. H. Beets-Tan, Monique Maas
Summary: This systematic review provides an overview of the current evidence of radiomics for clinical outcome measures in gastroenteropancreatic neuroendocrine tumours (GEP-NETs). Most studies focus on GEP-NET grade and differential diagnosis, while research on treatment response and long-term outcomes is lacking. Promising models have been developed, but evidence for clinically relevant aims is still limited.
EUROPEAN RADIOLOGY
(2022)
Review
Ophthalmology
Ikram Brahim, Mathieu Lamard, Anas-Alexis Benyoussef, Gwenole Quellec
Summary: This review examines the diagnosis methods of dry eye disease (DED), exploring the incorporation of automation. The diagnostic methods are categorized into classical, semi-automated, and promising AI-based automated methods.
CLINICAL AND EXPERIMENTAL OPHTHALMOLOGY
(2022)
Review
Computer Science, Artificial Intelligence
A. S. Albahri, Ali M. Duhaim, Mohammed A. Fadhel, Alhamzah Alnoor, Noor S. Baqer, Laith Alzubaidi, O. S. Albahri, A. H. Alamoodi, Jinshuai Bai, Asma Salhi, Jose Santamaria, Chun Ouyang, Ashish Gupta, Yuantong Gu, Muhammet Deveci
Summary: In recent years, there has been a significant shift in the healthcare sector towards embracing artificial intelligence (AI) to improve disease diagnosis accuracy and mitigate health risks. However, the development of trustworthy and explainable AI (XAI) in healthcare is still in its early stages. This study provides a systematic review of the trustworthiness and explainability of AI applications in healthcare, focusing on quality, bias risk, and data fusion, to offer more accurate insights and recommendations.
INFORMATION FUSION
(2023)
Review
Computer Science, Artificial Intelligence
Abdullahi B. Saka, Lukumon O. Oyedele, Lukman A. Akanbi, Sikiru A. Ganiyu, Daniel W. M. Chan, Sururah A. Bello
Summary: The idea of developing a system that can converse and understand human languages has been around since the 1200s. With the advancement in artificial intelligence (AI), Conversational AI came of age in 2010 with the launch of Apple's Siri. Despite being applied in sectors like aviation, tourism, and healthcare, the application of Conversational AI in the architecture engineering and construction (AEC) industry is lagging. A systematic review of Conversational AI in the AEC industry reveals its immense benefits and highlights the challenges and opportunities that need attention for further research and development. This study serves as the first attempt in the AEC field, providing insights into a fast-emerging research area.
ADVANCED ENGINEERING INFORMATICS
(2023)
Review
Computer Science, Artificial Intelligence
Amanda Lans, Robertus J. B. Pierik, John R. Bales, Mitchell S. Fourman, David Shin, Laura N. Kanbier, Jack Rifkin, William H. DiGiovanni, Rohan R. Chopra, Rana Moeinzad, Jorrit-Jan Verlaan, Joseph H. Schwab
Summary: The study aimed to evaluate the completeness of the CLAIM checklist and bias risk according to the QUADAS-2 tool for ML-based orthopaedic diagnostic imaging models. It found limited reporting of important information, such as handling missing data and data preprocessing steps by diagnostic ML studies for orthopaedic imaging, as well as a substantial number of works at high risk of bias. Future studies should adhere to acknowledged methodological standards to maximize the quality and applicability of their models.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2022)
Review
Radiology, Nuclear Medicine & Medical Imaging
Susan C. Shelmerdine, Richard D. White, Hantao Liu, Owen J. Arthurs, Neil J. Sebire
Summary: Most research and commercial efforts have focused on using artificial intelligence (AI) for fracture detection in adults, but the long-term clinical and medicolegal implications of missed fractures in children are more significant. This study assessed the available literature on the diagnostic performance of AI tools for pediatric fracture assessment on imaging, and compared this with the performance of human readers. The literature exhibits wide heterogeneity and limited information on algorithm performance on external datasets, making it difficult to understand how these tools may generalize to a wider pediatric population.
INSIGHTS INTO IMAGING
(2022)
Article
Computer Science, Interdisciplinary Applications
Shuting Xu, Ravinesh Deo, Jeffrey Soar, Prabal Datta Barua, Oliver Faust, Nusrat Homaira, Adam Jaffe, Arm Luthful Kabir, U. Rajendra Acharya
Summary: This study investigates the role of automated detection of obstructive airway diseases in reducing cost and improving diagnostic quality. Medical imaging, genetics, and physiological signals are the main sources used for disease detection. Machine Learning is more prevalent than Deep Learning in the field, with Convolutional Neural Network being a common DL classifier and Support Vector Machine being widely used in ML.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Multidisciplinary Sciences
Lei Jin, Tianyang Sun, Xi Liu, Zehong Cao, Yan Liu, Hong Chen, Yixin Ma, Jun Zhang, Yaping Zou, Yingchao Liu, Feng Shi, Dinggang Shen, Jinsong Wu
Summary: Accurate pathological classification and grading of gliomas is crucial in clinical diagnosis and treatment. In this study, deep learning techniques were used for automated histological pathology diagnosis of gliomas. The model showed high accuracy in both internal validation and multi-center testing.
Review
Engineering, Environmental
Ayilobeni Kikon, Paresh Chandra Deka
Summary: Drought is a natural disaster that causes serious damage to the economy, society, and the environment. It is difficult to define drought due to its complexity and slow onset. Researchers have conducted various studies on drought and found that Artificial Intelligence techniques play a significant role in drought assessment, monitoring, management, and forecasting.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Review
Food Science & Technology
Larissa Oliveira Chaves, Ana Luiza Gomes Domingos, Daniel Louzada Fernandes, Fabio Ribeiro Cerqueira, Rodrigo Siqueira-Batista, Josefina Bressan
Summary: The evaluation of food intake plays a crucial role in scientific research and clinical practice. Artificial intelligence tools, such as machine learning algorithms, are increasingly being used to assess food intake. This systematic review identified studies that used machine learning algorithms to evaluate food intake in various populations, highlighting the predominance of supervised learning algorithms.
CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION
(2023)
Review
Food Science & Technology
Zhenjiao Du, Wenfei Tian, Michael Tilley, Donghai Wang, Guorong Zhang, Yonghui Li
Summary: This review provides a comprehensive overview of the methodology and applications of near-infrared spectroscopy (NIRS), emphasizing its importance and potential in wheat quality assessment. While NIRS has achieved significant progress in the quantitative determination of analytical parameters, challenges remain in determining rheological parameters and end product quality. Future research should focus on model development, integrating more data and techniques, and improving calibration methods.
COMPREHENSIVE REVIEWS IN FOOD SCIENCE AND FOOD SAFETY
(2022)
Review
Nutrition & Dietetics
Jaroslaw Sak, Magdalena Suchodolska
Summary: Artificial intelligence is increasingly being utilized in biomedical, clinical, and nutritional epidemiology research in the fields of medicine and nutrition science. Different AI methods, such as artificial neural networks, machine learning algorithms, and deep learning algorithms, are applied in various aspects of nutrient studies. AI technology has the potential to revolutionize personalized nutrient supply and monitoring through the development of dietary systems.
Review
Health Care Sciences & Services
Shern Ping Choy, Byung Jin Kim, Alexandra Paolino, Wei Ren Tan, Sarah Man Lin Lim, Jessica Seo, Sze Ping Tan, Luc Francis, Teresa Tsakok, Michael Simpson, Jonathan N. W. N. Barker, Magnus D. Lynch, Mark S. Corbett, Catherine H. Smith, Satveer K. Mahil
Summary: Skin diseases affect a significant portion of the global population and pose a major healthcare burden. Utilizing deep learning for analyzing skin images can optimize healthcare workflows, but current research has limitations in methodology and reporting.
NPJ DIGITAL MEDICINE
(2023)
Review
Medicine, General & Internal
John S. Brownstein, Benjamin Rader, Christina M. Astley, Huaiyu Tian
Summary: This article discusses the application of AI and machine-learning tools in identifying and tracking disease outbreaks, as well as monitoring mitigation strategies.
NEW ENGLAND JOURNAL OF MEDICINE
(2023)