scholarly journals Machine Learning automatic assessment for glaucoma and myopia based on Corvis ST data

2022 ◽  
Vol 196 ◽  
pp. 454-460
Author(s):  
Danilo Leite ◽  
Maria Campelos ◽  
Ana Fernandes ◽  
Pedro Batista ◽  
João Beirão ◽  
...  
Keyword(s):  
Machine Learning ◽  
Automatic Assessment ◽  
Corvis St

scholarly journals Implementing Machine Learning in Interventional Cardiology: The Benefits are Worth the Trouble

2021 ◽  
Author(s):  
Walid Ben Ali ◽  
Ahmad Pesaranghader ◽  
Robert Avram ◽  
Reda Ibrahim ◽  
Thomas Modine ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
High Performance ◽  
Interventional Cardiology ◽  
Daily Practice ◽  
Automated Analysis ◽  
Coronary Intervention ◽  
Biomedical Data ◽  
Clinical Implementation ◽  
Automatic Assessment

Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed.

Künstliche Intelligenz zur Entwicklung von Screening-Parametern im Bereich der kornealen Biomechanik

2019 ◽  
Vol 236 (12) ◽  
pp. 1423-1427 ◽  
Author(s):  
Sven Reisdorf
Keyword(s):  
Machine Learning ◽  
Künstliche Intelligenz ◽  
Corvis St ◽  
Screening Parameter

ZusammenfassungMachine Learning stellt insbesondere dann eine sinnvolle Alternative dar, wenn eine Datenanalyse mit wissensbasierten analytischen Methoden sehr aufwendig und schwierig ist. In solchen Fällen bietet sich auch eine Kombination aus analytischen Methoden und empirischen Methoden mittels künstlicher Intelligenz (KI) an. Die Entwicklung verschiedener Auswertefunktionen des Corvis ST ist hierfür ein konkretes Beispiel. In diesem Beitrag wird die Entwicklung dreier Screening-Parameter mittels KI beschrieben. Der Artikel zeigt, wie diese Entwicklungen im Bereich der Erkennung von klinischem und subklinischem Keratokonus sowie Glaukom-Screening klinisch hilfreich sind.

scholarly journals Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble

2021 ◽  
Vol 8 ◽  
Author(s):  
Walid Ben Ali ◽  
Ahmad Pesaranghader ◽  
Robert Avram ◽  
Pavel Overtchouk ◽  
Nils Perrin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
High Performance ◽  
Interventional Cardiology ◽  
Daily Practice ◽  
Automated Analysis ◽  
Coronary Intervention ◽  
Biomedical Data ◽  
Clinical Implementation ◽  
Automatic Assessment

Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep Learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed.

scholarly journals Will the Machine Like Your Image?Automatic Assessment of Beauty in Images with Machine Learning Techniques †

Inventions ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 34 ◽  
Author(s):  
Matteo Bodini
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Processing System ◽  
Machine Learning Techniques ◽  
Community Studies ◽  
Automatic Assessment ◽  
Future Directions ◽  
Computer Methods ◽  
Learning Techniques ◽  
Work Done

Although the concept of image quality has been a subject of study for the image processing community for more than forty years (where, with the term “quality”, we are referring to the accuracy with which an image processing system captures, processes, stores, compresses, transmits, and displays the signals that compose an image), notions related to aesthetics of photographs and images have only appeared for about ten years within the community. Studies devoted to aesthetics of images are multiplying today, taking advantage of the latest machine learning techniques and mostly due to the proliferation of huge communities and websites, specialized in digital photography sharing and archiving, such as Flickr, Imgur, DeviantArt, and Instagram. In this review, we examine the latest advances of computer methods that aim at computationally distinguishing high-quality from low-quality photos and images, relying on machine learning techniques. The paper is organized as follows: First, we introduce many approaches to aesthetics, studied in philosophy, neurobiology, experimental psychology, and sociology, to see what lighting they propose to researchers. Such points of view let us explain the weakness of the current consensus on the difficult aesthetics problem and the importance of the ongoing debates on it. Then, we analyze the work done in the community of pattern recognition and artificial intelligence on the task of automatic aesthetic assessment, and we both compare and critically examine the presented results. Finally, we describe many issues that have not been addressed, and starting from these, we outline some possible future directions.

scholarly journals Emotion Classification Based on Biophysical Signals and Machine Learning Techniques

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 21 ◽  
Author(s):  
Oana Bălan ◽  
Gabriela Moise ◽  
Livia Petrescu ◽  
Alin Moldoveanu ◽  
Marius Leordeanu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Affective Computing ◽  
Machine Learning Techniques ◽  
Emotion Classification ◽  
Automatic Assessment ◽  
Cognitive Changes ◽  
Video Signals ◽  
Learning Techniques ◽  
Education Marketing

Emotions constitute an indispensable component of our everyday life. They consist of conscious mental reactions towards objects or situations and are associated with various physiological, behavioral, and cognitive changes. In this paper, we propose a comparative analysis between different machine learning and deep learning techniques, with and without feature selection, for binarily classifying the six basic emotions, namely anger, disgust, fear, joy, sadness, and surprise, into two symmetrical categorical classes (emotion and no emotion), using the physiological recordings and subjective ratings of valence, arousal, and dominance from the DEAP (Dataset for Emotion Analysis using EEG, Physiological and Video Signals) database. The results showed that the maximum classification accuracies for each emotion were: anger: 98.02%, joy:100%, surprise: 96%, disgust: 95%, fear: 90.75%, and sadness: 90.08%. In the case of four emotions (anger, disgust, fear, and sadness), the classification accuracies were higher without feature selection. Our approach to emotion classification has future applicability in the field of affective computing, which includes all the methods used for the automatic assessment of emotions and their applications in healthcare, education, marketing, website personalization, recommender systems, video games, and social media.

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