101
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Durve M, Orsini S, Tiribocchi A, Montessori A, Tucny JM, Lauricella M, Camposeo A, Pisignano D, Succi S. Benchmarking YOLOv5 and YOLOv7 models with DeepSORT for droplet tracking applications. THE EUROPEAN PHYSICAL JOURNAL. E, SOFT MATTER 2023; 46:32. [PMID: 37154834 PMCID: PMC10167152 DOI: 10.1140/epje/s10189-023-00290-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 04/15/2023] [Indexed: 05/10/2023]
Abstract
Tracking droplets in microfluidics is a challenging task. The difficulty arises in choosing a tool to analyze general microfluidic videos to infer physical quantities. The state-of-the-art object detector algorithm You Only Look Once (YOLO) and the object tracking algorithm Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) are customizable for droplet identification and tracking. The customization includes training YOLO and DeepSORT networks to identify and track the objects of interest. We trained several YOLOv5 and YOLOv7 models and the DeepSORT network for droplet identification and tracking from microfluidic experimental videos. We compare the performance of the droplet tracking applications with YOLOv5 and YOLOv7 in terms of training time and time to analyze a given video across various hardware configurations. Despite the latest YOLOv7 being 10% faster, the real-time tracking is only achieved by lighter YOLO models on RTX 3070 Ti GPU machine due to additional significant droplet tracking costs arising from the DeepSORT algorithm. This work is a benchmark study for the YOLOv5 and YOLOv7 networks with DeepSORT in terms of the training time and inference time for a custom dataset of microfluidic droplets.
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Affiliation(s)
- Mihir Durve
- Center for Life Nano- & Neuro-Science, Fondazione Istituto Italiano di Tecnologia (IIT), viale Regina Elena 295, 00161, Rome, Italy.
| | - Sibilla Orsini
- NEST, Istituto Nanoscienze-CNR and Scuola Normale Superiore, Piazza San Silvestro 12, 56127, Pisa, Italy
- Istituto per le Applicazioni del Calcolo del Consiglio Nazionale delle Ricerche, via dei Taurini 19, 00185, Rome, Italy
| | - Adriano Tiribocchi
- Istituto per le Applicazioni del Calcolo del Consiglio Nazionale delle Ricerche, via dei Taurini 19, 00185, Rome, Italy
| | - Andrea Montessori
- Dipartimento di Ingegneria Civile, Informatica e delle Tecnologie Aeronautiche, Università degli Studi Roma TRE, via Vito Volterra 62, Rome, 00146, Italy
| | - Jean-Michel Tucny
- Center for Life Nano- & Neuro-Science, Fondazione Istituto Italiano di Tecnologia (IIT), viale Regina Elena 295, 00161, Rome, Italy
- Dipartimento di Ingegneria Civile, Informatica e delle Tecnologie Aeronautiche, Università degli Studi Roma TRE, via Vito Volterra 62, Rome, 00146, Italy
| | - Marco Lauricella
- Istituto per le Applicazioni del Calcolo del Consiglio Nazionale delle Ricerche, via dei Taurini 19, 00185, Rome, Italy
| | - Andrea Camposeo
- NEST, Istituto Nanoscienze-CNR and Scuola Normale Superiore, Piazza San Silvestro 12, 56127, Pisa, Italy
| | - Dario Pisignano
- NEST, Istituto Nanoscienze-CNR and Scuola Normale Superiore, Piazza San Silvestro 12, 56127, Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, Largo B. Pontecorvo 3, 56127, Pisa, Italy
| | - Sauro Succi
- Center for Life Nano- & Neuro-Science, Fondazione Istituto Italiano di Tecnologia (IIT), viale Regina Elena 295, 00161, Rome, Italy
- Physics Department, Harvard University, 17 Oxford Street, Cambridge, MA, 02138, USA
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102
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Nagipogu RT, Fu D, Reif JH. A survey on molecular-scale learning systems with relevance to DNA computing. NANOSCALE 2023; 15:7676-7694. [PMID: 37066980 DOI: 10.1039/d2nr06202j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
DNA computing has emerged as a promising alternative to achieve programmable behaviors in chemistry by repurposing the nucleic acid molecules into chemical hardware upon which synthetic chemical programs can be executed. These chemical programs are capable of simulating diverse behaviors, including boolean logic computation, oscillations, and nanorobotics. Chemical environments such as the cell are marked by uncertainty and are prone to random fluctuations. For this reason, potential DNA-based molecular devices that aim to be deployed into such environments should be capable of adapting to the stochasticity inherent in them. In keeping with this goal, a new subfield has emerged within DNA computing, focusing on developing approaches that embed learning and inference into chemical reaction systems. If realized in biochemical contexts, such molecular machines can engender novel applications in fields such as biotechnology, synthetic biology, and medicine. Therefore, it would be beneficial to review how different ideas were conceived, how the progress has been so far, and what the emerging ideas are in this nascent field of 'molecular-scale learning'.
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Affiliation(s)
| | - Daniel Fu
- Department of Computer Science, Duke University, Durham, NC, USA.
| | - John H Reif
- Department of Computer Science, Duke University, Durham, NC, USA.
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103
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Zhu Y, Li J, Kim J, Li S, Zhao Y, Bahari J, Eliahoo P, Li G, Kawakita S, Haghniaz R, Gao X, Falcone N, Ermis M, Kang H, Liu H, Kim H, Tabish T, Yu H, Li B, Akbari M, Emaminejad S, Khademhosseini A. Skin-interfaced electronics: A promising and intelligent paradigm for personalized healthcare. Biomaterials 2023; 296:122075. [PMID: 36931103 PMCID: PMC10085866 DOI: 10.1016/j.biomaterials.2023.122075] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/23/2023] [Accepted: 03/02/2023] [Indexed: 03/09/2023]
Abstract
Skin-interfaced electronics (skintronics) have received considerable attention due to their thinness, skin-like mechanical softness, excellent conformability, and multifunctional integration. Current advancements in skintronics have enabled health monitoring and digital medicine. Particularly, skintronics offer a personalized platform for early-stage disease diagnosis and treatment. In this comprehensive review, we discuss (1) the state-of-the-art skintronic devices, (2) material selections and platform considerations of future skintronics toward intelligent healthcare, (3) device fabrication and system integrations of skintronics, (4) an overview of the skintronic platform for personalized healthcare applications, including biosensing as well as wound healing, sleep monitoring, the assessment of SARS-CoV-2, and the augmented reality-/virtual reality-enhanced human-machine interfaces, and (5) current challenges and future opportunities of skintronics and their potentials in clinical translation and commercialization. The field of skintronics will not only minimize physical and physiological mismatches with the skin but also shift the paradigm in intelligent and personalized healthcare and offer unprecedented promise to revolutionize conventional medical practices.
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Affiliation(s)
- Yangzhi Zhu
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States.
| | - Jinghang Li
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Jinjoo Kim
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Shaopei Li
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Yichao Zhao
- Interconnected and Integrated Bioelectronics Lab, Department of Electrical and Computer Engineering, and Materials Science and Engineering, University of California, Los Angeles, CA, 90095, United States
| | - Jamal Bahari
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Payam Eliahoo
- Biomedical Engineering Department, University of Southern California, Los Angeles, CA, 90007, United States
| | - Guanghui Li
- The Centre of Nanoscale Science and Technology and Key Laboratory of Functional Polymer Materials, Institute of Polymer Chemistry, College of Chemistry, Nankai University, Tianjin, 300071, China; Renewable Energy Conversion and Storage Center (RECAST), Nankai University, Tianjin, 300071, China
| | - Satoru Kawakita
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Reihaneh Haghniaz
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Xiaoxiang Gao
- Department of Nanoengineering, University of California, San Diego, La Jolla, CA, 92093, United States
| | - Natashya Falcone
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Menekse Ermis
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Heemin Kang
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Hao Liu
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, PR China
| | - HanJun Kim
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States; College of Pharmacy, Korea University, Sejong, 30019, Republic of Korea
| | - Tanveer Tabish
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Haidong Yu
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China
| | - Bingbing Li
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States; Department of Manufacturing Systems Engineering and Management, California State University, Northridge, CA, 91330, United States
| | - Mohsen Akbari
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States; Laboratory for Innovation in Microengineering (LiME), Department of Mechanical Engineering, Center for Biomedical Research, University of Victoria, Victoria, BC V8P 2C5, Canada
| | - Sam Emaminejad
- Interconnected and Integrated Bioelectronics Lab, Department of Electrical and Computer Engineering, and Materials Science and Engineering, University of California, Los Angeles, CA, 90095, United States
| | - Ali Khademhosseini
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States.
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104
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Young F, Mason R, Morris RE, Stuart S, Godfrey A. IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:4100. [PMID: 37112441 PMCID: PMC10144082 DOI: 10.3390/s23084100] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 06/19/2023]
Abstract
Walking/gait quality is a useful clinical tool to assess general health and is now broadly described as the sixth vital sign. This has been mediated by advances in sensing technology, including instrumented walkways and three-dimensional motion capture. However, it is wearable technology innovation that has spawned the highest growth in instrumented gait assessment due to the capabilities for monitoring within and beyond the laboratory. Specifically, instrumented gait assessment with wearable inertial measurement units (IMUs) has provided more readily deployable devices for use in any environment. Contemporary IMU-based gait assessment research has shown evidence of the robust quantifying of important clinical gait outcomes in, e.g., neurological disorders to gather more insightful habitual data in the home and community, given the relatively low cost and portability of IMUs. The aim of this narrative review is to describe the ongoing research regarding the need to move gait assessment out of bespoke settings into habitual environments and to consider the shortcomings and inefficiencies that are common within the field. Accordingly, we broadly explore how the Internet of Things (IoT) could better enable routine gait assessment beyond bespoke settings. As IMU-based wearables and algorithms mature in their corroboration with alternate technologies, such as computer vision, edge computing, and pose estimation, the role of IoT communication will enable new opportunities for remote gait assessment.
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Affiliation(s)
- Fraser Young
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rachel Mason
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rosie E. Morris
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Samuel Stuart
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
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105
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Cygert S, Pastuszak K, Górski F, Sieczczyński M, Juszczyk P, Rutkowski A, Lewalski S, Różański R, Jopek MA, Jassem J, Czyżewski A, Wurdinger T, Best MG, Żaczek AJ, Supernat A. Platelet-Based Liquid Biopsies through the Lens of Machine Learning. Cancers (Basel) 2023; 15:cancers15082336. [PMID: 37190262 DOI: 10.3390/cancers15082336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/11/2023] [Accepted: 04/13/2023] [Indexed: 05/17/2023] Open
Abstract
Liquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability to the model. In this work, we have used RNA sequencing data of tumor-educated platelets (TEPs) and performed a binary classification (cancer vs. no-cancer). First, we compiled a large-scale dataset with more than a thousand donors. Further, we used different convolutional neural networks (CNNs) and boosting methods to evaluate the classifier performance. We have obtained an impressive result of 0.96 area under the curve. We then identified different clusters of splice variants using expert knowledge from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Employing boosting algorithms, we identified the features with the highest predictive power. Finally, we tested the robustness of the models using test data from novel hospitals. Notably, we did not observe any decrease in model performance. Our work proves the great potential of using TEP data for cancer patient classification and opens the avenue for profound cancer diagnostics.
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Affiliation(s)
- Sebastian Cygert
- Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdańsk, Poland
- Ideas NCBR, 00-801 Warsaw, Poland
| | - Krzysztof Pastuszak
- Department of Algorithms and System Modeling, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdańsk, Poland
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, Medical University of Gdańsk, 80-210 Gdańsk, Poland
- Center of Biostatistics and Bioinformatics, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| | - Franciszek Górski
- Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdańsk, Poland
| | - Michał Sieczczyński
- Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdańsk, Poland
| | - Piotr Juszczyk
- Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdańsk, Poland
| | - Antoni Rutkowski
- Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdańsk, Poland
| | - Sebastian Lewalski
- Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdańsk, Poland
| | | | - Maksym Albin Jopek
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, Medical University of Gdańsk, 80-210 Gdańsk, Poland
- Center of Biostatistics and Bioinformatics, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| | - Jacek Jassem
- Department of Oncology and Radiotherapy, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| | - Andrzej Czyżewski
- Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdańsk, Poland
| | - Thomas Wurdinger
- Department of Neurosurgery, Amsterdam University Medical Center, 1081 Amsterdam, The Netherlands
| | - Myron G Best
- Department of Neurosurgery, Amsterdam University Medical Center, 1081 Amsterdam, The Netherlands
| | - Anna J Żaczek
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| | - Anna Supernat
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, Medical University of Gdańsk, 80-210 Gdańsk, Poland
- Center of Biostatistics and Bioinformatics, Medical University of Gdańsk, 80-210 Gdańsk, Poland
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106
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Paulauskaite-Taraseviciene A, Siaulys J, Sutiene K, Petravicius T, Navickas S, Oliandra M, Rapalis A, Balciunas J. Geriatric Care Management System Powered by the IoT and Computer Vision Techniques. Healthcare (Basel) 2023; 11:healthcare11081152. [PMID: 37107987 PMCID: PMC10138364 DOI: 10.3390/healthcare11081152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/03/2023] [Accepted: 04/13/2023] [Indexed: 04/29/2023] Open
Abstract
The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients' data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient's position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.
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Affiliation(s)
| | - Julius Siaulys
- Faculty of Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
| | - Kristina Sutiene
- Department of Mathematical Modeling, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
| | - Titas Petravicius
- Faculty of Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
| | - Skirmantas Navickas
- Faculty of Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
| | - Marius Oliandra
- Faculty of Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
| | - Andrius Rapalis
- Biomedical Engineering Institute, Kaunas University of Technology, K. Barsausko 59, 51423 Kaunas, Lithuania
- Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Studentu 48, 51367 Kaunas, Lithuania
| | - Justinas Balciunas
- Faculty of Medicine, Vilnius University, Universiteto 3, 01513 Vilnius, Lithuania
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107
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Belkovsky M, Passerotti CC, Maximiano LF, Otoch JP, Cruz JASDA. The learning curve of bilateral laparoscopic varicocelectomy: a prospective study. Rev Col Bras Cir 2023; 50:e20233456. [PMID: 37075467 PMCID: PMC10508658 DOI: 10.1590/0100-6991e-20233456-en] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/20/2022] [Indexed: 04/21/2023] Open
Abstract
Varicocele occurs in 15% of general male population and in 35% of infertile men. Since 1992, surgical correction with laparoscopic varicocelectomy is the gold standard for treatment of symptomatic patients or patients with abnormal seminal analysis. The learning curve for this frequently performed procedure has not yet been described. In the present study, we investigated the learning curve of a single urologist in training performing his first 21 laparoscopic varicocelectomies using qualitative and quantitative tools to evaluate his performance during the process. Our results show that 14 bilateral laparoscopic varicocelectomies are enough to achieve the plateau of the learning curve.
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Affiliation(s)
- Mikhael Belkovsky
- - Faculdade de Medicina da USP, Técnica Cirúrgica e Cirurgia Experimental - São Paulo - SP - Brasil
- - Hospital Alemão Oswaldo Cruz, Centro de Cirurgia Robótica - São Paulo - SP - Brasil
| | | | - Linda Ferreira Maximiano
- - Faculdade de Medicina da USP, Técnica Cirúrgica e Cirurgia Experimental - São Paulo - SP - Brasil
| | - José Pinhata Otoch
- - Faculdade de Medicina da USP, Técnica Cirúrgica e Cirurgia Experimental - São Paulo - SP - Brasil
| | - Jose Arnaldo Shiomi DA Cruz
- - Faculdade de Medicina da USP, Técnica Cirúrgica e Cirurgia Experimental - São Paulo - SP - Brasil
- - Hospital Alemão Oswaldo Cruz, Centro de Cirurgia Robótica - São Paulo - SP - Brasil
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108
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Farzaneh N, Ansari S, Lee E, Ward KR, Sjoding MW. Collaborative strategies for deploying artificial intelligence to complement physician diagnoses of acute respiratory distress syndrome. NPJ Digit Med 2023; 6:62. [PMID: 37031252 PMCID: PMC10082784 DOI: 10.1038/s41746-023-00797-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 03/10/2023] [Indexed: 04/10/2023] Open
Abstract
There is a growing gap between studies describing the capabilities of artificial intelligence (AI) diagnostic systems using deep learning versus efforts to investigate how or when to integrate AI systems into a real-world clinical practice to support physicians and improve diagnosis. To address this gap, we investigate four potential strategies for AI model deployment and physician collaboration to determine their potential impact on diagnostic accuracy. As a case study, we examine an AI model trained to identify findings of the acute respiratory distress syndrome (ARDS) on chest X-ray images. While this model outperforms physicians at identifying findings of ARDS, there are several reasons why fully automated ARDS detection may not be optimal nor feasible in practice. Among several collaboration strategies tested, we find that if the AI model first reviews the chest X-ray and defers to a physician if it is uncertain, this strategy achieves a higher diagnostic accuracy (0.869, 95% CI 0.835-0.903) compared to a strategy where a physician reviews a chest X-ray first and defers to an AI model if uncertain (0.824, 95% CI 0.781-0.862), or strategies where the physician reviews the chest X-ray alone (0.808, 95% CI 0.767-0.85) or the AI model reviews the chest X-ray alone (0.847, 95% CI 0.806-0.887). If the AI model reviews a chest X-ray first, this allows the AI system to make decisions for up to 79% of cases, letting physicians focus on the most challenging subsets of chest X-rays.
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Affiliation(s)
- Negar Farzaneh
- The Max Harry Weil Institute for Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI, USA.
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.
| | - Sardar Ansari
- The Max Harry Weil Institute for Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI, USA
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Elizabeth Lee
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Kevin R Ward
- The Max Harry Weil Institute for Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI, USA
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Michael W Sjoding
- The Max Harry Weil Institute for Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, Division of Pulmonary and Critical Care, University of Michigan Medical School, Ann Arbor, MI, USA
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109
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Iqbal S, N. Qureshi A, Li J, Mahmood T. On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:3173-3233. [PMID: 37260910 PMCID: PMC10071480 DOI: 10.1007/s11831-023-09899-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 02/19/2023] [Indexed: 06/02/2023]
Abstract
Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.
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Affiliation(s)
- Saeed Iqbal
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab 54000 Pakistan
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 Beijing China
| | - Adnan N. Qureshi
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab 54000 Pakistan
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 Beijing China
- Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing, 100124 Beijing China
| | - Tariq Mahmood
- Artificial Intelligence and Data Analytics (AIDA) Lab, College of Computer & Information Sciences (CCIS), Prince Sultan University, Riyadh, 11586 Kingdom of Saudi Arabia
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110
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Douglas MJ, Callcut R, Celi LA, Merchant N. Interpretation and Use of Applied/Operational Machine Learning and Artificial Intelligence in Surgery. Surg Clin North Am 2023; 103:317-333. [PMID: 36948721 DOI: 10.1016/j.suc.2022.11.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
Applications for artificial intelligence (AI) and machine learning in surgery include image interpretation, data summarization, automated narrative construction, trajectory and risk prediction, and operative navigation and robotics. The pace of development has been exponential, and some AI applications are working well. However, demonstrations of clinical utility, validity, and equity have lagged algorithm development and limited widespread adoption of AI into clinical practice. Outdated computing infrastructure and regulatory challenges which promote data silos are key barriers. Multidisciplinary teams will be needed to address these challenges and to build AI systems that are relevant, equitable, and dynamic.
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Affiliation(s)
- Molly J Douglas
- Department of Surgery, University of Arizona, 1501 N Campbell Avenue, Tucson, AZ 85724, USA.
| | - Rachel Callcut
- Trauma, Acute Care Surgery and Surgical Critical Care, University of California, Davis, 2335 Stockton Boulevard, Sacramento, CA 95817, USA. https://twitter.com/callcura
| | - Leo Anthony Celi
- Laboratory of Computational Physiology, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA; Beth Israel Deaconess Medical Center. https://twitter.com/MITCriticalData
| | - Nirav Merchant
- Data Science Institute, University of Arizona, 1230 North Cherry Avenue, Tucson, AZ 85721, USA
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Giri N, Roy RS, Cheng J. Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions. Curr Opin Struct Biol 2023; 79:102536. [PMID: 36773336 PMCID: PMC10023387 DOI: 10.1016/j.sbi.2023.102536] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 12/20/2022] [Accepted: 01/03/2023] [Indexed: 02/11/2023]
Abstract
Cryo-Electron Microscopy (cryo-EM) has emerged as a key technology to determine the structure of proteins, particularly large protein complexes and assemblies in recent years. A key challenge in cryo-EM data analysis is to automatically reconstruct accurate protein structures from cryo-EM density maps. In this review, we briefly overview various deep learning methods for building protein structures from cryo-EM density maps, analyze their impact, and discuss the challenges of preparing high-quality data sets for training deep learning models. Looking into the future, more advanced deep learning models of effectively integrating cryo-EM data with other sources of complementary data such as protein sequences and AlphaFold-predicted structures need to be developed to further advance the field.
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Affiliation(s)
- Nabin Giri
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, Missouri, USA; NextGen Precision Health, University of Missouri, Columbia, 65211, Missouri, USA. https://twitter.com/@nvngiri
| | - Raj S Roy
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, Missouri, USA. https://twitter.com/@rajshekhorroy
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, Missouri, USA; NextGen Precision Health, University of Missouri, Columbia, 65211, Missouri, USA.
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Li H, Zou L, Kowah JAH, He D, Liu Z, Ding X, Wen H, Wang L, Yuan M, Liu X. A compact review of progress and prospects of deep learning in drug discovery. J Mol Model 2023; 29:117. [PMID: 36976427 DOI: 10.1007/s00894-023-05492-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 02/27/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND Drug discovery processes, such as new drug development, drug synergy, and drug repurposing, consume significant yearly resources. Computer-aided drug discovery can effectively improve the efficiency of drug discovery. Traditional computer methods such as virtual screening and molecular docking have achieved many gratifying results in drug development. However, with the rapid growth of computer science, data structures have changed considerably; with more extensive and dimensional data and more significant amounts of data, traditional computer methods can no longer be applied well. Deep learning methods are based on deep neural network structures that can handle high-dimensional data very well, so they are used in current drug development. RESULTS This review summarized the applications of deep learning methods in drug discovery, such as drug target discovery, drug de novo design, drug recommendation, drug synergy, and drug response prediction. While applying deep learning methods to drug discovery suffers from a lack of data, transfer learning is an excellent solution to this problem. Furthermore, deep learning methods can extract deeper features and have higher predictive power than other machine learning methods. Deep learning methods have great potential in drug discovery and are expected to facilitate drug discovery development.
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Affiliation(s)
- Huijun Li
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Lin Zou
- College of Medicine, Guangxi University, Nanning, 530004, China
| | | | - Dongqiong He
- College of Chemistry and Chemical Engineering, Guangxi University, Nanning, 530004, China
| | - Zifan Liu
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Xuejie Ding
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Hao Wen
- College of Chemistry and Chemical Engineering, Guangxi University, Nanning, 530004, China
| | - Lisheng Wang
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Mingqing Yuan
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Xu Liu
- College of Medicine, Guangxi University, Nanning, 530004, China.
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den Boer RB, Jaspers TJM, de Jongh C, Pluim JPW, van der Sommen F, Boers T, van Hillegersberg R, Van Eijnatten MAJM, Ruurda JP. Deep learning-based recognition of key anatomical structures during robot-assisted minimally invasive esophagectomy. Surg Endosc 2023:10.1007/s00464-023-09990-z. [PMID: 36947221 DOI: 10.1007/s00464-023-09990-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/25/2023] [Indexed: 03/23/2023]
Abstract
OBJECTIVE To develop a deep learning algorithm for anatomy recognition in thoracoscopic video frames from robot-assisted minimally invasive esophagectomy (RAMIE) procedures using deep learning. BACKGROUND RAMIE is a complex operation with substantial perioperative morbidity and a considerable learning curve. Automatic anatomy recognition may improve surgical orientation and recognition of anatomical structures and might contribute to reducing morbidity or learning curves. Studies regarding anatomy recognition in complex surgical procedures are currently lacking. METHODS Eighty-three videos of consecutive RAMIE procedures between 2018 and 2022 were retrospectively collected at University Medical Center Utrecht. A surgical PhD candidate and an expert surgeon annotated the azygos vein and vena cava, aorta, and right lung on 1050 thoracoscopic frames. 850 frames were used for training of a convolutional neural network (CNN) to segment the anatomical structures. The remaining 200 frames of the dataset were used for testing the CNN. The Dice and 95% Hausdorff distance (95HD) were calculated to assess algorithm accuracy. RESULTS The median Dice of the algorithm was 0.79 (IQR = 0.20) for segmentation of the azygos vein and/or vena cava. A median Dice coefficient of 0.74 (IQR = 0.86) and 0.89 (IQR = 0.30) were obtained for segmentation of the aorta and lung, respectively. Inference time was 0.026 s (39 Hz). The prediction of the deep learning algorithm was compared with the expert surgeon annotations, showing an accuracy measured in median Dice of 0.70 (IQR = 0.19), 0.88 (IQR = 0.07), and 0.90 (0.10) for the vena cava and/or azygos vein, aorta, and lung, respectively. CONCLUSION This study shows that deep learning-based semantic segmentation has potential for anatomy recognition in RAMIE video frames. The inference time of the algorithm facilitated real-time anatomy recognition. Clinical applicability should be assessed in prospective clinical studies.
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Affiliation(s)
- R B den Boer
- Department of Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - T J M Jaspers
- Department of Biomedical Engineering, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands
| | - C de Jongh
- Department of Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - J P W Pluim
- Department of Biomedical Engineering, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands
| | - F van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, Groene Loper 19, 5612 AP, Eindhoven, The Netherlands
| | - T Boers
- Department of Electrical Engineering, Eindhoven University of Technology, Groene Loper 19, 5612 AP, Eindhoven, The Netherlands
| | - R van Hillegersberg
- Department of Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - M A J M Van Eijnatten
- Department of Biomedical Engineering, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands
| | - J P Ruurda
- Department of Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
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Li H, Zou L, Kowah JAH, He D, Wang L, Yuan M, Liu X. Predicting Drug Synergy and Discovering New Drug Combinations Based on a Graph Autoencoder and Convolutional Neural Network. Interdiscip Sci 2023; 15:316-330. [PMID: 36943614 PMCID: PMC10029792 DOI: 10.1007/s12539-023-00558-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 02/23/2023] [Accepted: 02/23/2023] [Indexed: 03/23/2023]
Abstract
Drug synergy is a crucial component in drug reuse since it solves the problem of sluggish drug development and the absence of corresponding drugs for several diseases. Predicting drug synergistic relationships can screen drug combinations in advance and reduce the waste of laboratory resources. In this research, we proposed a model that utilizes graph autoencoder and convolutional neural networks to predict drug synergy (GAECDS). Our methods include a graph convolutional neural network as an encoder to encode drug features and use a matrix factorization method as a decoder. Multilayer perceptron (MLP) was applied to process cell line features and combine them with drug features. Furthermore, the latent vectors generated during the encoding process are being used to predict drug synergistic scores using a convolutional neural network. By measuring prediction performance using AUC, AUPR, and F1 score, GAECDS superior to other state-of-the-art models. In addition, four pairs of the predicted top 10 drug combinations were found to work well enough for evaluation. The case study shows that the GAECDS approach is useful for identifying potential drug synergy.
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Affiliation(s)
- Huijun Li
- School of Medicine, Guangxi University, Nanning, 530004, China
| | - Lin Zou
- School of Medicine, Guangxi University, Nanning, 530004, China
| | - Jamal A H Kowah
- School of Chemistry and Chemical Engineering, Guangxi University, Nanning, 530004, China
| | - Dongqiong He
- School of Chemistry and Chemical Engineering, Guangxi University, Nanning, 530004, China
| | - Lisheng Wang
- School of Medicine, Guangxi University, Nanning, 530004, China
| | - Mingqing Yuan
- School of Medicine, Guangxi University, Nanning, 530004, China
| | - Xu Liu
- School of Medicine, Guangxi University, Nanning, 530004, China.
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Elhanani O, Ben-Uri R, Keren L. Spatial profiling technologies illuminate the tumor microenvironment. Cancer Cell 2023; 41:404-420. [PMID: 36800999 DOI: 10.1016/j.ccell.2023.01.010] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/01/2022] [Accepted: 01/26/2023] [Indexed: 02/18/2023]
Abstract
The tumor microenvironment (TME) is composed of many different cellular and acellular components that together drive tumor growth, invasion, metastasis, and response to therapies. Increasing realization of the significance of the TME in cancer biology has shifted cancer research from a cancer-centric model to one that considers the TME as a whole. Recent technological advancements in spatial profiling methodologies provide a systematic view and illuminate the physical localization of the components of the TME. In this review, we provide an overview of major spatial profiling technologies. We present the types of information that can be extracted from these data and describe their applications, findings and challenges in cancer research. Finally, we provide a future perspective of how spatial profiling could be integrated into cancer research to improve patient diagnosis, prognosis, stratification to treatment and development of novel therapeutics.
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Affiliation(s)
- Ofer Elhanani
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Raz Ben-Uri
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Leeat Keren
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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Njei B, McCarty TR, Mohan BP, Fozo L, Navaneethan U. Artificial intelligence in endoscopic imaging for detection of malignant biliary strictures and cholangiocarcinoma: a systematic review. Ann Gastroenterol 2023; 36:223-230. [PMID: 36864938 PMCID: PMC9932867 DOI: 10.20524/aog.2023.0779] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 01/05/2023] [Indexed: 02/05/2023] Open
Abstract
Background Artificial intelligence (AI), when applied to computer vision using a convolutional neural network (CNN), is a promising tool in "difficult-to-diagnose" conditions such as malignant biliary strictures and cholangiocarcinoma (CCA). The aim of this systematic review is to summarize and review the available data on the diagnostic utility of endoscopic AI-based imaging for malignant biliary strictures and CCA. Methods In this systematic review, PubMed, Scopus and Web of Science databases were reviewed for studies published from January 2000 to June 2022. Extracted data included type of endoscopic imaging modality, AI classifiers, and performance measures. Results The search yielded 5 studies involving 1465 patients. Of the 5 included studies, 4 (n=934; 3,775,819 images) used CNN in combination with cholangioscopy, while one study (n=531; 13,210 images) used CNN with endoscopic ultrasound (EUS). The average image processing speed of CNN with cholangioscopy was 7-15 msec per frame while that of CNN with EUS was 200-300 msec per frame. The highest performance metrics were observed with CNN-cholangioscopy (accuracy 94.9%, sensitivity 94.7%, and specificity 92.1%). CNN-EUS was associated with the greatest clinical performance application, providing station recognition and bile duct segmentation; thus reducing procedure length and providing real-time feedback to the endoscopist. Conclusions Our results suggest that there is increasing evidence to support a role for AI in the diagnosis of malignant biliary strictures and CCA. CNN-based machine leaning of cholangioscopy images appears to be the most promising, while CNN-EUS has the best clinical performance application.
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Affiliation(s)
- Basile Njei
- Global Clinical Scholars Program, Harvard Medical School, Boston, MA, USA (Basile Njei)
- Investigative Medicine Program, Yale University School of Medicine, New Haven, CT, USA (Basile Njei)
- Oxford Artificial Intelligence Programme, University of Oxford, United Kingdom (Basile Njei)
| | - Thomas R. McCarty
- Lynda K. and David M. Underwood Center for Digestive Disorders, Houston Methodist Hospital, TX, USA (Thomas R. McCarty)
| | - Babu P Mohan
- Division of Gastroenterology and Hepatology, University of Utah School of Medicine, Salt Lake City, USA (Babu P Mohan)
| | - Lydia Fozo
- Johns Hopkins University, Baltimore, MD, USA (Lydia Fozo)
| | - Udayakumar Navaneethan
- Center for IBD and Interventional IBD Unit, Digestive Health Institute, Orlando Health, FL, USA (Udayakumar Navaneethan)
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Shao J, Huang X, Gao T, Cao J, Wang Y, Zhang Q, Lou L, Ye J. Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy. Quant Imaging Med Surg 2023; 13:1592-1604. [PMID: 36915314 PMCID: PMC10006102 DOI: 10.21037/qims-22-551] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 11/25/2022] [Indexed: 01/05/2023]
Abstract
Background We aimed to propose a deep learning-based approach to automatically measure eyelid morphology in patients with thyroid-associated ophthalmopathy (TAO). Methods This prospective study consecutively included 74 eyes of patients with TAO and 74 eyes of healthy volunteers visiting the ophthalmology department in a tertiary hospital. Patients diagnosed as TAO and healthy volunteers who were age- and gender-matched met the eligibility criteria for recruitment. Facial images were taken under the same light conditions. Comprehensive eyelid morphological parameters, such as palpebral fissure (PF) length, margin reflex distance (MRD), eyelid retraction distance, eyelid length, scleral area, and mid-pupil lid distance (MPLD), were automatically calculated using our deep learning-based analysis system. MRD1 and 2 were manually measured. Bland-Altman plots and intraclass correlation coefficients (ICCs) were performed to assess the agreement between automatic and manual measurements of MRDs. The asymmetry of the eyelid contour was analyzed using the temporal: nasal ratio of the MPLD. All eyelid features were compared between TAO eyes and control eyes using the independent samples t-test. Results A strong agreement between automatic and manual measurement was indicated. Biases of MRDs in TAO eyes and control eyes ranged from -0.01 mm [95% limits of agreement (LoA): -0.64 to 0.63 mm] to 0.09 mm (LoA: -0.46 to 0.63 mm). ICCs ranged from 0.932 to 0.980 (P<0.001). Eyelid features were significantly different in TAO eyes and control eyes, including MRD1 (4.82±1.59 vs. 2.99±0.81 mm; P<0.001), MRD2 (5.89±1.16 vs. 5.47±0.73 mm; P=0.009), upper eyelid length (UEL) (27.73±4.49 vs. 25.42±4.35 mm; P=0.002), lower eyelid length (LEL) (31.51±4.59 vs. 26.34±4.72 mm; P<0.001), and total scleral area (SATOTAL) (96.14±34.38 vs. 56.91±14.97 mm2; P<0.001). The MPLDs at all angles showed significant differences in the 2 groups of eyes (P=0.008 at temporal 180°; P<0.001 at other angles). The greatest temporal-nasal asymmetry appeared at 75° apart from the midline in TAO eyes. Conclusions Our proposed system allowed automatic, comprehensive, and objective measurement of eyelid morphology by only using facial images, which has potential application prospects in TAO. Future work with a large sample of patients that contains different TAO subsets is warranted.
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Affiliation(s)
- Ji Shao
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
| | - Xingru Huang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Tao Gao
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
| | - Jing Cao
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
| | - Yaqi Wang
- College of Media Engineering, Communication University of Zhejiang, Hangzhou, China
| | - Qianni Zhang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Lixia Lou
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
| | - Juan Ye
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
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Li X, Zhang R, Wang Q, Duan X, Sun Y, Wang J. SAR-CGAN: Improved generative adversarial network for EIT reconstruction of lung diseases. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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119
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Patsanis A, Sunoqrot MRS, Bathen TF, Elschot M. CROPro: a tool for automated cropping of prostate magnetic resonance images. J Med Imaging (Bellingham) 2023; 10:024004. [PMID: 36895761 PMCID: PMC9990132 DOI: 10.1117/1.jmi.10.2.024004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 02/09/2023] [Indexed: 03/09/2023] Open
Abstract
Purpose To bypass manual data preprocessing and optimize deep learning performance, we developed and evaluated CROPro, a tool to standardize automated cropping of prostate magnetic resonance (MR) images. Approach CROPro enables automatic cropping of MR images regardless of patient health status, image size, prostate volume, or pixel spacing. CROPro can crop foreground pixels from a region of interest (e.g., prostate) with different image sizes, pixel spacing, and sampling strategies. Performance was evaluated in the context of clinically significant prostate cancer (csPCa) classification. Transfer learning was used to train five convolutional neural network (CNN) and five vision transformer (ViT) models using different combinations of cropped image sizes ( 64 × 64 , 128 × 128 , and 256 × 256 pixels2), pixel spacing ( 0.2 × 0.2 , 0.3 × 0.3 , 0.4 × 0.4 , and 0.5 × 0.5 mm 2 ), and sampling strategies (center, random, and stride cropping) over the prostate. T2-weighted MR images ( N = 1475 ) from the online available PI-CAI challenge were used to train ( N = 1033 ), validate ( N = 221 ), and test ( N = 221 ) all models. Results Among CNNs, SqueezeNet with stride cropping (image size: 128 × 128 , pixel spacing: 0.2 × 0.2 mm 2 ) achieved the best classification performance ( 0.678 ± 0.006 ). Among ViTs, ViT-H/14 with random cropping (image size: 64 × 64 and pixel spacing: 0.5 × 0.5 mm 2 ) achieved the best performance ( 0.756 ± 0.009 ). Model performance depended on the cropped area, with optimal size generally larger with center cropping ( ∼ 40 cm 2 ) than random/stride cropping ( ∼ 10 cm 2 ). Conclusion We found that csPCa classification performance of CNNs and ViTs depends on the cropping settings. We demonstrated that CROPro is well suited to optimize these settings in a standardized manner, which could improve the overall performance of deep learning models.
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Affiliation(s)
- Alexandros Patsanis
- Norwegian University of Science and Technology, Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Trondheim, Norway
| | - Mohammed R. S. Sunoqrot
- Norwegian University of Science and Technology, Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Trondheim, Norway
- St. Olavs Hospital, Trondheim University Hospital, Department of Radiology and Nuclear Medicine, Trondheim, Norway
| | - Tone F. Bathen
- Norwegian University of Science and Technology, Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Trondheim, Norway
- St. Olavs Hospital, Trondheim University Hospital, Department of Radiology and Nuclear Medicine, Trondheim, Norway
| | - Mattijs Elschot
- Norwegian University of Science and Technology, Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Trondheim, Norway
- St. Olavs Hospital, Trondheim University Hospital, Department of Radiology and Nuclear Medicine, Trondheim, Norway
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Huang Z, Zhao X, Ziv O, Laurita KR, Rollins AM, Hendon CP. Automated analysis framework for in vivo cardiac ablation therapy monitoring with optical coherence tomography. BIOMEDICAL OPTICS EXPRESS 2023; 14:1228-1242. [PMID: 36950243 PMCID: PMC10026573 DOI: 10.1364/boe.480943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/12/2023] [Accepted: 02/16/2023] [Indexed: 06/18/2023]
Abstract
Radiofrequency ablation (RFA) is a minimally invasive procedure that is commonly used for the treatment of atrial fibrillation. However, it is associated with a significant risk of arrhythmia recurrence and complications owing to the lack of direct visualization of cardiac substrates and real-time feedback on ablation lesion transmurality. Within this manuscript, we present an automated deep learning framework for in vivo intracardiac optical coherence tomography (OCT) analysis of swine left atria. Our model can accurately identify cardiac substrates, monitor catheter-tissue contact stability, and assess lesion transmurality on both OCT intensity and polarization-sensitive OCT data. To the best of our knowledge, we have developed the first automatic framework for in vivo cardiac OCT analysis, which holds promise for real-time monitoring and guidance of cardiac RFA therapy..
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Affiliation(s)
- Ziyi Huang
- Department of Electrical Engineering, Columbia University, New York, NY, USA
| | - Xiaowei Zhao
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Ohad Ziv
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Heart and Vascular Research Center, MetroHealth Campus, Case Western Reserve University, Cleveland, OH, USA
| | - Kenneth R. Laurita
- Heart and Vascular Research Center, MetroHealth Campus, Case Western Reserve University, Cleveland, OH, USA
| | - Andrew M. Rollins
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Christine P. Hendon
- Department of Electrical Engineering, Columbia University, New York, NY, USA
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Deep ensemble learning enables highly accurate classification of stored red blood cell morphology. Sci Rep 2023; 13:3152. [PMID: 36823298 PMCID: PMC9950070 DOI: 10.1038/s41598-023-30214-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 02/17/2023] [Indexed: 02/25/2023] Open
Abstract
Changes in red blood cell (RBC) morphology distribution have emerged as a quantitative biomarker for the degradation of RBC functional properties during hypothermic storage. Previously published automated methods for classifying the morphology of stored RBCs often had insufficient accuracy and relied on proprietary code and datasets, making them difficult to use in many research and clinical applications. Here we describe the development and validation of a highly accurate open-source RBC morphology classification pipeline based on ensemble deep learning (DL). The DL-enabled pipeline utilized adaptive thresholding or semantic segmentation for RBC identification, a deep ensemble of four convolutional neural networks (CNNs) to classify RBC morphology, and Kalman filtering with Hungarian assignment for tracking changes in the morphology of individual RBCs over time. The ensembled CNNs were trained and evaluated on thousands of individual RBCs from two open-access datasets previously collected to quantify the morphological heterogeneity and washing-induced shape recovery of stored RBCs. Confusion matrices and reliability diagrams demonstrated under-confidence of the constituent models and an accuracy of about 98% for the deep ensemble. Such a high accuracy allowed the CNN ensemble to uncover new insights over our previously published studies. Re-analysis of the datasets yielded much more accurate distributions of the effective diameters of stored RBCs at each stage of morphological degradation (discocyte: 7.821 ± 0.429 µm, echinocyte 1: 7.800 ± 0.581 µm, echinocyte 2: 7.304 ± 0.567 µm, echinocyte 3: 6.433 ± 0.490 µm, sphero-echinocyte: 5.963 ± 0.348 µm, spherocyte: 5.904 ± 0.292 µm, stomatocyte: 7.080 ± 0.522 µm). The effective diameter distributions were significantly different across all morphologies, with considerable effect sizes for non-neighboring classes. A combination of morphology classification with cell tracking enabled the discovery of a relatively rare and previously overlooked shape recovery of some sphero-echinocytes to early-stage echinocytes after washing with 1% human serum albumin solution. Finally, the datasets and code have been made freely available online to enable replication, further improvement, and adaptation of our work for other applications.
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Wu Y, Olvera-Barrios A, Yanagihara R, Kung TPH, Lu R, Leung I, Mishra AV, Nussinovitch H, Grimaldi G, Blazes M, Lee CS, Egan C, Tufail A, Lee AY. Training Deep Learning Models to Work on Multiple Devices by Cross-Domain Learning with No Additional Annotations. Ophthalmology 2023; 130:213-222. [PMID: 36154868 PMCID: PMC9868052 DOI: 10.1016/j.ophtha.2022.09.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 09/07/2022] [Accepted: 09/16/2022] [Indexed: 01/25/2023] Open
Abstract
PURPOSE To create an unsupervised cross-domain segmentation algorithm for segmenting intraretinal fluid and retinal layers on normal and pathologic macular OCT images from different manufacturers and camera devices. DESIGN We sought to use generative adversarial networks (GANs) to generalize a segmentation model trained on one OCT device to segment B-scans obtained from a different OCT device manufacturer in a fully unsupervised approach without labeled data from the latter manufacturer. PARTICIPANTS A total of 732 OCT B-scans from 4 different OCT devices (Heidelberg Spectralis, Topcon 1000, Maestro2, and Zeiss Plex Elite 9000). METHODS We developed an unsupervised GAN model, GANSeg, to segment 7 retinal layers and intraretinal fluid in Topcon 1000 OCT images (domain B) that had access only to labeled data on Heidelberg Spectralis images (domain A). GANSeg was unsupervised because it had access only to 110 Heidelberg labeled OCTs and 556 raw and unlabeled Topcon 1000 OCTs. To validate GANSeg segmentations, 3 masked graders manually segmented 60 OCTs from an external Topcon 1000 test dataset independently. To test the limits of GANSeg, graders also manually segmented 3 OCTs from Zeiss Plex Elite 9000 and Topcon Maestro2. A U-Net was trained on the same labeled Heidelberg images as baseline. The GANSeg repository with labeled annotations is at https://github.com/uw-biomedical-ml/ganseg. MAIN OUTCOME MEASURES Dice scores comparing segmentation results from GANSeg and the U-Net model with the manual segmented images. RESULTS Although GANSeg and U-Net achieved comparable Dice scores performance as human experts on the labeled Heidelberg test dataset, only GANSeg achieved comparable Dice scores with the best performance for the ganglion cell layer plus inner plexiform layer (90%; 95% confidence interval [CI], 68%-96%) and the worst performance for intraretinal fluid (58%; 95% CI, 18%-89%), which was statistically similar to human graders (79%; 95% CI, 43%-94%). GANSeg significantly outperformed the U-Net model. Moreover, GANSeg generalized to both Zeiss and Topcon Maestro2 swept-source OCT domains, which it had never encountered before. CONCLUSIONS GANSeg enables the transfer of supervised deep learning algorithms across OCT devices without labeled data, thereby greatly expanding the applicability of deep learning algorithms.
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Affiliation(s)
- Yue Wu
- Department of Ophthalmology, University of Washington, Seattle, Washington
| | - Abraham Olvera-Barrios
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom; Institute of Ophthalmology, University College London, London, United Kingdom
| | - Ryan Yanagihara
- Department of Ophthalmology, University of Washington, Seattle, Washington
| | | | - Randy Lu
- Department of Ophthalmology, University of Washington, Seattle, Washington
| | - Irene Leung
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Amit V Mishra
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | | | - Gabriela Grimaldi
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Marian Blazes
- Department of Ophthalmology, University of Washington, Seattle, Washington
| | - Cecilia S Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington; Roger and Angie Karalis Johnson Retina Center, Seattle, Washington
| | - Catherine Egan
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom; Institute of Ophthalmology, University College London, London, United Kingdom
| | - Adnan Tufail
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom; Institute of Ophthalmology, University College London, London, United Kingdom
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington; Roger and Angie Karalis Johnson Retina Center, Seattle, Washington.
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Kushnure DT, Tyagi S, Talbar SN. LiM-Net: Lightweight multi-level multiscale network with deep residual learning for automatic liver segmentation in CT images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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124
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Zeng Y, Xu X. Label Diffusion Graph Learning network for semi-supervised breast histological image recognition. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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125
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Grading of gliomas using transfer learning on MRI images. MAGMA (NEW YORK, N.Y.) 2023; 36:43-53. [PMID: 36326937 DOI: 10.1007/s10334-022-01046-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/04/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Despite the critical role of Magnetic Resonance Imaging (MRI) in the diagnosis of brain tumours, there are still many pitfalls in the exact grading of them, in particular, gliomas. In this regard, it was aimed to examine the potential of Transfer Learning (TL) and Machine Learning (ML) algorithms in the accurate grading of gliomas on MRI images. MATERIALS AND METHODS Dataset has included four types of axial MRI images of glioma brain tumours with grades I-IV: T1-weighted, T2-weighted, FLAIR, and T1-weighted Contrast-Enhanced (T1-CE). Images were resized, normalized, and randomly split into training, validation, and test sets. ImageNet pre-trained Convolutional Neural Networks (CNNs) were utilized for feature extraction and classification, using Adam and SGD optimizers. Logistic Regression (LR) and Support Vector Machine (SVM) methods were also implemented for classification instead of Fully Connected (FC) layers taking advantage of features extracted by each CNN. RESULTS Evaluation metrics were computed to find the model with the best performance, and the highest overall accuracy of 99.38% was achieved for the model containing an SVM classifier and features extracted by pre-trained VGG-16. DISCUSSION It was demonstrated that developing Computer-aided Diagnosis (CAD) systems using pre-trained CNNs and classification algorithms is a functional approach to automatically specify the grade of glioma brain tumours in MRI images. Using these models is an excellent alternative to invasive methods and helps doctors diagnose more accurately before treatment.
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Zhou J, Li G, Wang R, Chen R, Luo S. A Novel Contrastive Self-Supervised Learning Framework for Solving Data Imbalance in Solder Joint Defect Detection. ENTROPY (BASEL, SWITZERLAND) 2023; 25:268. [PMID: 36832635 PMCID: PMC9954869 DOI: 10.3390/e25020268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/16/2023] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Poor chip solder joints can severely affect the quality of the finished printed circuit boards (PCBs). Due to the diversity of solder joint defects and the scarcity of anomaly data, it is a challenging task to automatically and accurately detect all types of solder joint defects in the production process in real time. To address this issue, we propose a flexible framework based on contrastive self-supervised learning (CSSL). In this framework, we first design several special data augmentation approaches to generate abundant synthetic, not good (sNG) data from the normal solder joint data. Then, we develop a data filter network to distill the highest quality data from sNG data. Based on the proposed CSSL framework, a high-accuracy classifier can be obtained even when the available training data are very limited. Ablation experiments verify that the proposed method can effectively improve the ability of the classifier to learn normal solder joint (OK) features. Through comparative experiments, the classifier trained with the help of the proposed method can achieve an accuracy of 99.14% on the test set, which is better than other competitive methods. In addition, its reasoning time is less than 6 ms per chip image, which is in favor of the real-time defect detection of chip solder joints.
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127
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Kruse E, Döllinger M, Schützenberger A, Kist AM. GlottisNetV2: Temporal Glottal Midline Detection Using Deep Convolutional Neural Networks. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 11:137-144. [PMID: 36816097 PMCID: PMC9933989 DOI: 10.1109/jtehm.2023.3237859] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 11/27/2022] [Accepted: 01/04/2023] [Indexed: 11/26/2023]
Abstract
High-speed videoendoscopy is a major tool for quantitative laryngology. Glottis segmentation and glottal midline detection are crucial for computing vocal fold-specific, quantitative parameters. However, fully automated solutions show limited clinical applicability. Especially unbiased glottal midline detection remains a challenging problem. We developed a multitask deep neural network for glottis segmentation and glottal midline detection. We used techniques from pose estimation to estimate the anterior and posterior points in endoscopy images. Neural networks were set up in TensorFlow/Keras and trained and evaluated with the BAGLS dataset. We found that a dual decoder deep neural network termed GlottisNetV2 outperforms the previously proposed GlottisNet in terms of MAPE on the test dataset (1.85% to 6.3%) while converging faster. Using various hyperparameter tunings, we allow fast and directed training. Using temporal variant data on an additional data set designed for this task, we can improve the median prediction accuracy from 2.1% to 1.76% when using 12 consecutive frames and additional temporal filtering. We found that temporal glottal midline detection using a dual decoder architecture together with keypoint estimation allows accurate midline prediction. We show that our proposed architecture allows stable and reliable glottal midline predictions ready for clinical use and analysis of symmetry measures.
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Affiliation(s)
- Elina Kruse
- Department Artificial Intelligence in Biomedical EngineeringFriedrich-Alexander-University Erlangen–Nürnberg (FAU)91052ErlangenGermany
| | - Michael Döllinger
- Division of Phoniatrics and Pediatric AudiologyDepartment of Otorhinolaryngology, Head and Neck SurgeryUniversity Hospital Erlangen, Friedrich-Alexander-University Erlangen–Nürnberg (FAU)91054ErlangenGermany
| | - Anne Schützenberger
- Division of Phoniatrics and Pediatric AudiologyDepartment of Otorhinolaryngology, Head and Neck SurgeryUniversity Hospital Erlangen, Friedrich-Alexander-University Erlangen–Nürnberg (FAU)91054ErlangenGermany
| | - Andreas M. Kist
- Department Artificial Intelligence in Biomedical EngineeringFriedrich-Alexander-University Erlangen–Nürnberg (FAU)91052ErlangenGermany
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Lyra S, Mustafa A, Rixen J, Borik S, Lueken M, Leonhardt S. Conditional Generative Adversarial Networks for Data Augmentation of a Neonatal Image Dataset. SENSORS (BASEL, SWITZERLAND) 2023; 23:999. [PMID: 36679796 PMCID: PMC9864455 DOI: 10.3390/s23020999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 06/17/2023]
Abstract
In today's neonatal intensive care units, monitoring vital signs such as heart rate and respiration is fundamental for neonatal care. However, the attached sensors and electrodes restrict movement and can cause medical-adhesive-related skin injuries due to the immature skin of preterm infants, which may lead to serious complications. Thus, unobtrusive camera-based monitoring techniques in combination with image processing algorithms based on deep learning have the potential to allow cable-free vital signs measurements. Since the accuracy of deep-learning-based methods depends on the amount of training data, proper validation of the algorithms is difficult due to the limited image data of neonates. In order to enlarge such datasets, this study investigates the application of a conditional generative adversarial network for data augmentation by using edge detection frames from neonates to create RGB images. Different edge detection algorithms were used to validate the input images' effect on the adversarial network's generator. The state-of-the-art network architecture Pix2PixHD was adapted, and several hyperparameters were optimized. The quality of the generated RGB images was evaluated using a Mechanical Turk-like multistage survey conducted by 30 volunteers and the FID score. In a fake-only stage, 23% of the images were categorized as real. A direct comparison of generated and real (manually augmented) images revealed that 28% of the fake data were evaluated as more realistic. An FID score of 103.82 was achieved. Therefore, the conducted study shows promising results for the training and application of conditional generative adversarial networks to augment highly limited neonatal image datasets.
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Affiliation(s)
- Simon Lyra
- Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, 52074 Aachen, Germany
| | - Arian Mustafa
- Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, 52074 Aachen, Germany
| | - Jöran Rixen
- Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, 52074 Aachen, Germany
| | - Stefan Borik
- Department of Electromagnetic and Biomedical Engineering, Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia
| | - Markus Lueken
- Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, 52074 Aachen, Germany
| | - Steffen Leonhardt
- Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, 52074 Aachen, Germany
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129
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Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology. Diagnostics (Basel) 2023; 13:diagnostics13020286. [PMID: 36673096 PMCID: PMC9857980 DOI: 10.3390/diagnostics13020286] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 12/24/2022] [Accepted: 01/05/2023] [Indexed: 01/15/2023] Open
Abstract
In the era of big data, text-based medical data, such as electronic health records (EHR) and electronic medical records (EMR), are growing rapidly. EHR and EMR are collected from patients to record their basic information, lab tests, vital signs, clinical notes, and reports. EHR and EMR contain the helpful information to assist oncologists in computer-aided diagnosis and decision making. However, it is time consuming for doctors to extract the valuable information they need and analyze the information from the EHR and EMR data. Recently, more and more research works have applied natural language processing (NLP) techniques, i.e., rule-based, machine learning-based, and deep learning-based techniques, on the EHR and EMR data for computer-aided diagnosis in oncology. The objective of this review is to narratively review the recent progress in the area of NLP applications for computer-aided diagnosis in oncology. Moreover, we intend to reduce the research gap between artificial intelligence (AI) experts and clinical specialists to design better NLP applications. We originally identified 295 articles from the three electronic databases: PubMed, Google Scholar, and ACL Anthology; then, we removed the duplicated papers and manually screened the irrelevant papers based on the content of the abstract; finally, we included a total of 23 articles after the screening process of the literature review. Furthermore, we provided an in-depth analysis and categorized these studies into seven cancer types: breast cancer, lung cancer, liver cancer, prostate cancer, pancreatic cancer, colorectal cancer, and brain tumors. Additionally, we identified the current limitations of NLP applications on supporting the clinical practices and we suggest some promising future research directions in this paper.
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130
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Computer-Aided Early Melanoma Brain-Tumor Detection Using Deep-Learning Approach. Biomedicines 2023; 11:biomedicines11010184. [PMID: 36672693 PMCID: PMC9856126 DOI: 10.3390/biomedicines11010184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 01/06/2023] [Accepted: 01/09/2023] [Indexed: 01/14/2023] Open
Abstract
Brain tumors affect the normal functioning of the brain and if not treated in time these cancerous cells may affect the other tissues, blood vessels, and nerves surrounding these cells. Today, a large population worldwide is affected by the precarious disease of the brain tumor. Healthy tissues of the brain are suspected to be damaged because of tumors that become the most significant reason for a large number of deaths nowadays. Therefore, their early detection is necessary to prevent patients from unfortunate mishaps resulting in loss of lives. The manual detection of brain tumors is a challenging task due to discrepancies in appearance in terms of shape, size, nucleus, etc. As a result, an automatic system is required for the early detection of brain tumors. In this paper, the detection of tumors in brain cells is carried out using a deep convolutional neural network with stochastic gradient descent (SGD) optimization algorithm. The multi-classification of brain tumors is performed using the ResNet-50 model and evaluated on the public Kaggle brain-tumor dataset. The method achieved 99.82% and 99.5% training and testing accuracy, respectively. The experimental result indicates that the proposed model outperformed baseline methods, and provides a compelling reason to be applied to other diseases.
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131
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Dudeja T, Dubey SK, Bhatt AK. Ensembled EfficientNetB3 architecture for multi-class classification of tumours in MRI images. INTELLIGENT DECISION TECHNOLOGIES 2023. [DOI: 10.3233/idt-220150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Healthcare informatics is one of the major concern domains in the processing of medical imaging for the diagnosis and treatment of brain tumours all over the world. Timely diagnosis of abnormal structures in brain tumours helps the clinical applications, medicines, doctors etc. in processing and analysing the medical imaging. The multi-class image classification of brain tumours faces challenges such as the scaling of large dataset, training of image datasets, efficiency, accuracy etc. EfficientNetB3 neural network scales the images in three dimensions resulting in improved accuracy. The novel neural network framework utilizes the optimization of an ensembled architecture of EfficientNetB3 with U-Net for MRI images which applies a semantic segmentation model for pre-trained backbone networks. The proposed neural model operates on a substantial network which will adapt the robustness by capturing the extraction of features in the U-Net encoder. The decoder will be enabling pixel-level localization at the definite precision level by an average ensemble of segmentation models. The ensembled pre-trained models will provide better training and prediction of abnormal structures in MRI images and thresholds for multi-classification of medical image visualization. The proposed model results in mean accuracy of 99.24 on the Kaggle dataset with 3064 images with a mean Dice score coefficient (DSC) of 0.9124 which is being compared with two state-of-art neural models.
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Affiliation(s)
- Tina Dudeja
- Department of Computer Science and Engineering, Amity University, Noida, Uttar Pradesh, India
| | - Sanjay Kumar Dubey
- Department of Computer Science and Engineering, Amity University, Noida, Uttar Pradesh, India
| | - Ashutosh Kumar Bhatt
- School of Computer Science and Information Technology, Uttarakhand Open University, Haldwani, Uttarakhand, India
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132
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Tang D, Jin W, Liu D, Che J, Yang Y. Siam Deep Feature KCF Method and Experimental Study for Pedestrian Tracking. SENSORS (BASEL, SWITZERLAND) 2023; 23:482. [PMID: 36617099 PMCID: PMC9824739 DOI: 10.3390/s23010482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 12/01/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
The tracking of a particular pedestrian is an important issue in computer vision to guarantee societal safety. Due to the limited computing performances of unmanned aerial vehicle (UAV) systems, the Correlation Filter (CF) algorithm has been widely used to perform the task of tracking. However, it has a fixed template size and cannot effectively solve the occlusion problem. Thus, a tracking-by-detection framework was designed in the current research. A lightweight YOLOv3-based (You Only Look Once version 3) mode which had Efficient Channel Attention (ECA) was integrated into the CF algorithm to provide deep features. In addition, a lightweight Siamese CNN with Cross Stage Partial (CSP) provided the representations of features learned from massive face images, allowing the target similarity in data association to be guaranteed. As a result, a Deep Feature Kernelized Correlation Filters method coupled with Siamese-CSP(Siam-DFKCF) was established to increase the tracking robustness. From the experimental results, it can be concluded that the anti-occlusion and re-tracking performance of the proposed method was increased. The tracking accuracy Distance Precision (DP) and Overlap Precision (OP) had been increased to 0.934 and 0.909 respectively in our test data.
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Affiliation(s)
- Di Tang
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Weijie Jin
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Dawei Liu
- China Aerodynamics Research and Development Center, High Speed Aerodynamic Institute, Mianyang 621000, China
| | - Jingqi Che
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Yin Yang
- China Aerodynamics Research and Development Center, High Speed Aerodynamic Institute, Mianyang 621000, China
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133
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Hu S, Zhao X, Huang L, Huang K. Global Instance Tracking: Locating Target More Like Humans. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023; 45:576-592. [PMID: 35196228 DOI: 10.1109/tpami.2022.3153312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Target tracking, the essential ability of the human visual system, has been simulated by computer vision tasks. However, existing trackers perform well in austere experimental environments but fail in challenges like occlusion and fast motion. The massive gap indicates that researches only measure tracking performance rather than intelligence. How to scientifically judge the intelligence level of trackers? Distinct from decision-making problems, lacking three requirements (a challenging task, a fair environment, and a scientific evaluation procedure) makes it strenuous to answer the question. In this article, we first propose the global instance tracking (GIT) task, which is supposed to search an arbitrary user-specified instance in a video without any assumptions about camera or motion consistency, to model the human visual tracking ability. Whereafter, we construct a high-quality and large-scale benchmark VideoCube to create a challenging environment. Finally, we design a scientific evaluation procedure using human capabilities as the baseline to judge tracking intelligence. Additionally, we provide an online platform with toolkit and an updated leaderboard. Although the experimental results indicate a definite gap between trackers and humans, we expect to take a step forward to generate authentic human-like trackers. The database, toolkit, evaluation server, and baseline results are available at http://videocube.aitestunion.com.
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134
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Schromm TM, Grosse CU. From 2D projections to the 3D rotation matrix: an attempt for finding a machine learning approach for the efficient evaluation of mechanical joining elements in X-ray computed tomography volume data. SN APPLIED SCIENCES 2023; 5:18. [PMCID: PMC9743106 DOI: 10.1007/s42452-022-05220-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 11/09/2022] [Indexed: 12/14/2022] Open
Abstract
Destructive and predominantly manual procedures are commonly used in the automotive industry for the testing of mechanical joints, such as rivets or screws. Combining X-ray computed tomography (CT) and machine learning (ML) bears the potential of a non-destructive and largely automated methodology. Assuming the desired result is a comprehensible and documentable evaluation, three basic steps need to be automatized: First, a joint must be detected and identified as such in a CT scan of the joined parts. Second, the detected region containing the joint is rotated to a predefined orientation. Third, key measures in cross-sections from the newly oriented joint are dimensioned and documented. This work deals only with the second step, the rotation. On the one hand, we present a methodology for creating a well-curated data set for the contextual machine learning application. On the other, we evaluate its performance on the well-known ResNet50. More concretely, we investigate if it is possible for a deep convolutional neural network (CNN) to learn the respective rotation matrix from three volume projections that are perpendicular to each other. Two scenarios are investigated: In one scenario we assume that future data that is presented to the network has similar rivet demographics to historic data. We therefore do not employ hold-out sets for the network evaluation. In the other scenario we assume the opposite and therefore evaluating the networks performance with hold-out sets. We show that from a machine learning point of view, a CNN like ResNet50 is well able to learn this relationship with acceptable accuracy. In most cases the validation loss dropped below 0.1 after only a couple of epochs. In one particular case, we even reached both mean and median errors lower than 0.2 for approximately 80% of the entire test set of 1600 examples using our methodology. From an application point of view, however, these low test set errors should be treated with caution since small deviations from the intended rotation matrix can cause volume warping and translation. In another case, in which we used a hold-out set, only a fraction of the median errors were below 0.2.
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Affiliation(s)
- T. M. Schromm
- Chair of Non-Destructive Testing, Technical University of Munich, Franz Langinger Str. 10, 81245 Munich, Germany
| | - C. U. Grosse
- Chair of Non-Destructive Testing, Technical University of Munich, Franz Langinger Str. 10, 81245 Munich, Germany
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135
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Chan PY, Tay A, Chen D, De Freitas M, Millet C, Nguyen-Duc T, Duke G, Lyall J, Nguyen JT, McNeil J, Hopper I. Ambient intelligence-based monitoring of staff and patient activity in the intensive care unit. Aust Crit Care 2023; 36:92-98. [PMID: 36244918 DOI: 10.1016/j.aucc.2022.08.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 08/19/2022] [Accepted: 08/20/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Caregiver workload in the ICU setting is difficult to numerically quantify. Ambient Intelligence utilises computer vision-guided neural networks to continuously monitor multiple datapoints in video feeds, has become increasingly efficient at automatically tracking various aspects of human movement. OBJECTIVES To assess the feasibility of using Ambient Intelligence to track and quantify allpatient and caregiver activity within a bedspace over the course of an ICU admission and also to establish patient specific factors, and environmental factors such as time ofday, that might contribute to an increased workload in ICU workers. METHODS 5000 images were manually annotated and then used to train You Only LookOnce (YOLOv4), an open-source computer vision algorithm. Comparison of patientmotion and caregiver activity was then performed between these patients. RESULTS The algorithm was deployed on 14 patients comprising 1762800 framesof new, untrained data. There was a strong correlation between the number ofcaregivers in the room and the standardized movement of the patient (p < 0.0001) withmore caregivers associated with more movement. There was a significant difference incaregiver activity throughout the day (p < 0.05), HDU vs. ICU status (p < 0.05), delirious vs. non delirious patients (p < 0.05), and intubated vs. not intubated patients(p < 0.05). Caregiver activity was lowest between 0400 and 0800 (average .71 ± .026caregivers per hour) with statistically significant differences in activity compared to 0800-2400 (p < 0.05). Caregiver activity was highest between 1200 and 1600 (1.02 ± .031 caregivers per hour) with a statistically significant difference in activity comparedto activity from 1600 to 0800 (p < 0.05). The three most dominant predictors of workeractivity were patient motion (Standardized Dominance 78.6%), Mechanical Ventilation(Standardized Dominance 7.9%) and Delirium (Standardized Dominance 6.2%). CONCLUSION Ambient Intelligence could potentially be used to derive a single standardized metricthat could be applied to patients to illustrate their overall workload. This could be usedto predict workflow demands for better staff deployment, monitoring of caregiver workload, and potentially as a tool to predict burnout.
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Affiliation(s)
- Peter Y Chan
- Department of Intensive Care Medicine, Eastern Health, Melbourne, Victoria, Australia; School of Public Health and Prevention Medicine, Monash University, Melbourne, Victoria, Australia.
| | - Andrew Tay
- Department of Intensive Care Medicine, Eastern Health, Melbourne, Victoria, Australia
| | - David Chen
- Department of Intensive Care Medicine, Eastern Health, Melbourne, Victoria, Australia
| | - Maria De Freitas
- Department of Intensive Care Medicine, Eastern Health, Melbourne, Victoria, Australia
| | - Coralie Millet
- Department of Intensive Care Medicine, Eastern Health, Melbourne, Victoria, Australia
| | - Thanh Nguyen-Duc
- School of Public Health and Prevention Medicine, Monash University, Melbourne, Victoria, Australia
| | - Graeme Duke
- Department of Intensive Care Medicine, Eastern Health, Melbourne, Victoria, Australia
| | - Jessica Lyall
- Department of Intensive Care Medicine, Eastern Health, Melbourne, Victoria, Australia
| | - John T Nguyen
- School of Public Health and Prevention Medicine, Monash University, Melbourne, Victoria, Australia
| | - John McNeil
- School of Public Health and Prevention Medicine, Monash University, Melbourne, Victoria, Australia
| | - Ingrid Hopper
- School of Public Health and Prevention Medicine, Monash University, Melbourne, Victoria, Australia
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Machine Learning in Cardiovascular Imaging: A Scoping Review of Published Literature. CURRENT RADIOLOGY REPORTS 2023; 11:34-45. [PMID: 36531124 PMCID: PMC9742664 DOI: 10.1007/s40134-022-00407-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2022] [Indexed: 12/14/2022]
Abstract
Purpose of Review In this study, we planned and carried out a scoping review of the literature to learn how machine learning (ML) has been investigated in cardiovascular imaging (CVI). Recent Findings During our search, we found numerous studies that developed or utilized existing ML models for segmentation, classification, object detection, generation, and regression applications involving cardiovascular imaging data. We first quantitatively investigated the different aspects of study characteristics, data handling, model development, and performance evaluation in all studies that were included in our review. We then supplemented these findings with a qualitative synthesis to highlight the common themes in the studied literature and provided recommendations to pave the way for upcoming research. Summary ML is a subfield of artificial intelligence (AI) that enables computers to learn human-like decision-making from data. Due to its novel applications, ML is gaining more and more attention from researchers in the healthcare industry. Cardiovascular imaging is an active area of research in medical imaging with lots of room for incorporating new technologies, like ML. Supplementary Information The online version contains supplementary material available at 10.1007/s40134-022-00407-8.
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Venkaiahppalaswamy B, Prasad Reddy PVGD, Batha S. Hybrid deep learning approaches for the detection of diabetic retinopathy using optimized wavelet based model. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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138
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Sekandi JN, Shi W, Zhu R, Kaggwa P, Mwebaze E, Li S. Application of Artificial Intelligence to the Monitoring of Medication Adherence for Tuberculosis Treatment in Africa: Algorithm Development and Validation. JMIR AI 2023; 2:e40167. [PMID: 38464947 PMCID: PMC10923555 DOI: 10.2196/40167] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Background Artificial intelligence (AI) applications based on advanced deep learning methods in image recognition tasks can increase efficiency in the monitoring of medication adherence through automation. AI has sparsely been evaluated for the monitoring of medication adherence in clinical settings. However, AI has the potential to transform the way health care is delivered even in limited-resource settings such as Africa. Objective We aimed to pilot the development of a deep learning model for simple binary classification and confirmation of proper medication adherence to enhance efficiency in the use of video monitoring of patients in tuberculosis treatment. Methods We used a secondary data set of 861 video images of medication intake that were collected from consenting adult patients with tuberculosis in an institutional review board-approved study evaluating video-observed therapy in Uganda. The video images were processed through a series of steps to prepare them for use in a training model. First, we annotated videos using a specific protocol to eliminate those with poor quality. After the initial annotation step, 497 videos had sufficient quality for training the models. Among them, 405 were positive samples, whereas 92 were negative samples. With some preprocessing techniques, we obtained 160 frames with a size of 224 × 224 in each video. We used a deep learning framework that leveraged 4 convolutional neural networks models to extract visual features from the video frames and automatically perform binary classification of adherence or nonadherence. We evaluated the diagnostic properties of the different models using sensitivity, specificity, F1-score, and precision. The area under the curve (AUC) was used to assess the discriminative performance and the speed per video review as a metric for model efficiency. We conducted a 5-fold internal cross-validation to determine the diagnostic and discriminative performance of the models. We did not conduct external validation due to a lack of publicly available data sets with specific medication intake video frames. Results Diagnostic properties and discriminative performance from internal cross-validation were moderate to high in the binary classification tasks with 4 selected automated deep learning models. The sensitivity ranged from 92.8 to 95.8%, specificity from 43.5 to 55.4%, F1-score from 0.91 to 0.92, precision from 88% to 90.1%, and AUC from 0.78 to 0.85. The 3D ResNet model had the highest precision, AUC, and speed. Conclusions All 4 deep learning models showed comparable diagnostic properties and discriminative performance. The findings serve as a reasonable proof of concept to support the potential application of AI in the binary classification of video frames to predict medication adherence.
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Affiliation(s)
- Juliet Nabbuye Sekandi
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, United States
- Global Health Institute, College of Public Health, University of Georgia, Athens, GA, United States
| | - Weili Shi
- School of Data Science, University of Virginia, Charlottesville, VA, United States
| | - Ronghang Zhu
- School of Computing, College of Engineering & Franklin College of Arts and Sciences, University of Georgia, Athens, GA, United States
| | - Patrick Kaggwa
- Department of Epidemiology and Biostatistics, School of Public Health, Makerere University, Kampala, Uganda
| | - Ernest Mwebaze
- Sunbird AI, Kampala, Uganda
- Artificial Intelligence Research Lab, College of Computing and Information Science, Makerere University, Kampala, Uganda
| | - Sheng Li
- School of Data Science, University of Virginia, Charlottesville, VA, United States
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Vahadane A, Sharma S, Mandal D, Dabbeeru M, Jakthong J, Garcia-Guzman M, Majumdar S, Lee CW. Development of an automated combined positive score prediction pipeline using artificial intelligence on multiplexed immunofluorescence images. Comput Biol Med 2023; 152:106337. [PMID: 36502695 DOI: 10.1016/j.compbiomed.2022.106337] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 11/05/2022] [Accepted: 11/16/2022] [Indexed: 11/25/2022]
Abstract
Immunotherapy targeting immune checkpoint proteins, such as programmed cell death ligand 1 (PD-L1), has shown impressive outcomes in many clinical trials but only 20%-40% of patients benefit from it. Utilizing Combined Positive Score (CPS) to evaluate PD-L1 expression in tumour biopsies to identify patients with the highest likelihood of responsiveness to anti-PD-1/PD-L1 therapy has been approved by the Food and Drug Administration for several solid tumour types. Current CPS workflow requires a pathologist to manually score the two-colour PD-L1 chromogenic immunohistochemistry image. Multiplex immunofluorescence (mIF) imaging reveals the expression of an increased number of immune markers in tumour biopsies and has been used extensively in immunotherapy research. Recent rapid progress of Artificial Intelligence (AI)-based imaging analysis, particularly Deep Learning, provides cost effective and high-quality solutions to healthcare. In this article, we propose an imaging pipeline that utilizes three-colour mIF images (DAPI, PD-L1, and Pan-cytokeratin) as input and predicts the CPS using AI techniques. Our novel pipeline is composed of three modules employing algorithms of image processing, machine learning, and deep learning techniques. The first module of quality check (QC) detects and removes the image regions contaminated with sectioning and staining artefacts. The QC module ensures that only image regions free of the three common artefacts are used for downstream analysis. The second module of nuclear segmentation uses deep learning to segment and count nuclei in the DAPI images wherein our specialized method can accurately separate touching nuclei. The third module of cell phenotyping calculates CPS by identifying and counting PD-L1 positive cells and tumour cells. These modules are data-efficient and require only few manual annotations for training purposes. Using tumour biopsies from a clinical trial, we found that the CPS from the AI-based models shows a high Spearman correlation (78%, p = 0.003) to the pathologist-scored CPS.
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Affiliation(s)
- Abhishek Vahadane
- Rakuten India Enterprise Private Ltd, Bagmane Pallavi Tower #20, 1st Cross, Raja Ram Mohan Roy Road, S. R. Nagar, Bengaluru, Karnataka, 560027, India
| | - Shreya Sharma
- Rakuten India Enterprise Private Ltd, Bagmane Pallavi Tower #20, 1st Cross, Raja Ram Mohan Roy Road, S. R. Nagar, Bengaluru, Karnataka, 560027, India
| | - Devraj Mandal
- Rakuten India Enterprise Private Ltd, Bagmane Pallavi Tower #20, 1st Cross, Raja Ram Mohan Roy Road, S. R. Nagar, Bengaluru, Karnataka, 560027, India
| | - Madan Dabbeeru
- Rakuten India Enterprise Private Ltd, Bagmane Pallavi Tower #20, 1st Cross, Raja Ram Mohan Roy Road, S. R. Nagar, Bengaluru, Karnataka, 560027, India
| | | | | | - Shantanu Majumdar
- Rakuten India Enterprise Private Ltd, Bagmane Pallavi Tower #20, 1st Cross, Raja Ram Mohan Roy Road, S. R. Nagar, Bengaluru, Karnataka, 560027, India
| | - Chung-Wein Lee
- Rakuten Medical Inc., 11080 Roselle Street, San Diego, CA, 92121, USA.
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Multi-layer segmentation of retina OCT images via advanced U-net architecture. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2022.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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141
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Machine learning-assisted system using digital facial images to predict the clinical activity score in thyroid-associated orbitopathy. Sci Rep 2022; 12:22085. [PMID: 36543834 PMCID: PMC9772205 DOI: 10.1038/s41598-022-25887-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Abstract
Although the clinical activity score (CAS) is a validated scoring system for identifying disease activity of thyroid-associated orbitopathy (TAO), it may produce differing results depending on the evaluator, and an experienced ophthalmologist is required for accurate evaluation. In this study, we developed a machine learning (ML)-assisted system to mimic an expert's CAS assessment using digital facial images and evaluated its accuracy for predicting the CAS and diagnosing active TAO (CAS ≥ 3). An ML-assisted system was designed to assess five CAS components related to inflammatory signs (redness of the eyelids, redness of the conjunctiva, swelling of the eyelids, inflammation of the caruncle and/or plica, and conjunctival edema) in patients' facial images and to predict the CAS by considering two components of subjective symptoms (spontaneous retrobulbar pain and pain on gaze). To train and test the system, 3,060 cropped images from 1020 digital facial images of TAO patients were used. The reference CAS for each image was scored by three ophthalmologists, each with > 15 years of clinical experience. We repeated the experiments for 30 randomly split training and test sets at a ratio of 8:2. The sensitivity and specificity of the ML-assisted system for diagnosing active TAO were 72.7% and 83.2% in the test set constructed from the entire dataset. For the test set constructed from the dataset with consistent results for the three ophthalmologists, the sensitivity and specificity for diagnosing active TAO were 88.1% and 86.9%. In the test sets from the entire dataset and from the dataset with consistent results, 40.0% and 49.9% of the predicted CAS values were the same as the reference CAS, respectively. The system predicted the CAS within 1 point of the reference CAS in 84.6% and 89.0% of cases when tested using the entire dataset and in the dataset with consistent results, respectively. An ML-assisted system estimated the clinical activity of TAO and detect inflammatory active TAO with reasonable accuracy. The accuracy could be improved further by obtaining more data. This ML-assisted system can help evaluate the disease activity consistently as well as accurately and enable the early diagnosis and timely treatment of active TAO.
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Qadri S, Yki-Järvinen H. The quest for the missing links in fatty liver genetics: Deep learning to the rescue! Cell Rep Med 2022; 3:100862. [PMID: 36543096 PMCID: PMC9798017 DOI: 10.1016/j.xcrm.2022.100862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Park, MacLean, et al. conduct an exome-wide association study of liver fat content in the Penn Medicine BioBank.1 By leveraging machine learning-assisted analysis of clinical CT scans to quantify steatosis, they uncover previously undescribed liver fat-associated genetic variants.
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Affiliation(s)
- Sami Qadri
- University of Helsinki and Helsinki University Hospital, Helsinki, Finland,Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Hannele Yki-Järvinen
- University of Helsinki and Helsinki University Hospital, Helsinki, Finland,Minerva Foundation Institute for Medical Research, Helsinki, Finland,Corresponding author
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Using AI and computer vision to analyze technical proficiency in robotic surgery. Surg Endosc 2022; 37:3010-3017. [PMID: 36536082 DOI: 10.1007/s00464-022-09781-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 11/27/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Intraoperative skills assessment is time-consuming and subjective; an efficient and objective computer vision-based approach for feedback is desired. In this work, we aim to design and validate an interpretable automated method to evaluate technical proficiency using colorectal robotic surgery videos with artificial intelligence. METHODS 92 curated clips of peritoneal closure were characterized by both board-certified surgeons and a computer vision AI algorithm to compare the measures of surgical skill. For human ratings, six surgeons graded clips according to the GEARS assessment tool; for AI assessment, deep learning computer vision algorithms for surgical tool detection and tracking were developed and implemented. RESULTS For the GEARS category of efficiency, we observe a positive correlation between human expert ratings of technical efficiency and AI-determined total tool movement (r = - 0.72). Additionally, we show that more proficient surgeons perform closure with significantly less tool movement compared to less proficient surgeons (p < 0.001). For the GEARS category of bimanual dexterity, a positive correlation between expert ratings of bimanual dexterity and the AI model's calculated measure of bimanual movement based on simultaneous tool movement (r = 0.48) was also observed. On average, we also find that higher skill clips have significantly more simultaneous movement in both hands compared to lower skill clips (p < 0.001). CONCLUSIONS In this study, measurements of technical proficiency extracted from AI algorithms are shown to correlate with those given by expert surgeons. Although we target measurements of efficiency and bimanual dexterity, this work suggests that artificial intelligence through computer vision holds promise for efficiently standardizing grading of surgical technique, which may help in surgical skills training.
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Sangers TE, Wakkee M, Moolenburgh FJ, Nijsten T, Lugtenberg M. Towards successful implementation of artificial intelligence in skin cancer care: a qualitative study exploring the views of dermatologists and general practitioners. Arch Dermatol Res 2022; 315:1187-1195. [PMID: 36477587 PMCID: PMC9734890 DOI: 10.1007/s00403-022-02492-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 10/17/2022] [Accepted: 11/21/2022] [Indexed: 12/12/2022]
Abstract
Recent studies show promising potential for artificial intelligence (AI) to assist healthcare providers (HCPs) in skin cancer care. The aim of this study is to explore the views of dermatologists and general practitioners (GPs) regarding the successful implementation of AI when assisting HCPs in skin cancer care. We performed a qualitative focus group study, consisting of six focus groups with 16 dermatologists and 17 GPs, varying in prior knowledge and experience with AI, gender, and age. An in-depth inductive thematic content analysis was deployed. Perceived benefits, barriers, and preconditions were identified as main themes. Dermatologists and GPs perceive substantial benefits of AI, particularly an improved health outcome and care pathway between primary and secondary care. Doubts about accuracy, risk of health inequalities, and fear of replacement were among the most stressed barriers. Essential preconditions included adequate algorithm content, sufficient usability, and accessibility of AI. In conclusion, dermatologists and GPs perceive significant benefits from implementing AI in skin cancer care. However, to successfully implement AI, key barriers need to be addressed. Efforts should focus on ensuring algorithm transparency, validation, accessibility for all skin types, and adequate regulation of algorithms. Simultaneously, improving knowledge about AI could reduce the fear of replacement.
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Affiliation(s)
- Tobias E. Sangers
- Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Marlies Wakkee
- Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Folkert J. Moolenburgh
- Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Tamar Nijsten
- Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Marjolein Lugtenberg
- Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
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Development and Verification of Postural Control Assessment Using Deep-Learning-Based Pose Estimators: Towards Clinical Applications. Occup Ther Int 2022; 2022:6952999. [DOI: 10.1155/2022/6952999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/29/2022] [Accepted: 09/30/2022] [Indexed: 12/02/2022] Open
Abstract
Occupational therapists evaluate various aspects of a client’s occupational performance. Among these, postural control is one of the fundamental skills that need assessment. Recently, several methods have been proposed to estimate postural control abilities using deep-learning-based approaches. Such techniques allow for the potential to provide automated, precise, fine-grained quantitative indices simply by evaluating videos of a client engaging in a postural control task. However, the clinical applicability of these assessment tools requires further investigation. In the current study, we compared three deep-learning-based pose estimators to assess their clinical applicability in terms of accuracy of pose estimations and processing speed. In addition, we verified which of the proposed quantitative indices for postural controls best reflected the clinical evaluations of occupational therapists. A framework using deep-learning techniques broadens the possibility of quantifying clients’ postural control in a more fine-grained way compared with conventional coarse indices, which can lead to improved occupational therapy practice.
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146
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Huang J, Si H, Guo X, Zhong K. Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning. SENSORS (BASEL, SWITZERLAND) 2022; 22:9127. [PMID: 36501829 PMCID: PMC9737723 DOI: 10.3390/s22239127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/16/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
Distribution discrepancy is an intrinsic challenge in existing fingerprint-based indoor positioning system(s) (FIPS) due to real-time environmental variations; thus, the positioning model needs to be reconstructed frequently based on newly collected training data. However, it is expensive or impossible to collect adequate training samples to reconstruct the fingerprint database. Fortunately, transfer learning has proven to be an effective solution to mitigate the distribution discrepancy, enabling us to update the positioning model using newly collected training data in real time. However, in practical applications, traditional transfer learning algorithms no longer act well to feature space heterogeneity caused by different types or holding postures of fingerprint collection devices (such as smartphones). Moreover, current heterogeneous transfer methods typically require enough accurately labeled samples in the target domain, which is practically expensive and even unavailable. Aiming to solve these problems, a heterogeneous transfer learning framework based on co-occurrence data (HTL-CD) is proposed for FIPS, which can realize higher positioning accuracy and robustness against environmental changes without reconstructing the fingerprint database repeatedly. Specifically, the source domain samples are mapped into the feature space in the target domain, then the marginal and conditional distributions of the source and target samples are aligned in order to minimize the distribution divergence caused by collection device heterogeneity and environmental changes. Moreover, the utilized co-occurrence fingerprint data enables us to calculate correlation coefficients between heterogeneous samples without accurately labeled target samples. Furthermore, by resorting to the adopted correlation restriction mechanism, more valuable knowledge will be transferred to the target domain if the source samples are related to the target ones, which remarkably relieves the "negative transfer" issue. Real-world experimental performance implies that, even without accurately labeled samples in the target domain, the proposed HTL-CD can obtain at least 17.15% smaller average localization errors (ALEs) than existing transfer learning-based positioning methods, which further validates the effectiveness and superiority of our algorithm.
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Affiliation(s)
- Jian Huang
- Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Haonan Si
- Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Xiansheng Guo
- Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Ke Zhong
- Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
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147
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Smartphone video nystagmography using convolutional neural networks: ConVNG. J Neurol 2022; 270:2518-2530. [PMID: 36422668 PMCID: PMC10129923 DOI: 10.1007/s00415-022-11493-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 11/14/2022] [Accepted: 11/16/2022] [Indexed: 11/27/2022]
Abstract
Abstract
Background
Eye movement abnormalities are commonplace in neurological disorders. However, unaided eye movement assessments lack granularity. Although videooculography (VOG) improves diagnostic accuracy, resource intensiveness precludes its broad use. To bridge this care gap, we here validate a framework for smartphone video-based nystagmography capitalizing on recent computer vision advances.
Methods
A convolutional neural network was fine-tuned for pupil tracking using > 550 annotated frames: ConVNG. In a cross-sectional approach, slow-phase velocity of optokinetic nystagmus was calculated in 10 subjects using ConVNG and VOG. Equivalence of accuracy and precision was assessed using the “two one-sample t-test” (TOST) and Bayesian interval-null approaches. ConVNG was systematically compared to OpenFace and MediaPipe as computer vision (CV) benchmarks for gaze estimation.
Results
ConVNG tracking accuracy reached 9–15% of an average pupil diameter. In a fully independent clinical video dataset, ConVNG robustly detected pupil keypoints (median prediction confidence 0.85). SPV measurement accuracy was equivalent to VOG (TOST p < 0.017; Bayes factors (BF) > 24). ConVNG, but not MediaPipe, achieved equivalence to VOG in all SPV calculations. Median precision was 0.30°/s for ConVNG, 0.7°/s for MediaPipe and 0.12°/s for VOG. ConVNG precision was significantly higher than MediaPipe in vertical planes, but both algorithms’ precision was inferior to VOG.
Conclusions
ConVNG enables offline smartphone video nystagmography with an accuracy comparable to VOG and significantly higher precision than MediaPipe, a benchmark computer vision application for gaze estimation. This serves as a blueprint for highly accessible tools with potential to accelerate progress toward precise and personalized Medicine.
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Al-Garadi MA, Yang YC, Sarker A. The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges. Healthcare (Basel) 2022; 10:2270. [PMID: 36421593 PMCID: PMC9690240 DOI: 10.3390/healthcare10112270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/03/2022] [Accepted: 11/06/2022] [Indexed: 07/30/2023] Open
Abstract
The COVID-19 pandemic is the most devastating public health crisis in at least a century and has affected the lives of billions of people worldwide in unprecedented ways. Compared to pandemics of this scale in the past, societies are now equipped with advanced technologies that can mitigate the impacts of pandemics if utilized appropriately. However, opportunities are currently not fully utilized, particularly at the intersection of data science and health. Health-related big data and technological advances have the potential to significantly aid the fight against such pandemics, including the current pandemic's ongoing and long-term impacts. Specifically, the field of natural language processing (NLP) has enormous potential at a time when vast amounts of text-based data are continuously generated from a multitude of sources, such as health/hospital systems, published medical literature, and social media. Effectively mitigating the impacts of the pandemic requires tackling challenges associated with the application and deployment of NLP systems. In this paper, we review the applications of NLP to address diverse aspects of the COVID-19 pandemic. We outline key NLP-related advances on a chosen set of topics reported in the literature and discuss the opportunities and challenges associated with applying NLP during the current pandemic and future ones. These opportunities and challenges can guide future research aimed at improving the current health and social response systems and pandemic preparedness.
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Affiliation(s)
- Mohammed Ali Al-Garadi
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37240, USA
| | - Yuan-Chi Yang
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Abeed Sarker
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA
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He L, Ai Q, Lei Y, Pan L, Ren Y, Xu Z. Edge Enhancement Improves Adversarial Robustness in Image Classification. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.10.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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150
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Liu X, Flanagan C, Fang J, Lei Y, McGrath L, Wang J, Guo X, Guo J, McGrath H, Han Y. Comparative analysis of popular predictors for difficult laryngoscopy using hybrid intelligent detection methods. Heliyon 2022; 8:e11761. [DOI: 10.1016/j.heliyon.2022.e11761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/27/2022] [Accepted: 11/14/2022] [Indexed: 11/24/2022] Open
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