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Zhu B, Zhang C, Sui Y, Li L. FaceMotionPreserve: a generative approach for facial de-identification and medical information preservation. Sci Rep 2024; 14:17275. [PMID: 39068186 PMCID: PMC11283491 DOI: 10.1038/s41598-024-67989-5] [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: 01/12/2024] [Accepted: 07/18/2024] [Indexed: 07/30/2024] Open
Abstract
Telemedicine and video-based diagnosis have raised significant concerns regarding the protection of facial privacy. Effective de-identification methods require the preservation of diagnostic information related to normal and pathological facial movements, which play a crucial role in the diagnosis of various movement, neurological, and psychiatric disorders. In this work, we have developed FaceMotionPreserve , a deep generative model-based approach that transforms patients' facial identities while preserving facial dynamics with a novel face dynamic similarity module to enhance facial landmark consistency. We collected test videos from patients with Parkinson's disease recruited via telemedicine for evaluation of model performance and clinical applicability. The performance of FaceMotionPreserve was quantitatively evaluated based on neurologist diagnostic consistency, critical facial behavior fidelity, and correlation of general facial dynamics. In addition, we further validated the robustness and advancements of our model in preserving medical information with clinical examination videos from a different cohort of patients. FaceMotionPreserve is applicable to real-time integration, safeguarding facial privacy while retaining crucial medical information associated with facial movements to address concerns in telemedicine, and facilitating safer and more collaborative medical data sharing.
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Affiliation(s)
- Bingquan Zhu
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing, 100084, China
| | - Chen Zhang
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing, 100084, China
| | - Yanan Sui
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing, 100084, China.
| | - Luming Li
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing, 100084, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, China
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Sheerah HA, AlSalamah S, Alsalamah SA, Lu CT, Arafa A, Zaatari E, Alhomod A, Pujari S, Labrique A. The Rise of Virtual Health Care: Transforming the Health Care Landscape in the Kingdom of Saudi Arabia: A Review Article. Telemed J E Health 2024. [PMID: 38984415 DOI: 10.1089/tmj.2024.0114] [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] [Indexed: 07/11/2024] Open
Abstract
BACKGROUND The rise of virtual healthcare underscores the transformative influence of digital technologies in reshaping the healthcare landscape. As technology advances and the global demand for accessible and convenient healthcare services escalates, the virtual healthcare sector is gaining unprecedented momentum. Saudi Arabia, with its ambitious Vision 2030 initiative, is actively embracing digital innovation in the healthcare sector. METHODS In this narrative review, we discussed the key drivers and prospects of virtual healthcare in Saudi Arabia, highlighting its potential to enhance healthcare accessibility, quality, and patient outcomes. We also summarized the role of the COVID-19 pandemic in the digital transformation of healthcare in the country. Healthcare services provided by Seha Virtual Hospital in Saudi Arabia, the world's largest and Middle East's first virtual hospital, were also described. Finally, we proposed a roadmap for the future development of virtual health in the country. RESULTS AND CONCLUSIONS The integration of virtual healthcare into the existing healthcare system can enhance patient experiences, improve outcomes, and contribute to the overall well-being of the population. However, careful planning, collaboration, and investment are essential to overcome the challenges and ensure the successful implementation and sustainability of virtual healthcare in the country.
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Affiliation(s)
- Haytham A Sheerah
- Ministry of Health, Office of the Vice Minister of Health, Riyadh, Saudi Arabia
| | - Shada AlSalamah
- Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
- Department of Digital Health and Innovation, Science Division, World Health Organization, Geneva, Switzerland
| | - Sara A Alsalamah
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA
| | - Chang-Tien Lu
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA
| | - Ahmed Arafa
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan
- Department of Public Health and Community Medicine, Faculty of Medicine, Beni-Suef University, Beni-Suef, Egypt
| | - Ezzedine Zaatari
- Ministry of Health, Office of the Vice Minister of Health, Riyadh, Saudi Arabia
| | - Abdulaziz Alhomod
- Ministry of Health, SEHA Virtual Hospital, Riyadh, Saudi Arabia
- Emergency Medicine Administration, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Sameer Pujari
- Department of Digital Health and Innovation, Science Division, World Health Organization, Geneva, Switzerland
| | - Alain Labrique
- Department of International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland,United States
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P.L L, Vaddi R, Elish MO, Gonuguntla V, Yellampalli SS. CSDNet: A Novel Deep Learning Framework for Improved Cataract State Detection. Diagnostics (Basel) 2024; 14:983. [PMID: 38786279 PMCID: PMC11120207 DOI: 10.3390/diagnostics14100983] [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: 03/31/2024] [Revised: 04/22/2024] [Accepted: 04/25/2024] [Indexed: 05/25/2024] Open
Abstract
Cataracts, known for lens clouding and being a common cause of visual impairment, persist as a primary contributor to vision loss and blindness, presenting notable diagnostic and prognostic challenges. This work presents a novel framework called the Cataract States Detection Network (CSDNet), which utilizes deep learning methods to improve the detection of cataract states. The aim is to create a framework that is more lightweight and adaptable for use in environments or devices with limited memory or storage capacity. This involves reducing the number of trainable parameters while still allowing for effective learning of representations from data. Additionally, the framework is designed to be suitable for real-time or near-real-time applications where rapid inference is essential. This study utilizes cataract and normal images from the Ocular Disease Intelligent Recognition (ODIR) database. The suggested model employs smaller kernels, fewer training parameters, and layers to efficiently decrease the number of trainable parameters, thereby lowering computational costs and average running time compared to other pre-trained models such as VGG19, ResNet50, DenseNet201, MIRNet, Inception V3, Xception, and Efficient net B0. The experimental results illustrate that the proposed approach achieves a binary classification accuracy of 97.24% (normal or cataract) and an average cataract state detection accuracy of 98.17% (normal, grade 1-minimal cloudiness, grade 2-immature cataract, grade 3-mature cataract, and grade 4-hyper mature cataract), competing with state-of-the-art cataract detection methods. The resulting model is lightweight at 17 MB and has fewer trainable parameters (175, 617), making it suitable for deployment in environments or devices with constrained memory or storage capacity. With a runtime of 212 ms, it is well-suited for real-time or near-real-time applications requiring rapid inference.
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Affiliation(s)
- Lahari P.L
- Department of Electronics and Communication Engineering, SRM University AP, Andhra Pradesh, India; (L.P.); (R.V.)
| | - Ramesh Vaddi
- Department of Electronics and Communication Engineering, SRM University AP, Andhra Pradesh, India; (L.P.); (R.V.)
| | - Mahmoud O. Elish
- Computer Science Department, Gulf University for Science and Technology, Hawally 32093, Kuwait;
- GUST Engineering and Applied Innovation Research Center, Gulf University for Science and Technology, Hawally 32093, Kuwait
| | - Venkateswarlu Gonuguntla
- Symbiosis Centre for Medical Image Analysis, Symbiosis International (Deemed University), Pune, India
| | - Siva Sankar Yellampalli
- Department of Electronics and Communication Engineering, SRM University AP, Andhra Pradesh, India; (L.P.); (R.V.)
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El-Sherif DM, Ahmed AA, Sharif AF, Elzarif MT, Abouzid M. Greenway of Digital Health Technology During COVID-19 Crisis: Bibliometric Analysis, Challenges, and Future Perspective. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1458:315-334. [PMID: 39102206 DOI: 10.1007/978-3-031-61943-4_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/06/2024]
Abstract
Digital health has transformed the healthcare landscape by leveraging technology to improve patient outcomes and access to medical services. The COVID-19 pandemic has highlighted the urgent need for digital healthcare solutions that can mitigate the impact of the outbreak while ensuring patient safety. In this chapter, we delve into how digital health technologies such as telemedicine, mobile apps, and wearable devices can provide personalized care, reduce healthcare provider burden, and lower healthcare costs. We also explore the creation of a greenway of digital healthcare that safeguards patient confidentiality, enables efficient communication, and ensures cost-effective payment systems. This chapter showcases the potential of digital health to revolutionize healthcare delivery while ensuring patient well-being and medical staff satisfaction.
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Affiliation(s)
- Dina M El-Sherif
- Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, School of Food Science and Technology, Jiangnan University, Wuxi, 214122, China.
- National Institute of Oceanography and Fisheries (NIOF), Cairo, Egypt.
| | - Alhassan Ali Ahmed
- Department of Bioinformatics and Computational Biology, Poznan University of Medical Sciences, 60-781, Poznan, Poland
- Doctoral School, Poznan University of Medical Sciences, 60-812, Poznan, Poland
| | - Asmaa Fady Sharif
- Department of Forensic Medicine and Clinical Toxicology, Faculty of Medicine, Tanta University, Tanta, Egypt
- Clinical Medical Sciences Department, College of Medicine, Dar Al-Uloom University, Riyadh, Saudi Arabia
| | | | - Mohamed Abouzid
- Doctoral School, Poznan University of Medical Sciences, 60-812, Poznan, Poland
- Department of Physical Pharmacy and Pharmacokinetics, Faculty of Pharmacy, Poznan University of Medical Sciences, Rokietnicka 3 St., 60-806, Poznan, Poland
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Abbo LM, Vasiliu-Feltes I. Disrupting the infectious disease ecosystem in the digital precision health era innovations and converging emerging technologies. Antimicrob Agents Chemother 2023; 67:e0075123. [PMID: 37724872 PMCID: PMC10583659 DOI: 10.1128/aac.00751-23] [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] [Indexed: 09/21/2023] Open
Abstract
This commentary explores the convergence of precision health and evolving technologies, including the critical role of artificial intelligence (AI) and emerging technologies in infectious diseases (ID) and microbiology. We discuss their disruptive impact on the ID ecosystem and examine the transformative potential of frontier technologies in precision health, public health, and global health when deployed with robust ethical and data governance guardrails in place.
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Affiliation(s)
- Lilian M. Abbo
- Jackson Health System, Miami, Florida, USA
- Division of Infectious Diseases, Miller School of Medicine, University of Miami, Miami, Florida, USA
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Sivangi KB, Amilpur S, Dasari CM. ReGen-DTI: A novel generative drug target interaction model for predicting potential drug candidates against SARS-COV2. Comput Biol Chem 2023; 106:107927. [PMID: 37499436 DOI: 10.1016/j.compbiolchem.2023.107927] [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: 05/16/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 07/29/2023]
Abstract
Covid-19 has caused massive numbers of infections and fatalities globally. In response, there has been a large-scale experimental and computational research effort to study and develop drugs. Towards this, Deep learning techniques are used for the generation of potential novel drug candidates that are proven to be effective against exploring large molecular search spaces. Recent advances in reinforcement learning in conjunction with generative techniques has proven to be a promising field in the area of drug discovery. In this regard, we propose a generative drug discovery approach using reinforcement techniques for sampling novel molecules that bind to the main protease of SARS-COV2. The generative method reported significant validity scores for the generated novel molecules and captured the underlying features of the training molecules. Further, the model is fine-tuned on existing re-purposed molecules which are active towards specific target proteins based on similarity metrics. Upon fine tuning the model generated 92.71% valid, 93.55% unique, and 100% novel molecules. Unlike previous methods which are dependent on docking procedures, we proposed a deep learning based novel drug target interaction (DTI) model to find the binding affinity between candidate molecules and target protease sequence. Finally, the binding affinity of the generated molecules is predicted against the 3CLPro main protease by using the proposed DTI model. Most of the generated molecules have shown binding affinity scores <100 nM (lower the better), which are significantly better compared to the existing commercial drugs including Remdesevir.
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Affiliation(s)
- Kaushik Bhargav Sivangi
- Indian Institute of Information Technology, Sri City, Chittoor, 517646, Andhra Pradesh, India
| | - Santhosh Amilpur
- Indian Institute of Information Technology, Sri City, Chittoor, 517646, Andhra Pradesh, India
| | - Chandra Mohan Dasari
- Indian Institute of Information Technology, Sri City, Chittoor, 517646, Andhra Pradesh, India.
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Tramontano A, Tamburis O, Cioce S, Venticinque S, Magliulo M. Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation. Front Digit Health 2023; 5:1222898. [PMID: 37583833 PMCID: PMC10424792 DOI: 10.3389/fdgth.2023.1222898] [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: 06/01/2023] [Accepted: 07/10/2023] [Indexed: 08/17/2023] Open
Abstract
Medical devices (MDs) have been designed for monitoring the parameters of patients in many sectors. Nonetheless, despite being high-performing and reliable, they often turn out to be expensive and intrusive. In addition, MDs are almost exclusively used in controlled, hospital-based environments. Paving a path of technological innovation in the clinical field, a very active line of research is currently dealing with the possibility to rely on non-medical-graded low-cost devices, to develop unattended telemedicine (TM) solutions aimed at non-invasively gathering data, signals, and images. In this article, a TM solution is proposed for monitoring the heart rate (HR) of patients during sleep. A remote patient monitoring system (RPMS) featuring a smart belt equipped with pressure sensors for ballistocardiogram (BCG) signals sampling was deployed. A field trial was then conducted over a 2-month period on 24 volunteers, who also agreed to wear a finger pulse oximeter capable of producing a photoplethysmography (PPG) signal as the gold standard, to examine the feasibility of the solution via the estimation of HR values from the collected BCG signals. For this purpose, two of the highest-performing approaches for HR estimation from BCG signals, one algorithmic and the other based on a convolutional neural network (CNN), were retrieved from the literature and updated for a TM-related use case. Finally, HR estimation performances were assessed in terms of patient-wise mean absolute error (MAE). Results retrieved from the literature (controlled environment) outperformed those achieved in the experimentation (TM environment) by 29% (MAE = 4.24 vs. 5.46, algorithmic approach) and 52% (MAE = 2.32 vs. 3.54, CNN-based approach), respectively. Nonetheless, a low packet loss ratio, restrained elaboration time of the collected biomedical big data, low-cost deployment, and positive feedback from the users, demonstrate the robustness, reliability, and applicability of the proposed TM solution. In light of this, further steps will be planned to fulfill new targets, such as evaluation of respiratory rate (RR), and pattern assessment of the movement of the participants overnight.
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Affiliation(s)
- Adriano Tramontano
- Institute of Biostructures and Bioimaging, National Research Council (IBB–CNR), Naples, Italy
| | - Oscar Tamburis
- Institute of Biostructures and Bioimaging, National Research Council (IBB–CNR), Naples, Italy
- Department of Veterinary Medicine and Animal Productions, University of Naples “Federico II”, Naples, Italy
| | - Salvatore Cioce
- Institute of Biostructures and Bioimaging, National Research Council (IBB–CNR), Naples, Italy
| | - Salvatore Venticinque
- Department of Engineering, University of Campania “Luigi Vanvitelli”, Aversa (CE), Italy
| | - Mario Magliulo
- Institute of Biostructures and Bioimaging, National Research Council (IBB–CNR), Naples, Italy
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Clipped DeepControl: Deep neural network two-dimensional pulse design with an amplitude constraint layer. Artif Intell Med 2023; 135:102460. [PMID: 36628795 DOI: 10.1016/j.artmed.2022.102460] [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: 01/21/2022] [Revised: 11/18/2022] [Accepted: 11/18/2022] [Indexed: 11/27/2022]
Abstract
Advanced radio-frequency pulse design used in magnetic resonance imaging has recently been demonstrated with deep learning of (convolutional) neural networks and reinforcement learning. For two-dimensionally selective radio-frequency pulses, the (convolutional) neural network pulse prediction time (a few milliseconds) was in comparison more than three orders of magnitude faster than the conventional optimal control computation. The network pulses were from the supervised training capable of compensating scan-subject dependent inhomogeneities of B0 and B1+ fields. Unfortunately, the network presented with a small percentage of pulse amplitude overshoots in the test subset, despite the optimal control pulses used in training were fully constrained. Here, we have extended the convolutional neural network with a custom-made clipping layer that completely eliminates the risk of pulse amplitude overshoots, while preserving the ability to compensate for the inhomogeneous field conditions.
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