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Huffman N, Pasqualini I, Khan ST, Klika AK, Deren ME, Jin Y, Kunze KN, Piuzzi NS. Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review. JBJS Rev 2024; 12:01874474-202403000-00006. [PMID: 38466797 DOI: 10.2106/jbjs.rvw.23.00232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.
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Affiliation(s)
- Nickelas Huffman
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | | | - Shujaa T Khan
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Alison K Klika
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Matthew E Deren
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Yuxuan Jin
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York
| | - Nicolas S Piuzzi
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
- Department of Biomedical Engineering, Cleveland Clinic Foundation, Cleveland, Ohio
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Tsilivigkos C, Athanasopoulos M, Micco RD, Giotakis A, Mastronikolis NS, Mulita F, Verras GI, Maroulis I, Giotakis E. Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review. J Clin Med 2023; 12:6973. [PMID: 38002588 PMCID: PMC10672270 DOI: 10.3390/jcm12226973] [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: 10/14/2023] [Revised: 11/02/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
Over the last decades, the field of medicine has witnessed significant progress in artificial intelligence (AI), the Internet of Medical Things (IoMT), and deep learning (DL) systems. Otorhinolaryngology, and imaging in its various subspecialties, has not remained untouched by this transformative trend. As the medical landscape evolves, the integration of these technologies becomes imperative in augmenting patient care, fostering innovation, and actively participating in the ever-evolving synergy between computer vision techniques in otorhinolaryngology and AI. To that end, we conducted a thorough search on MEDLINE for papers published until June 2023, utilizing the keywords 'otorhinolaryngology', 'imaging', 'computer vision', 'artificial intelligence', and 'deep learning', and at the same time conducted manual searching in the references section of the articles included in our manuscript. Our search culminated in the retrieval of 121 related articles, which were subsequently subdivided into the following categories: imaging in head and neck, otology, and rhinology. Our objective is to provide a comprehensive introduction to this burgeoning field, tailored for both experienced specialists and aspiring residents in the domain of deep learning algorithms in imaging techniques in otorhinolaryngology.
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Affiliation(s)
- Christos Tsilivigkos
- 1st Department of Otolaryngology, National and Kapodistrian University of Athens, Hippocrateion Hospital, 115 27 Athens, Greece; (A.G.); (E.G.)
| | - Michail Athanasopoulos
- Department of Otolaryngology, University Hospital of Patras, 265 04 Patras, Greece; (M.A.); (N.S.M.)
| | - Riccardo di Micco
- Department of Otolaryngology and Head and Neck Surgery, Medical School of Hannover, 30625 Hannover, Germany;
| | - Aris Giotakis
- 1st Department of Otolaryngology, National and Kapodistrian University of Athens, Hippocrateion Hospital, 115 27 Athens, Greece; (A.G.); (E.G.)
| | - Nicholas S. Mastronikolis
- Department of Otolaryngology, University Hospital of Patras, 265 04 Patras, Greece; (M.A.); (N.S.M.)
| | - Francesk Mulita
- Department of Surgery, University Hospital of Patras, 265 04 Patras, Greece; (G.-I.V.); (I.M.)
| | - Georgios-Ioannis Verras
- Department of Surgery, University Hospital of Patras, 265 04 Patras, Greece; (G.-I.V.); (I.M.)
| | - Ioannis Maroulis
- Department of Surgery, University Hospital of Patras, 265 04 Patras, Greece; (G.-I.V.); (I.M.)
| | - Evangelos Giotakis
- 1st Department of Otolaryngology, National and Kapodistrian University of Athens, Hippocrateion Hospital, 115 27 Athens, Greece; (A.G.); (E.G.)
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Watkins ME. Designing an Effective Organizational Culture to Guard Against the Cyber Risks of Emerging Technologies. J Healthc Manag 2023; 68:239-250. [PMID: 37410988 DOI: 10.1097/jhm-d-23-00097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
The Internet of Medical Things (IoMT) and interoperable technologies have transformed how patient data affect medical care; such technological innovations revolutionize how healthcare organizations (HCOs) improve cost, quality, and access. New cyber risks, however, accompany developing cyber ecosystems. Although immediate data exchange is beneficial, risk arises from the IoMT's increased susceptibility to human influence. The success of quality care relies on protecting health information technology (HIT) against newly developing cyber vulnerabilities. Therefore, managers must be just as invested in their HCO's cybersecurity protocols as cybercriminals are in bypassing those protocols. This essay proposes a healthcare cyber resiliency model that leverages human and technical factors through a cycle of feedback and process improvement. It intends to equip healthcare administrators with the foundational philosophy necessary to secure their emerging technologies.
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Raj A, Prakash S. Privacy preservation of the internet of medical things using blockchain. HEALTH SERVICES AND OUTCOMES RESEARCH METHODOLOGY 2023. [DOI: 10.1007/s10742-023-00306-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain. Processes (Basel) 2023. [DOI: 10.3390/pr11030757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023] Open
Abstract
Every aspect of the 21st century has undergone a revolution because of the Internet of Things (IoT) and smart computing technologies. These technologies are applied in many different ways, from monitoring the state of crops and the moisture level of the soil in real-time to using drones to help with chores such as spraying pesticides. The extensive integration of both recent IT and conventional agriculture has brought in the phase of agriculture 4.0, often known as smart agriculture. Agriculture intelligence and automation are addressed by smart agriculture. However, with the advancement of agriculture brought about by recent digital technology, information security challenges cannot be overlooked. The article begins by providing an overview of the development of agriculture 4.0 with pros and cons. This study focused on layered architectural design, identified security issues, and presented security demands and upcoming prospects. In addition to that, we propose a security architectural framework for agriculture 4.0 that combines blockchain technology, fog computing, and software-defined networking. The suggested framework combines Ethereum blockchain and software-defined networking technologies on an open-source IoT platform. It is then tested with three different cases under a DDoS attack. The results of the performance analysis show that overall, the proposed security framework has performed well.
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Devi DH, Duraisamy K, Armghan A, Alsharari M, Aliqab K, Sorathiya V, Das S, Rashid N. 5G Technology in Healthcare and Wearable Devices: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23052519. [PMID: 36904721 PMCID: PMC10007389 DOI: 10.3390/s23052519] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 06/12/2023]
Abstract
Wearable devices with 5G technology are currently more ingrained in our daily lives, and they will now be a part of our bodies too. The requirement for personal health monitoring and preventive disease is increasing due to the predictable dramatic increase in the number of aging people. Technologies with 5G in wearables and healthcare can intensely reduce the cost of diagnosing and preventing diseases and saving patient lives. This paper reviewed the benefits of 5G technologies, which are implemented in healthcare and wearable devices such as patient health monitoring using 5G, continuous monitoring of chronic diseases using 5G, management of preventing infectious diseases using 5G, robotic surgery using 5G, and 5G with future of wearables. It has the potential to have a direct effect on clinical decision making. This technology could improve patient rehabilitation outside of hospitals and monitor human physical activity continuously. This paper draws the conclusion that the widespread adoption of 5G technology by healthcare systems enables sick people to access specialists who would be unavailable and receive correct care more conveniently.
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Affiliation(s)
- Delshi Howsalya Devi
- Department of AI & DS, Karpaga Vinayaga College of Engineering and Technology, Chengalpattu 603308, Tamil Nadu, India
| | - Kumutha Duraisamy
- Department of Biomedical Engineering, Karpaga Vinayaga College of Engineering and Technology, Chengalpattu 603308, Tamil Nadu, India
| | - Ammar Armghan
- Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
| | - Meshari Alsharari
- Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
| | - Khaled Aliqab
- Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
| | - Vishal Sorathiya
- Faculty of Engineering and Technology, Parul Institute of Engineering and Technology, Parul University, Waghodia Road, Vadodara 391760, Gujarat, India
| | - Sudipta Das
- Department of Electronics and Communication Engineering, IMPS College of Engineering and Technology, Malda 732103, West Bengal, India
| | - Nasr Rashid
- Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
- Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Nasr City, Cairo 11884, Egypt
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Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for Breast Cancer Risk Prediction Using Fuzzy C-Means and CNN Model. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:1491955. [PMID: 36760835 PMCID: PMC9904922 DOI: 10.1155/2023/1491955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 07/23/2022] [Accepted: 11/25/2022] [Indexed: 02/02/2023]
Abstract
The research interest in this field is that females are not aware of their health conditions until they develop tumour, especially when breast cancer is concerned. The breast cancer risk factors include genetics, heredity, and sedentary lifestyle. The prime concern for the mortality rate among females is breast cancer, and breast cancer is on the rise, both in rural and urban India. Women aged 45 or above are more vulnerable to this disease. Images are more effective at depicting information as compared to text. With the advancement in technology, several computerized techniques have come up to extract hidden information from the images. The processed images have found their application in several sectors and medical science is one of them. Disease-like breast cancer affects most women universally and it happens due to the existence of breast masses in the breast region for the development of breast cancer in women. Timely breast cancer detection can also increase the rate of effective treatment and the survival of women suffering from breast cancer. This work elaborates the method of performing hybrid segmentation techniques using CLAHE, morphological operations on mammogram images, and classified images using deep learning. Images from the MIAS database have been used to obtain readings for parameters: threshold, accuracy, sensitivity, specificity rate, biopsy rate, or a combination of all the parameters and many others under study.
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Cesario A, D’Oria M, Simone I, Patarnello S, Valentini V, Scambia G. Open Innovation as the Catalyst in the Personalized Medicine to Personalized Digital Medicine Transition. J Pers Med 2022; 12:jpm12091500. [PMID: 36143285 PMCID: PMC9505138 DOI: 10.3390/jpm12091500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/08/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022] Open
Abstract
Personalized medicine (PM) bridges several disciplines for understanding and addressing prevalent, complex, or rare situations in human health (e.g., complex phenotyping, risk stratification, etc.); therefore, digital and technological solutions have been integrated in the field to boost innovation and new knowledge generation. The open innovation (OI) paradigm proposes a method by which to respectfully manage disruptive change in biomedical organizations, as experienced by many organizations during digital transformation and the COVID-19 pandemic. In this article, we focus on how this paradigm has catalyzed the transition from PM to personalized digital medicine in a large-volume research hospital. Methods, challenges, and results are discussed. This case study is an endeavor to confirm that OI strategies could help manage urgent needs from the healthcare environment, while achieving sustainability-oriented, accountable innovation.
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Affiliation(s)
- Alfredo Cesario
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00182 Rome, Italy
- Gemelli Digital Medicine & Health, 00182 Rome, Italy
- Gemelli Generator, Gemelli Science and Technology Park (G-STeP), Fondazione Policlinico Universitario A. Gemelli IRCCS, 00182 Rome, Italy
| | - Marika D’Oria
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00182 Rome, Italy
- Correspondence:
| | - Irene Simone
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00182 Rome, Italy
- Gemelli Digital Medicine & Health, 00182 Rome, Italy
| | - Stefano Patarnello
- Gemelli Digital Medicine & Health, 00182 Rome, Italy
- Gemelli Generator, Gemelli Science and Technology Park (G-STeP), Fondazione Policlinico Universitario A. Gemelli IRCCS, 00182 Rome, Italy
| | - Vincenzo Valentini
- Gemelli Digital Medicine & Health, 00182 Rome, Italy
- Gemelli Generator, Gemelli Science and Technology Park (G-STeP), Fondazione Policlinico Universitario A. Gemelli IRCCS, 00182 Rome, Italy
- Department of Diagnostic Imaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00182 Rome, Italy
| | - Giovanni Scambia
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00182 Rome, Italy
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