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Nofal MN, Al Awayshish MM, Yousef AJ, Alamaren AM, Al-Rabadi ZI, Haddad DS, Al-Rbaihat YA, Al-Qusous YN. General surgery educational resources for Jordanian medical students. Surg Open Sci 2024; 20:62-65. [PMID: 38911059 PMCID: PMC11190551 DOI: 10.1016/j.sopen.2024.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 05/21/2024] [Indexed: 06/25/2024] Open
Abstract
Background To outline the resources deemed most beneficial to medical students during their general surgery clerkship, as well as to examine their link to students' general surgery scores and the usage of artificial intelligence in general surgery study. Methods A retrospective survey of Jordanian medical students from six universities was done between March and June 2023 using a 7-item questionnaire covering questions concerning general surgery study methods and scores. Descriptive statistics were used to evaluate demographic data. Chi-square is used to evaluate categorical data, with a P value <0.05 deemed significant. Results The average age of respondents was 23.3 years, and 54.2 % of the respondents were females, 47.8 % were from Mutah University. Most students (48.2 %) relied on tutor lectures. Students who studied through instructor lectures had the highest grades (9 % excellent, 17 % very good), followed by students who studied using surgery textbooks (6.8 % and 14.6 %, respectively). The relationship between the study method and academic achievement was statistically significant (P < 0.05). Conclusions Traditional face-to-face learning with instructor lectures and surgery textbooks is still the most efficient approach to attain the greatest scores. Medical students are still underutilizing artificial intelligence.
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Affiliation(s)
- Mohammad Nebih Nofal
- Department of General Surgery and Anesthesia, Faculty of Medicine, Mutah University, Karak, Jordan
| | | | - Ali Jad Yousef
- Department of General Surgery and Anesthesia, Faculty of Medicine, Mutah University, Karak 61710, Jordan
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Morya VK, Lee HW, Shahid H, Magar AG, Lee JH, Kim JH, Jun L, Noh KC. Application of ChatGPT for Orthopedic Surgeries and Patient Care. Clin Orthop Surg 2024; 16:347-356. [PMID: 38827766 PMCID: PMC11130626 DOI: 10.4055/cios23181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 11/15/2023] [Accepted: 12/12/2023] [Indexed: 06/05/2024] Open
Abstract
Artificial intelligence (AI) has rapidly transformed various aspects of life, and the launch of the chatbot "ChatGPT" by OpenAI in November 2022 has garnered significant attention and user appreciation. ChatGPT utilizes natural language processing based on a "generative pre-trained transfer" (GPT) model, specifically the transformer architecture, to generate human-like responses to a wide range of questions and topics. Equipped with approximately 57 billion words and 175 billion parameters from online data, ChatGPT has potential applications in medicine and orthopedics. One of its key strengths is its personalized, easy-to-understand, and adaptive response, which allows it to learn continuously through user interaction. This article discusses how AI, especially ChatGPT, presents numerous opportunities in orthopedics, ranging from preoperative planning and surgical techniques to patient education and medical support. Although ChatGPT's user-friendly responses and adaptive capabilities are laudable, its limitations, including biased responses and ethical concerns, necessitate its cautious and responsible use. Surgeons and healthcare providers should leverage the strengths of the ChatGPT while recognizing its current limitations and verifying critical information through independent research and expert opinions. As AI technology continues to evolve, ChatGPT may become a valuable tool in orthopedic education and patient care, leading to improved outcomes and efficiency in healthcare delivery. The integration of AI into orthopedics offers substantial benefits but requires careful consideration and continuous improvement.
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Affiliation(s)
- Vivek Kumar Morya
- Department of Orthopedic Surgery, Hallym University Kangnam Sacred Heart Hospital, Seoul, Korea
| | - Ho-Won Lee
- Department of Orthopedic Surgery, Hallym University Kangnam Sacred Heart Hospital, Seoul, Korea
| | - Hamzah Shahid
- Department of Orthopedic Surgery, Hallym University Kangnam Sacred Heart Hospital, Seoul, Korea
| | - Anuja Gajanan Magar
- Department of Orthopedic Surgery, Hallym University Kangnam Sacred Heart Hospital, Seoul, Korea
| | - Ju-Hyung Lee
- Department of Orthopedic Surgery, Hallym University Kangnam Sacred Heart Hospital, Seoul, Korea
| | - Jae-Hyung Kim
- Department of Orthopedic Surgery, Hallym University Kangnam Sacred Heart Hospital, Seoul, Korea
| | - Lang Jun
- Department of Orthopedic Surgery, Hallym University Kangnam Sacred Heart Hospital, Seoul, Korea
| | - Kyu-Cheol Noh
- Department of Orthopedic Surgery, Hallym University Kangnam Sacred Heart Hospital, Seoul, Korea
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Wang N, Yang S, Gao Q, Jin X. Immersive teaching using virtual reality technology to improve ophthalmic surgical skills for medical postgraduate students. Postgrad Med 2024. [PMID: 38819302 DOI: 10.1080/00325481.2024.2363171] [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: 04/08/2024] [Accepted: 05/28/2024] [Indexed: 06/01/2024]
Abstract
Medical education is primarily based on practical schooling and the accumulation of experience and skills, which is important for the growth and development of young ophthalmic surgeons. However, present learning and refresher methods are constrained by several factors. Nevertheless, virtual reality (VR) technology has considerably contributed to medical training worldwide, providing convenient and practical auxiliary value for the selection of students' sub-majors. Moreover, it offers previously inaccessible surgical step training, scenario simulations, and immersive evaluation exams. This paper outlines the current applications of VR immersive teaching methods for ophthalmic surgery interns.
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Affiliation(s)
- Ning Wang
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Shuo Yang
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Qi Gao
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xiuming Jin
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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Cheng C, Liang X, Guo D, Xie D. Application of Artificial Intelligence in Shoulder Pathology. Diagnostics (Basel) 2024; 14:1091. [PMID: 38893618 PMCID: PMC11171621 DOI: 10.3390/diagnostics14111091] [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: 04/02/2024] [Revised: 05/16/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
Artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopedic-specific problems. One particular area of investigation focuses on shoulder pathology, which is a range of disorders or abnormalities of the shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate current AI applications in shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI technology are also discussed.
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Affiliation(s)
- Cong Cheng
- Department of Orthopaedics, People’s Hospital of Longhua, Shenzhen 518000, China;
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Xinzhi Liang
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Dong Guo
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Denghui Xie
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
- Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
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Wu XY, Fang HH, Xu YW, Zhang YL, Zhang SC, Yang WH. Bibliometric analysis of hotspots and trends of global myopia research. Int J Ophthalmol 2024; 17:940-950. [PMID: 38766336 PMCID: PMC11074204 DOI: 10.18240/ijo.2024.05.20] [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: 07/01/2023] [Accepted: 09/14/2023] [Indexed: 05/22/2024] Open
Abstract
AIM To gain insights into the global research hotspots and trends of myopia. METHODS Articles were downloaded from January 1, 2013 to December 31, 2022 from the Science Core Database website and were mainly statistically analyzed by bibliometrics software. RESULTS A total of 444 institutions in 87 countries published 4124 articles. Between 2013 and 2022, China had the highest number of publications (n=1865) and the highest H-index (61). Sun Yat-sen University had the highest number of publications (n=229) and the highest H-index (33). Ophthalmology is the main category in related journals. Citations from 2020 to 2022 highlight keywords of options and reference, child health (pediatrics), myopic traction mechanism, public health, and machine learning, which represent research frontiers. CONCLUSION Myopia has become a hot research field. China and Chinese institutions have the strongest academic influence in the field from 2013 to 2022. The main driver of myopic research is still medical or ophthalmologists. This study highlights the importance of public health in addressing the global rise in myopia, especially its impact on children's health. At present, a unified theoretical system is still needed. Accurate surgical and therapeutic solutions must be proposed for people with different characteristics to manage and intervene refractive errors. In addition, the benefits of artificial intelligence (AI) models are also reflected in disease monitoring and prediction.
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Affiliation(s)
- Xing-Yang Wu
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
| | - Hui-Hui Fang
- School of Future Technology, South China University of Technology, Guangzhou 510641, Guangdong Province, China
| | - Yan-Wu Xu
- School of Future Technology, South China University of Technology, Guangzhou 510641, Guangdong Province, China
| | - Yan-Ling Zhang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
| | - Shao-Chong Zhang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
| | - Wei-Hua Yang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
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Haykal D, Garibyan L, Flament F, Cartier H. Hybrid cosmetic dermatology: AI generated horizon. Skin Res Technol 2024; 30:e13721. [PMID: 38696225 PMCID: PMC11064925 DOI: 10.1111/srt.13721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 04/15/2024] [Indexed: 05/04/2024]
Affiliation(s)
| | - Lilit Garibyan
- Wellman Center for PhotomedicineMassachusetts General HospitalBostonMassachusettsUSA
- Department of DermatologyHarvard Medical SchoolBostonMassachusettsUSA
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7
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Delsoz M, Madadi Y, Raja H, Munir WM, Tamm B, Mehravaran S, Soleimani M, Djalilian A, Yousefi S. Performance of ChatGPT in Diagnosis of Corneal Eye Diseases. Cornea 2024; 43:664-670. [PMID: 38391243 DOI: 10.1097/ico.0000000000003492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 12/28/2023] [Indexed: 02/24/2024]
Abstract
PURPOSE The aim of this study was to assess the capabilities of ChatGPT-4.0 and ChatGPT-3.5 for diagnosing corneal eye diseases based on case reports and compare with human experts. METHODS We randomly selected 20 cases of corneal diseases including corneal infections, dystrophies, and degenerations from a publicly accessible online database from the University of Iowa. We then input the text of each case description into ChatGPT-4.0 and ChatGPT-3.5 and asked for a provisional diagnosis. We finally evaluated the responses based on the correct diagnoses, compared them with the diagnoses made by 3 corneal specialists (human experts), and evaluated interobserver agreements. RESULTS The provisional diagnosis accuracy based on ChatGPT-4.0 was 85% (17 correct of 20 cases), whereas the accuracy of ChatGPT-3.5 was 60% (12 correct cases of 20). The accuracy of 3 corneal specialists compared with ChatGPT-4.0 and ChatGPT-3.5 was 100% (20 cases, P = 0.23, P = 0.0033), 90% (18 cases, P = 0.99, P = 0.6), and 90% (18 cases, P = 0.99, P = 0.6), respectively. The interobserver agreement between ChatGPT-4.0 and ChatGPT-3.5 was 65% (13 cases), whereas the interobserver agreement between ChatGPT-4.0 and 3 corneal specialists was 85% (17 cases), 80% (16 cases), and 75% (15 cases), respectively. However, the interobserver agreement between ChatGPT-3.5 and each of 3 corneal specialists was 60% (12 cases). CONCLUSIONS The accuracy of ChatGPT-4.0 in diagnosing patients with various corneal conditions was markedly improved than ChatGPT-3.5 and promising for potential clinical integration. A balanced approach that combines artificial intelligence-generated insights with clinical expertise holds a key role for unveiling its full potential in eye care.
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Affiliation(s)
- Mohammad Delsoz
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, TN
| | - Yeganeh Madadi
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, TN
| | - Hina Raja
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, TN
| | - Wuqaas M Munir
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD
| | - Brendan Tamm
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD
| | - Shiva Mehravaran
- Department of Biology, School of Computer, Mathematical, and Natural Sciences, Morgan State University, Baltimore, MD
| | - Mohammad Soleimani
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran ; and
| | - Ali Djalilian
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL
| | - Siamak Yousefi
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, TN
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN
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Lopez-Lopez V, Morise Z, Albaladejo-González M, Gavara CG, Goh BKP, Koh YX, Paul SJ, Hilal MA, Mishima K, Krürger JAP, Herman P, Cerezuela A, Brusadin R, Kaizu T, Lujan J, Rotellar F, Monden K, Dalmau M, Gotohda N, Kudo M, Kanazawa A, Kato Y, Nitta H, Amano S, Valle RD, Giuffrida M, Ueno M, Otsuka Y, Asano D, Tanabe M, Itano O, Minagawa T, Eshmuminov D, Herrero I, Ramírez P, Ruipérez-Valiente JA, Robles-Campos R, Wakabayashi G. Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study. Surg Endosc 2024; 38:2411-2422. [PMID: 38315197 PMCID: PMC11078826 DOI: 10.1007/s00464-024-10681-6] [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: 08/07/2023] [Accepted: 01/02/2024] [Indexed: 02/07/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is becoming more useful as a decision-making and outcomes predictor tool. We have developed AI models to predict surgical complexity and the postoperative course in laparoscopic liver surgery for segments 7 and 8. METHODS We included patients with lesions located in segments 7 and 8 operated by minimally invasive liver surgery from an international multi-institutional database. We have employed AI models to predict surgical complexity and postoperative outcomes. Furthermore, we have applied SHapley Additive exPlanations (SHAP) to make the AI models interpretable. Finally, we analyzed the surgeries not converted to open versus those converted to open. RESULTS Overall, 585 patients and 22 variables were included. Multi-layer Perceptron (MLP) showed the highest performance for predicting surgery complexity and Random Forest (RF) for predicting postoperative outcomes. SHAP detected that MLP and RF gave the highest relevance to the variables "resection type" and "largest tumor size" for predicting surgery complexity and postoperative outcomes. In addition, we explored between surgeries converted to open and non-converted, finding statistically significant differences in the variables "tumor location," "blood loss," "complications," and "operation time." CONCLUSION We have observed how the application of SHAP allows us to understand the predictions of AI models in surgical complexity and the postoperative outcomes of laparoscopic liver surgery in segments 7 and 8.
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Affiliation(s)
- Victor Lopez-Lopez
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Zeniche Morise
- Department of Surgery, Fujita Health University School of Medicine Okazaki Medical Center, Okazaki, Aichi, Japan
| | | | - Concepción Gomez Gavara
- Department of HPB Surgery and Transplants, Vall d'Hebron University Hospital, Barcelona Autonomic University, Barcelona, Spain
| | - Brian K P Goh
- Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore, Singapore
- Surgery Academic Clinical Programme, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Ye Xin Koh
- Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore, Singapore
- Surgery Academic Clinical Programme, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Sijberden Jasper Paul
- Department of Surgery, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Mohammed Abu Hilal
- Department of Surgery, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
- Department of Surgery, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Kohei Mishima
- Department of Surgery, Ageo Central General Hospital, Ageo, Japan
| | - Jaime Arthur Pirola Krürger
- Serviço de Cirurgia do Fígado, Divisão de Cirurgia do Aparelho Digestivo, Departamento de Gastroenterologia, Faculdade de Medicina, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Paulo Herman
- Serviço de Cirurgia do Fígado, Divisão de Cirurgia do Aparelho Digestivo, Departamento de Gastroenterologia, Faculdade de Medicina, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Alvaro Cerezuela
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Roberto Brusadin
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Takashi Kaizu
- Department of General, Pediatric and Hepatobiliary-Pancreatic Surgery, Kitasato University School of Medicine, Sagamihara, Japan
| | - Juan Lujan
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
- Department of General Surgery, School of Medicine, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Fernando Rotellar
- Department of General Surgery, School of Medicine, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Kazuteru Monden
- Department of Surgery, Fukuyama City Hospital, Hiroshima, Japan
| | - Mar Dalmau
- Department of HPB Surgery and Transplants, Vall d'Hebron University Hospital, Barcelona Autonomic University, Barcelona, Spain
| | - Naoto Gotohda
- Department of Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Masashi Kudo
- Department of Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Akishige Kanazawa
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka City General Hospital, Osaka, Japan
| | - Yutaro Kato
- Department of Surgery, Fujita Health University, Toyoake, Japan
| | - Hiroyuki Nitta
- Department of Surgery, Iwate Medical University, Iwate, Japan
| | - Satoshi Amano
- Department of Surgery, Iwate Medical University, Iwate, Japan
| | | | - Mario Giuffrida
- General Surgery Unit, Parma University Hospital, Parma, Italy
| | - Masaki Ueno
- Second Department of Surgery, Wakayama Medical University, 811-1 Kimiidera, Wakayama City, Wakayama, Japan
| | | | - Daisuke Asano
- Department of Hepatobiliary and Pancreatic Surgery, Graduate School of Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Minoru Tanabe
- Department of Hepatobiliary and Pancreatic Surgery, Graduate School of Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Osamu Itano
- Department of Hepato-Biliary-Pancreatic and Gastrointestinal Surgery, School of Medicine, International University of Health and Welfare, Chiba, Japan
| | - Takuya Minagawa
- Department of Hepato-Biliary-Pancreatic and Gastrointestinal Surgery, School of Medicine, International University of Health and Welfare, Chiba, Japan
| | - Dilmurodjon Eshmuminov
- Department of Surgery and Transplantation, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Irene Herrero
- Department of Surgery, Getafe University Hospital, Madrid, Spain
| | - Pablo Ramírez
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | | | - Ricardo Robles-Campos
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Go Wakabayashi
- Department of Surgery, Ageo Central General Hospital, Ageo, Japan
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Culp ML, Mahmoud S, Liu D, Haworth IS. An Artificial Intelligence-Supported Medicinal Chemistry Project: An Example for Incorporating Artificial Intelligence Within the Pharmacy Curriculum. AMERICAN JOURNAL OF PHARMACEUTICAL EDUCATION 2024; 88:100696. [PMID: 38574998 DOI: 10.1016/j.ajpe.2024.100696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 03/12/2024] [Accepted: 03/29/2024] [Indexed: 04/06/2024]
Abstract
OBJECTIVE This study aims to integrate and use AI to teach core concepts in a medicinal chemistry course and to increase the familiarity of pharmacy students with AI in pharmacy practice and drug development. Artificial intelligence (AI) is a multidisciplinary science that aims to build software tools that mimic human intelligence. AI is revolutionizing pharmaceutical research and patient care. Hence, it is important to include AI in pharmacy education to prepare a competent workforce of pharmacists with skills in this area. METHODS AI principles were introduced in a required medicinal chemistry course for first-year pharmacy students. An AI software, KNIME, was used to examine structure-activity relationships for 5 drugs. Students completed a data sheet that required comprehension of molecular structures and drug-protein interactions. These data were then used to make predictions for molecules with novel substituents using AI. The familiarity of students with AI was surveyed before and after this activity. RESULTS There was an increase in the number of students indicating familiarity with use of AI in pharmacy (before vs after: 25.3% vs 74.5%). The introduction of AI stimulated interest in the course content (> 60% of students indicated increased interest in medicinal chemistry) without compromising the learning outcomes. Almost 70% of students agreed that more AI should be taught in the PharmD curriculum. CONCLUSION This is a successful and transferable example of integrating AI in pharmacy education without changing the main learning objectives of a course. This approach is likely to stimulate student interest in AI applications in pharmacy.
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Affiliation(s)
- Megan L Culp
- University of Southern California, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Department of Pharmacology & Pharmaceutical Sciences, Los Angeles, CA, USA
| | - Sara Mahmoud
- University of the Pacific Thomas J. Long School of Pharmacy, Department of Pharmacy Practice, Stockton, CA, USA.
| | - Daniel Liu
- University of Southern California, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Department of Pharmacology & Pharmaceutical Sciences, Los Angeles, CA, USA
| | - Ian S Haworth
- University of Southern California, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Department of Pharmacology & Pharmaceutical Sciences, Los Angeles, CA, USA
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10
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Lu T, Lu M, Liu H, Song D, Wang Z, Guo Y, Fang Y, Chen Q, Li T. Establishment of a prognostic model for gastric cancer patients who underwent radical gastrectomy using machine learning: a two-center study. Front Oncol 2024; 13:1282042. [PMID: 38665864 PMCID: PMC11043579 DOI: 10.3389/fonc.2023.1282042] [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: 08/23/2023] [Accepted: 12/21/2023] [Indexed: 04/28/2024] Open
Abstract
Objective Gastric cancer is a prevalent gastrointestinal malignancy worldwide. In this study, a prognostic model was developed for gastric cancer patients who underwent radical gastrectomy using machine learning, employing advanced computational techniques to investigate postoperative mortality risk factors in such patients. Methods Data of 295 patients with gastric cancer who underwent radical gastrectomy at the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) between March 2016 and November 2019 were retrospectively analyzed as the training group. Additionally, 109 patients who underwent radical gastrectomy at the Department of General Surgery Affiliated to Jining First People's Hospital (Jining, China) were included for external validation. Four machine learning models, including logistic regression (LR), decision tree (DT), random forest (RF), and gradient boosting machine (GBM), were utilized. Model performance was assessed by comparing the area under the curve (AUC) for each model. An LR-based nomogram model was constructed to assess patients' clinical prognosis. Results Lasso regression identified eight associated factors: age, sex, maximum tumor diameter, nerve or vascular invasion, TNM stage, gastrectomy type, lymphocyte count, and carcinoembryonic antigen (CEA) level. The performance of these models was evaluated using the AUC. In the training group, the AUC values were 0.795, 0.759, 0.873, and 0.853 for LR, DT, RF, and GBM, respectively. In the validation group, the AUC values were 0.734, 0.708, 0.746, and 0.707 for LR, DT, RF, and GBM, respectively. The nomogram model, constructed based on LR, demonstrated excellent clinical prognostic evaluation capabilities. Conclusion Machine learning algorithms are robust performance assessment tools for evaluating the prognosis of gastric cancer patients who have undergone radical gastrectomy. The LR-based nomogram model can aid clinicians in making more reliable clinical decisions.
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Affiliation(s)
- Tong Lu
- Department of Emergency Medicine, Jining No.1 People’s Hospital, Jining, China
| | - Miao Lu
- Wuxi Mental Health Center, Wuxi, China
| | - Haonan Liu
- Department of Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Daqing Song
- Department of Emergency Medicine, Jining No.1 People’s Hospital, Jining, China
| | - Zhengzheng Wang
- Department of Gastroenterology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Yahui Guo
- Department of Gastroenterology, Xuzhou First People’s Hospital, Xuzhou, China
| | - Yu Fang
- Jiangsu Normal University, Xuzhou, China
| | - Qi Chen
- Department of Gastroenterology, Jining First People’s Hospital, Jining, China
| | - Tao Li
- Department of Emergency Medicine, Jining No.1 People’s Hospital, Jining, China
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Stueckle CA, Haage P. The radiologist as a physician - artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians - a narrative review. ROFO-FORTSCHR RONTG 2024. [PMID: 38569517 DOI: 10.1055/a-2271-0799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
BACKGROUND Large volumes of data increasing over time lead to a shortage of radiologists' time. The use of systems based on artificial intelligence (AI) offers opportunities to relieve the burden on radiologists. The AI systems are usually optimized for a radiological area. Radiologists must understand the basic features of its technical function in order to be able to assess the weaknesses and possible errors of the system and use the strengths of the system. This "explainability" creates trust in an AI system and shows its limits. METHOD Based on an expanded Medline search for the key words "radiology, artificial intelligence, referring physician interaction, patient interaction, job satisfaction, communication of findings, expectations", subjective additional relevant articles were considered for this narrative review. RESULTS The use of AI is well advanced, especially in radiology. The programmer should provide the radiologist with clear explanations as to how the system works. All systems on the market have strengths and weaknesses. Some of the optimizations are unintentionally specific, as they are often adapted too precisely to a certain environment that often does not exist in practice - this is known as "overfitting". It should also be noted that there are specific weak points in the systems, so-called "adversarial examples", which lead to fatal misdiagnoses by the AI even though these cannot be visually distinguished from an unremarkable finding by the radiologist. The user must know which diseases the system is trained for, which organ systems are recognized and taken into account by the AI, and, accordingly, which are not properly assessed. This means that the user can and must critically review the results and adjust the findings if necessary. Correctly applied AI can result in a time savings for the radiologist. If he knows how the system works, he only has to spend a short amount of time checking the results. The time saved can be used for communication with patients and referring physicians and thus contribute to higher job satisfaction. CONCLUSION Radiology is a constantly evolving specialty with enormous responsibility, as radiologists often make the diagnosis to be treated. AI-supported systems should be used consistently to provide relief and support. Radiologists need to know the strengths, weaknesses, and areas of application of these AI systems in order to save time. The time gained can be used for communication with patients and referring physicians. KEY POINTS · Explainable AI systems help to improve workflow and to save time.. · The physician must critically review AI results, under consideration of the limitations of the AI.. · The AI system will only provide useful results if it has been adapted to the data type and data origin.. · The communicating radiologist interested in the patient is important for the visibility of the discipline.. CITATION FORMAT · Stueckle CA, Haage P. The radiologist as a physician - artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians - a narrative review. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2271-0799.
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Affiliation(s)
| | - Patrick Haage
- Diagnostic and Interventional Radiology, HELIOS Universitätsklinikum Wuppertal, Germany
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12
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Gao L, Xing B. Bone cement reinforcement improves the therapeutic effects of screws in elderly patients with pelvic fragility factures. J Orthop Surg Res 2024; 19:191. [PMID: 38500199 PMCID: PMC10949620 DOI: 10.1186/s13018-024-04666-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 03/06/2024] [Indexed: 03/20/2024] Open
Abstract
BACKGROUND Pelvic fragility fractures in elderly individuals present significant challenges in orthopedic and geriatric medicine due to reduced bone density and increased frailty associated with aging. METHODS This study involved 150 elderly patients with pelvic fragility fractures. The patients were divided into two groups, the observation group (Observation) and the control group (Control), using a random number table. Artificial intelligence, specifically the Tianji Orthopedic Robot, was employed for surgical assistance. The observation group received bone cement reinforcement along with screw fixation using the robotic system, while the control group received conventional screw fixation alone. Follow-up data were collected for one-year post-treatment. RESULTS The observation group exhibited significantly lower clinical healing time of fractures and reduced bed rest time compared to the control group. Additionally, the observation group experienced less postoperative pain at 1 and 3 months, indicating the benefits of bone cement reinforcement. Moreover, patients in the observation group demonstrated significantly better functional recovery at 1-, 3-, and 6-months post-surgery compared to the control group. CONCLUSION The combination of bone cement reinforcement and robotic technology resulted in accelerated fracture healing, reduced bed rest time, and improved postoperative pain relief and functional recovery.
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Affiliation(s)
- Lecai Gao
- Department of Orthopaedic Surgery, Hebei Cangzhou Hospital of Integrated Traditional Chinese Medicine and Western Medicine, Cangzhou, Hebei, 061000, China
| | - Baorui Xing
- Department of Orthopaedic Surgery, Hebei Cangzhou Hospital of Integrated Traditional Chinese Medicine and Western Medicine, Cangzhou, Hebei, 061000, China.
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Okita J, Nakata T, Uchida H, Kudo A, Fukuda A, Ueno T, Tanigawa M, Sato N, Shibata H. Development and validation of a machine learning model to predict time to renal replacement therapy in patients with chronic kidney disease. BMC Nephrol 2024; 25:101. [PMID: 38493099 PMCID: PMC10943785 DOI: 10.1186/s12882-024-03527-9] [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: 12/13/2023] [Accepted: 02/28/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Predicting time to renal replacement therapy (RRT) is important in patients at high risk for end-stage kidney disease. We developed and validated machine learning models for predicting the time to RRT and compared its accuracy with conventional prediction methods that uses the rate of estimated glomerular filtration rate (eGFR) decline. METHODS Data of adult chronic kidney disease (CKD) patients who underwent hemodialysis at Oita University Hospital from April 2016 to March 2021 were extracted from electronic medical records (N = 135). A new machine learning predictor was compared with the established prediction method that uses the eGFR decline rate and the accuracy of the prediction models was determined using the coefficient of determination (R2). The data were preprocessed and split into training and validation datasets. We created multiple machine learning models using the training data and evaluated their accuracy using validation data. Furthermore, we predicted the time to RRT using a conventional prediction method that uses the eGFR decline rate for patients who had measured eGFR three or more times in two years and evaluated its accuracy. RESULTS The least absolute shrinkage and selection operator regression model exhibited moderate accuracy with an R2 of 0.60. By contrast, the conventional prediction method was found to be extremely low with an R2 of -17.1. CONCLUSIONS The significance of this study is that it shows that machine learning can predict time to RRT moderately well with continuous values from data at a single time point. This approach outperforms the conventional prediction method that uses eGFR time series data and presents new avenues for CKD treatment.
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Affiliation(s)
- Jun Okita
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan
| | - Takeshi Nakata
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan.
| | - Hiroki Uchida
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan
| | - Akiko Kudo
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan
| | - Akihiro Fukuda
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan
| | - Tamio Ueno
- Department of Medical Technology and Sciences, School of Health Sciences at Fukuoka, International University of Health and Welfare, Okawa, Japan
| | - Masato Tanigawa
- Department of Biophysics, Faculty of Medicine, Oita University, Oita, Japan
| | - Noboru Sato
- Department of Healthcare AI Data Science, Faculty of Medicine, Oita University, Oita, Japan
| | - Hirotaka Shibata
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan
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Abbas A, Rehman MS, Rehman SS. Comparing the Performance of Popular Large Language Models on the National Board of Medical Examiners Sample Questions. Cureus 2024; 16:e55991. [PMID: 38606229 PMCID: PMC11007479 DOI: 10.7759/cureus.55991] [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: 03/11/2024] [Indexed: 04/13/2024] Open
Abstract
INTRODUCTION Large language models (LLMs) have transformed various domains in medicine, aiding in complex tasks and clinical decision-making, with OpenAI's GPT-4, GPT-3.5, Google's Bard, and Anthropic's Claude among the most widely used. While GPT-4 has demonstrated superior performance in some studies, comprehensive comparisons among these models remain limited. Recognizing the significance of the National Board of Medical Examiners (NBME) exams in assessing the clinical knowledge of medical students, this study aims to compare the accuracy of popular LLMs on NBME clinical subject exam sample questions. METHODS The questions used in this study were multiple-choice questions obtained from the official NBME website and are publicly available. Questions from the NBME subject exams in medicine, pediatrics, obstetrics and gynecology, clinical neurology, ambulatory care, family medicine, psychiatry, and surgery were used to query each LLM. The responses from GPT-4, GPT-3.5, Claude, and Bard were collected in October 2023. The response by each LLM was compared to the answer provided by the NBME and checked for accuracy. Statistical analysis was performed using one-way analysis of variance (ANOVA). RESULTS A total of 163 questions were queried by each LLM. GPT-4 scored 163/163 (100%), GPT-3.5 scored 134/163 (82.2%), Bard scored 123/163 (75.5%), and Claude scored 138/163 (84.7%). The total performance of GPT-4 was statistically superior to that of GPT-3.5, Claude, and Bard by 17.8%, 15.3%, and 24.5%, respectively. The total performance of GPT-3.5, Claude, and Bard was not significantly different. GPT-4 significantly outperformed Bard in specific subjects, including medicine, pediatrics, family medicine, and ambulatory care, and GPT-3.5 in ambulatory care and family medicine. Across all LLMs, the surgery exam had the highest average score (18.25/20), while the family medicine exam had the lowest average score (3.75/5). Conclusion: GPT-4's superior performance on NBME clinical subject exam sample questions underscores its potential in medical education and practice. While LLMs exhibit promise, discernment in their application is crucial, considering occasional inaccuracies. As technological advancements continue, regular reassessments and refinements are imperative to maintain their reliability and relevance in medicine.
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Affiliation(s)
- Ali Abbas
- Medical School, University of Texas Southwestern Medical School, Dallas, USA
| | - Mahad S Rehman
- Medical School, University of Texas Southwestern Medical School, Dallas, USA
| | - Syed S Rehman
- Nephrology, Baptist Hospitals of Southeast Texas, Beaumont, USA
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Lee GU, Hong DY, Kim SY, Kim JW, Lee YH, Park SO, Lee KR. Comparison of the problem-solving performance of ChatGPT-3.5, ChatGPT-4, Bing Chat, and Bard for the Korean emergency medicine board examination question bank. Medicine (Baltimore) 2024; 103:e37325. [PMID: 38428889 PMCID: PMC10906566 DOI: 10.1097/md.0000000000037325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 01/31/2024] [Indexed: 03/03/2024] Open
Abstract
Large language models (LLMs) have been deployed in diverse fields, and the potential for their application in medicine has been explored through numerous studies. This study aimed to evaluate and compare the performance of ChatGPT-3.5, ChatGPT-4, Bing Chat, and Bard for the Emergency Medicine Board Examination question bank in the Korean language. Of the 2353 questions in the question bank, 150 questions were randomly selected, and 27 containing figures were excluded. Questions that required abilities such as analysis, creative thinking, evaluation, and synthesis were classified as higher-order questions, and those that required only recall, memory, and factual information in response were classified as lower-order questions. The answers and explanations obtained by inputting the 123 questions into the LLMs were analyzed and compared. ChatGPT-4 (75.6%) and Bing Chat (70.7%) showed higher correct response rates than ChatGPT-3.5 (56.9%) and Bard (51.2%). ChatGPT-4 showed the highest correct response rate for the higher-order questions at 76.5%, and Bard and Bing Chat showed the highest rate for the lower-order questions at 71.4%. The appropriateness of the explanation for the answer was significantly higher for ChatGPT-4 and Bing Chat than for ChatGPT-3.5 and Bard (75.6%, 68.3%, 52.8%, and 50.4%, respectively). ChatGPT-4 and Bing Chat outperformed ChatGPT-3.5 and Bard in answering a random selection of Emergency Medicine Board Examination questions in the Korean language.
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Affiliation(s)
- Go Un Lee
- Department of Emergency Medicine, Konkuk University Medical Center, Seoul, Republic of Korea
| | - Dae Young Hong
- Department of Emergency Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Sin Young Kim
- Department of Emergency Medicine, Konkuk University Medical Center, Seoul, Republic of Korea
| | - Jong Won Kim
- Department of Emergency Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Young Hwan Lee
- Department of Emergency Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Sang O Park
- Department of Emergency Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Kyeong Ryong Lee
- Department of Emergency Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea
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Brandão M, Mendes F, Martins M, Cardoso P, Macedo G, Mascarenhas T, Mascarenhas Saraiva M. Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology. J Clin Med 2024; 13:1061. [PMID: 38398374 PMCID: PMC10889757 DOI: 10.3390/jcm13041061] [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: 12/31/2023] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.
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Affiliation(s)
- Marta Brandão
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Guilherme Macedo
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Teresa Mascarenhas
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Obstetrics and Gynecology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Mascarenhas Saraiva
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
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Aedo-Martín D. [Translated article] Artificial intelligence: Future and challenges in modern medicine. Rev Esp Cir Ortop Traumatol (Engl Ed) 2024:S1888-4415(24)00047-X. [PMID: 38325569 DOI: 10.1016/j.recot.2024.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 03/30/2023] [Indexed: 02/09/2024] Open
Affiliation(s)
- D Aedo-Martín
- Servicio de Cirugía Ortopédica y Traumatología, Hospital Universitario del Henares, Madrid, Spain; Unidad de Medicina Deportiva y Traumatología, Hospital Vithas Internacional, Madrid, Spain; Unidad de Mano, Invictum Medical Sports Center, Madrid, Spain.
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Gritti MN, AlTurki H, Farid P, Morgan CT. Progression of an Artificial Intelligence Chatbot (ChatGPT) for Pediatric Cardiology Educational Knowledge Assessment. Pediatr Cardiol 2024; 45:309-313. [PMID: 38170274 DOI: 10.1007/s00246-023-03385-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024]
Abstract
Artificial intelligence chatbots, like ChatGPT, have become powerful tools that are disrupting how humans interact with technology. The potential uses within medicine are vast. In medical education, these chatbots have shown improvements, in a short time span, in generalized medical examinations. We evaluated the overall performance and improvement between ChatGPT 3.5 and 4.0 in a test of pediatric cardiology knowledge. ChatGPT 3.5 and ChatGPT 4.0 were used to answer text-based multiple-choice questions derived from a Pediatric Cardiology Board Review textbook. Each chatbot was given an 88 question test, subcategorized into 11 topics. We excluded questions with modalities other than text (sound clips or images). Statistical analysis was done using an unpaired two-tailed t-test. Of the same 88 questions, ChatGPT 4.0 answered 66% of the questions correctly (n = 58/88) which was significantly greater (p < 0.0001) than ChatGPT 3.5, which only answered 38% (33/88). The ChatGPT 4.0 version also did better on each subspeciality topic as compared to ChatGPT 3.5. While acknowledging that ChatGPT does not yet offer subspecialty level knowledge in pediatric cardiology, the performance in pediatric cardiology educational assessments showed a considerable improvement in a short period of time between ChatGPT 3.5 and 4.0.
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Affiliation(s)
- Michael N Gritti
- Division of Cardiology, The Labatt Family Heart Centre, The Hospital for Sick Children, 555 University Ave, Toronto, ON, M5G 1X8, Canada.
- Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
| | - Hussain AlTurki
- Department of Pediatrics, University of Toronto, Toronto, ON, Canada
- Department of Pediatrics, The Hospital for Sick Children, Toronto, ON, Canada
| | - Pedrom Farid
- Division of Cardiology, The Labatt Family Heart Centre, The Hospital for Sick Children, 555 University Ave, Toronto, ON, M5G 1X8, Canada
- Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
| | - Conall T Morgan
- Division of Cardiology, The Labatt Family Heart Centre, The Hospital for Sick Children, 555 University Ave, Toronto, ON, M5G 1X8, Canada
- Department of Pediatrics, University of Toronto, Toronto, ON, Canada
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Thi YVN, Vu TD, Do VQ, Ngo AD, Show PL, Chu DT. Residual toxins on aquatic animals in the Pacific areas: Current findings and potential health effects. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 906:167390. [PMID: 37758133 DOI: 10.1016/j.scitotenv.2023.167390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/20/2023] [Accepted: 09/24/2023] [Indexed: 10/03/2023]
Abstract
The Pacific Ocean is among the five largest and deepest oceans in the world. The area of the Pacific Ocean covers about 28 % of the Earth's surface. This is the habitat of many marine species, and its diversity is recognized as a fundamental element of Pacific culture and heritage. The ecosystems of aquatic animals are highly affected by climate change and by other factors. Residual toxins on aquatic animals can be categorized into two types based on origin: toxins of marine origin and toxins associated with human activity. Residual toxins have emerged as a global concern in recent years due to their frequent presence in aquatic environments. Furthermore, residual toxins in organisms living in the marine environment in the Pacific Ocean region also seriously affect food safety, food security, and especially human health. In this review we discuss important issues about residual toxins on aquatic animals in the Pacific areas specifically about the types of toxins that exist in marine animals, their contamination pathways in the Asia, Pacific region and the potential health effects for humans, the application of information technology and artificial intelligence in residual toxins on aquatic animal.
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Affiliation(s)
- Yen Vy Nguyen Thi
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Viet Nam; Faculty of Applied Sciences, International School, Vietnam National University, Hanoi, Viet Nam
| | - Thuy-Duong Vu
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Viet Nam
| | - Van Quy Do
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Viet Nam
| | - Anh Dao Ngo
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Viet Nam
| | - Pau Loke Show
- Department of Chemical Engineering, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates
| | - Dinh Toi Chu
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Viet Nam; Faculty of Applied Sciences, International School, Vietnam National University, Hanoi, Viet Nam.
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Gaddum O, Chapiro J. An Interventional Radiologist's Primer of Critical Appraisal of Artificial Intelligence Research. J Vasc Interv Radiol 2024; 35:7-14. [PMID: 37769940 DOI: 10.1016/j.jvir.2023.09.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/17/2023] [Accepted: 09/18/2023] [Indexed: 10/03/2023] Open
Abstract
Recent advances in artificial intelligence (AI) are expected to cause a significant paradigm shift in all digital data-driven aspects of information gain, processing, and decision making in both clinical healthcare and medical research. The field of interventional radiology (IR) will be enmeshed in this innovation, yet the collective IR expertise in the field of AI remains rudimentary because of lack of training. This primer provides the clinical interventional radiologist with a simple guide for critically appraising AI research and products by identifying 12 fundamental items that should be considered: (a) need for AI technology to address the clinical problem, (b) type of applied Al algorithm, (c) data quality and degree of annotation, (d) reporting of accuracy, (e) applicability of standardized reporting, (f) reproducibility of methodology and data transparency, (g) algorithm validation, (h) interpretability, (i) concrete impact on IR, (j) pathway toward translation to clinical practice, (k) clinical benefit and cost-effectiveness, and (l) regulatory framework.
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Affiliation(s)
- Olivia Gaddum
- Division of Interventional Radiology, Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
| | - Julius Chapiro
- Division of Interventional Radiology, Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut.
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Hattab J, Porrello A, Romano A, Rosamilia A, Ghidini S, Bernabò N, Capobianco Dondona A, Corradi A, Marruchella G. Scoring Enzootic Pneumonia-like Lesions in Slaughtered Pigs: Traditional vs. Artificial-Intelligence-Based Methods. Pathogens 2023; 12:1460. [PMID: 38133343 PMCID: PMC10747234 DOI: 10.3390/pathogens12121460] [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: 11/06/2023] [Revised: 12/08/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023] Open
Abstract
Artificial-intelligence-based methods are regularly used in the biomedical sciences, mainly in the field of diagnostic imaging. Recently, convolutional neural networks have been trained to score pleurisy and pneumonia in slaughtered pigs. The aim of this study is to further evaluate the performance of a convolutional neural network when compared with the gold standard (i.e., scores provided by a skilled operator along the slaughter chain through visual inspection and palpation). In total, 441 lungs (180 healthy and 261 diseased) are included in this study. Each lung was scored according to traditional methods, which represent the gold standard (Madec's and Christensen's grids). Moreover, the same lungs were photographed and thereafter scored by a trained convolutional neural network. Overall, the results reveal that the convolutional neural network is very specific (95.55%) and quite sensitive (85.05%), showing a rather high correlation when compared with the scores provided by a skilled veterinarian (Spearman's coefficient = 0.831, p < 0.01). In summary, this study suggests that convolutional neural networks could be effectively used at slaughterhouses and stimulates further investigation in this field of research.
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Affiliation(s)
- Jasmine Hattab
- Department of Veterinary Medicine, University of Teramo, Loc. Piano d’Accio, 64100 Teramo, Italy;
| | - Angelo Porrello
- AImageLab, University of Modena and Reggio Emilia, Via Vivarelli 10/1, 41125 Modena, Italy;
| | - Anastasia Romano
- Associació Porcsa. GSP, Partida La Caparrella 97C, 25192 Lleida, Spain;
| | - Alfonso Rosamilia
- Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia-Romagna “Bruno Ubertini” (IZSLER), 25124 Brescia, Italy;
| | - Sergio Ghidini
- Department of Food and Drug, University of Parma, Via del Taglio 10, 43126 Parma, Italy;
| | - Nicola Bernabò
- Department of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Via Renato Balzarini 1, 64100 Teramo, Italy;
| | | | - Attilio Corradi
- Department of Veterinary Science, University of Parma, Via del Taglio 10, 43126 Parma, Italy;
| | - Giuseppe Marruchella
- Department of Veterinary Medicine, University of Teramo, Loc. Piano d’Accio, 64100 Teramo, Italy;
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Shi J, Bendig D, Vollmar HC, Rasche P. Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study. J Med Internet Res 2023; 25:e45815. [PMID: 38064255 PMCID: PMC10746970 DOI: 10.2196/45815] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 08/16/2023] [Accepted: 09/30/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners. OBJECTIVE In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research. METHODS Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy. RESULTS From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI's application in the realm of medicine. CONCLUSIONS The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.
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Affiliation(s)
- Jin Shi
- Institute for Entrepreneurship, University of Münster, Münster, Germany
| | - David Bendig
- Institute for Entrepreneurship, University of Münster, Münster, Germany
| | | | - Peter Rasche
- Department of Healthcare, University of Applied Science - Hochschule Niederrhein, Krefeld, Germany
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Bianco Prevot L, Fozzato S, Cannavò L, Accetta R, Amadei F, Basile M, Leigheb M, Basile G. Pathological Fracture of the Proximal Humerus Occurred on Metastases of Probable Kidney Origin in the Absence of Primary Lesions: A Case Report. Healthcare (Basel) 2023; 11:3108. [PMID: 38131998 PMCID: PMC10742696 DOI: 10.3390/healthcare11243108] [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: 10/30/2023] [Revised: 11/28/2023] [Accepted: 12/01/2023] [Indexed: 12/23/2023] Open
Abstract
Cancer of unknown primary (CUP) origin represents a diagnostic and therapeutic challenge. These tumours spread to different parts of the body even if the site of origin has not been identified. When renal metastases are observed without an obvious primary lesion, it is important to exclude the possibility of a primary kidney tumour that may be unknown or too small to be detected. The diagnosis of CUP is established after a careful clinical evaluation and diagnostic tests, including blood chemistry and laboratory tests, instrumental exams (CT, MRI, PET, bone scan), biopsy, and molecular and cytogenetic analysis. Once the diagnosis of CUP with kidney metastases is confirmed, treatment depends on the location of the metastases, the patient's health status, and available treatment options. The latter includes surgery to remove metastases, radiation therapy, or systemic treatment such as chemotherapy or immunotherapy. It is important that patients with CUP are evaluated by a multidisciplinary team of specialists, who can contribute to planning the most appropriate treatment. In this article, we report the clinical case of a patient with a pathological fracture of the proximal humerus which occurred on metastases of probable renal origin in the absence of primary lesions.
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Affiliation(s)
- Luca Bianco Prevot
- IRCCS Orthopaedic Institute Galeazzi, 20161 Milan, Italy; (L.B.P.); (G.B.)
| | - Stefania Fozzato
- IRCCS Orthopaedic Institute Galeazzi, 20161 Milan, Italy; (L.B.P.); (G.B.)
| | - Luca Cannavò
- Orthopaedic Department, Esine Hospital, 25040 Brescia, Italy
| | - Riccardo Accetta
- IRCCS Orthopaedic Institute Galeazzi, 20161 Milan, Italy; (L.B.P.); (G.B.)
| | - Federico Amadei
- Hand and Peripheral Nerve Centre, COF Lanzo Hospital, 22020 Alta Valle Intelvi, Italy
| | - Michela Basile
- Department of Biomedical and Dental Sciences and Morpho Functional Imaging, University of Messina, 98122 Messina, Italy
| | - Massimiliano Leigheb
- Orthopaedics and Traumatology Unit, “Maggiore Della Carità” Hospital, Department of Health Sciences, University of Piemonte Orientale (UPO), Via Solaroli 17, 28100 Novara, Italy
| | - Giuseppe Basile
- IRCCS Orthopaedic Institute Galeazzi, 20161 Milan, Italy; (L.B.P.); (G.B.)
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Carrasco-Ribelles LA, Llanes-Jurado J, Gallego-Moll C, Cabrera-Bean M, Monteagudo-Zaragoza M, Violán C, Zabaleta-del-Olmo E. Prediction models using artificial intelligence and longitudinal data from electronic health records: a systematic methodological review. J Am Med Inform Assoc 2023; 30:2072-2082. [PMID: 37659105 PMCID: PMC10654870 DOI: 10.1093/jamia/ocad168] [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: 05/05/2023] [Revised: 08/02/2023] [Accepted: 08/11/2023] [Indexed: 09/04/2023] Open
Abstract
OBJECTIVE To describe and appraise the use of artificial intelligence (AI) techniques that can cope with longitudinal data from electronic health records (EHRs) to predict health-related outcomes. METHODS This review included studies in any language that: EHR was at least one of the data sources, collected longitudinal data, used an AI technique capable of handling longitudinal data, and predicted any health-related outcomes. We searched MEDLINE, Scopus, Web of Science, and IEEE Xplorer from inception to January 3, 2022. Information on the dataset, prediction task, data preprocessing, feature selection, method, validation, performance, and implementation was extracted and summarized using descriptive statistics. Risk of bias and completeness of reporting were assessed using a short form of PROBAST and TRIPOD, respectively. RESULTS Eighty-one studies were included. Follow-up time and number of registers per patient varied greatly, and most predicted disease development or next event based on diagnoses and drug treatments. Architectures generally were based on Recurrent Neural Networks-like layers, though in recent years combining different layers or transformers has become more popular. About half of the included studies performed hyperparameter tuning and used attention mechanisms. Most performed a single train-test partition and could not correctly assess the variability of the model's performance. Reporting quality was poor, and a third of the studies were at high risk of bias. CONCLUSIONS AI models are increasingly using longitudinal data. However, the heterogeneity in reporting methodology and results, and the lack of public EHR datasets and code sharing, complicate the possibility of replication. REGISTRATION PROSPERO database (CRD42022331388).
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Affiliation(s)
- Lucía A Carrasco-Ribelles
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, 08007, Spain
- Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), Barcelona, 08034, Spain
- Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Mataró, 08303, Spain
| | - José Llanes-Jurado
- Instituto de Investigación e Innovación en Bioingeniería (i3B), Universitat Politècnica de València (UPV), València, 46022, Spain
| | - Carlos Gallego-Moll
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, 08007, Spain
- Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Mataró, 08303, Spain
| | - Margarita Cabrera-Bean
- Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), Barcelona, 08034, Spain
| | - Mònica Monteagudo-Zaragoza
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, 08007, Spain
| | - Concepción Violán
- Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Mataró, 08303, Spain
- Direcció d’Atenció Primària Metropolitana Nord, Institut Català de Salut, Badalona, 08915, Spain
- Fundació Institut d’Investigació en ciències de la salut Germans Trias i Pujol (IGTP), Badalona, 08916, Spain
- Fundació UAB, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193, Spain
| | - Edurne Zabaleta-del-Olmo
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, 08007, Spain
- Gerència Territorial de Barcelona, Institut Català de la Salut, Carrer de Balmes 22, Barcelona, 08007, Spain
- Nursing Department, Faculty of Nursing, Universitat de Girona, Girona, 17003, Spain
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25
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Battistelli M, Izzo A, D’Ercole M, D’Alessandris QG, Montano N. The role of artificial intelligence in the management of trigeminal neuralgia. Front Surg 2023; 10:1310414. [PMID: 38033529 PMCID: PMC10687176 DOI: 10.3389/fsurg.2023.1310414] [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: 10/09/2023] [Accepted: 11/01/2023] [Indexed: 12/02/2023] Open
Abstract
Trigeminal neuralgia (TN) is the most frequent facial pain. It is difficult to treat pharmacologically and a significant amount of patients can become drug-resistant requiring surgical intervention. From an etiologically point of view TN can be distinguished in a classic form, usually due to a neurovascular conflict, a secondary form (for example related to multiple sclerosis or a cerebello-pontine angle tumor) and an idiopathic form in which no anatomical cause is identifiable. Despite numerous efforts to treat TN, many patients experience recurrence after multiple operations. This fact reflects our incomplete understanding of TN pathogenesis. Artificial intelligence (AI) uses computer technology to develop systems for extension of human intelligence. In the last few years, it has been a widespread of AI in different areas of medicine to implement diagnostic accuracy, treatment selection and even drug production. The aim of this mini-review is to provide an up to date of the state-of-art of AI applications in TN diagnosis and management.
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Affiliation(s)
| | | | | | | | - Nicola Montano
- Department of Neuroscience, Neurosurgery Section, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
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26
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Dong Z, Shen C, Tang J, Wang B, Liao H. Accuracy of Thoracic Ultrasonography for the Diagnosis of Pediatric Pneumonia: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2023; 13:3457. [PMID: 37998593 PMCID: PMC10670251 DOI: 10.3390/diagnostics13223457] [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: 10/05/2023] [Revised: 11/05/2023] [Accepted: 11/13/2023] [Indexed: 11/25/2023] Open
Abstract
As an emerging imaging technique, thoracic ultrasonography (TUS) is increasingly utilized in the diagnosis of lung diseases in children and newborns, especially in emergency and critical settings. This systematic review aimed to estimate the diagnostic accuracy of TUS in childhood pneumonia. We searched Embase, PubMed, and Web of Science for studies until July 2023 using both TUS and chest radiography (CR) for the diagnosis of pediatric pneumonia. Two researchers independently screened the literature based on the inclusion and exclusion criteria, collected the results, and assessed the risk of bias using the Diagnostic Accuracy Study Quality Assessment (QUADAS) tool. A total of 26 articles met our inclusion criteria and were included in the final analysis, including 22 prospective studies and four retrospective studies. The StataMP 14.0 software was used for the analysis of the study. The overall pooled sensitivity was 0.95 [95% confidence intervals (CI), 0.92-0.97] and the specificity was 0.94 [95% CI, 0.88-0.97], depicting a good diagnostic accuracy. Our results indicated that TUS was an effective imaging modality for detecting pediatric pneumonia. It is a potential alternative to CXR and a follow-up for pediatric pneumonia due to its simplicity, versatility, low cost, and lack of radiation hazards.
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Affiliation(s)
- Zhenghao Dong
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China; (Z.D.); (C.S.); (B.W.)
| | - Cheng Shen
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China; (Z.D.); (C.S.); (B.W.)
| | - Jinhai Tang
- Department of Radiation Oncology, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China
| | - Beinuo Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China; (Z.D.); (C.S.); (B.W.)
| | - Hu Liao
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China; (Z.D.); (C.S.); (B.W.)
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27
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Carnevale A, Mannocchi I, Schena E, Carli M, Sassi MSH, Marino M, Longo UG. Performance Evaluation of an Immersive Virtual Reality Application for Rehabilitation after Arthroscopic Rotator Cuff Repair. Bioengineering (Basel) 2023; 10:1305. [PMID: 38002429 PMCID: PMC10668954 DOI: 10.3390/bioengineering10111305] [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: 09/18/2023] [Revised: 10/11/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
Few studies have evaluated the effectiveness of shoulder rehabilitation in virtual environments. The objective of this study was to investigate the performance of a custom virtual reality application (VR app) with a stereophotogrammetric system considered the gold standard. A custom VR app was designed considering the recommended rehabilitation exercises following arthroscopic rotator cuff repair. Following the setting of the play space, the user's arm length, and height, five healthy volunteers performed four levels of rehabilitative exercises. Results for the first and second rounds of flexion and abduction displayed low total mean absolute error values and low numbers of unmet conditions. In internal and external rotation, the number of times conditions were not met was slightly higher; this was attributed to a lack of isolated shoulder movement. Data is promising, and volunteers were able to reach goal conditions more often than not. Despite positive results, more literature comparing VR applications with gold-standard clinical parameters is necessary. Nevertheless, results contribute to a body of literature that continues to encourage the application of VR to shoulder rehabilitation programs.
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Affiliation(s)
- Arianna Carnevale
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Álvaro del Portillo, 200, 00128 Roma, Italy; (A.C.); (M.M.)
| | - Ilaria Mannocchi
- Department of Industrial, Electronic and Mechanical Engineering, University of Roma Tre, Via Vito Volterra, 62, 00146 Roma, Italy; (I.M.); (M.C.); (M.S.H.S.)
| | - Emiliano Schena
- Unit of Measurement and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Roma, Italy;
| | - Marco Carli
- Department of Industrial, Electronic and Mechanical Engineering, University of Roma Tre, Via Vito Volterra, 62, 00146 Roma, Italy; (I.M.); (M.C.); (M.S.H.S.)
| | - Mohamed Saifeddine Hadj Sassi
- Department of Industrial, Electronic and Mechanical Engineering, University of Roma Tre, Via Vito Volterra, 62, 00146 Roma, Italy; (I.M.); (M.C.); (M.S.H.S.)
| | - Martina Marino
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Álvaro del Portillo, 200, 00128 Roma, Italy; (A.C.); (M.M.)
| | - Umile Giuseppe Longo
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Álvaro del Portillo, 200, 00128 Roma, Italy; (A.C.); (M.M.)
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Roma, Italy
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28
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Palavicini G. Intelligent Health: Progress and Benefit of Artificial Intelligence in Sensing-Based Monitoring and Disease Diagnosis. SENSORS (BASEL, SWITZERLAND) 2023; 23:9053. [PMID: 38005442 PMCID: PMC10675666 DOI: 10.3390/s23229053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 10/20/2023] [Accepted: 10/26/2023] [Indexed: 11/26/2023]
Abstract
Technology has progressed and allows people to go further in multiple fields related to social issues. Medicine cannot be the exception, especially nowadays, when the COVID-19 pandemic has accelerated the use of technology to continue living meaningfully, but mainly in giving consideration to people who remain confined at home with health issues. Our research question is: how can artificial intelligence (AI) translated into technological devices be used to identify health issues, improve people's health, or prevent severe patient damage? Our work hypothesis is that technology has improved so much during the last decades that Medicine cannot remain apart from this progress. It must integrate technology into treatments so proper communication between intelligent devices and human bodies could better prevent health issues and even correct those already manifested. Consequently, we will answer: what has been the progress of Medicine using intelligent sensor-based devices? Which of those devices are the most used in medical practices? Which is the most benefited population, and what do physicians currently use this technology for? Could sensor-based monitoring and disease diagnosis represent a difference in how the medical praxis takes place nowadays, favouring prevention as opposed to healing?
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Affiliation(s)
- Gabriela Palavicini
- Department of Media and Digital Culture, Instituto Tecnológico y de Estudios Superiores de Monterrey, Mexico City 01389, Mexico
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29
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Duschner N, Baguer DO, Schmidt M, Griewank KG, Hadaschik E, Hetzer S, Wiepjes B, Le'Clerc Arrastia J, Jansen P, Maass P, Schaller J. Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma. J Dtsch Dermatol Ges 2023; 21:1329-1337. [PMID: 37814387 DOI: 10.1111/ddg.15180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 06/15/2023] [Indexed: 10/11/2023]
Abstract
BACKGROUND Institutes of dermatopathology are faced with considerable challenges including a continuously rising numbers of submitted specimens and a shortage of specialized health care practitioners. Basal cell carcinoma (BCC) is one of the most common tumors in the fair-skinned western population and represents a major part of samples submitted for histological evaluation. Digitalizing glass slides has enabled the application of artificial intelligence (AI)-based procedures. To date, these methods have found only limited application in routine diagnostics. The aim of this study was to establish an AI-based model for automated BCC detection. PATIENTS AND METHODS In three dermatopathological centers, daily routine practice BCC cases were digitalized. The diagnosis was made both conventionally by analog microscope and digitally through an AI-supported algorithm based on a U-Net architecture neural network. RESULTS In routine practice, the model achieved a sensitivity of 98.23% (center 1) and a specificity of 98.51%. The model generalized successfully without additional training to samples from the other centers, achieving similarly high accuracies in BCC detection (sensitivities of 97.67% and 98.57% and specificities of 96.77% and 98.73% in centers 2 and 3, respectively). In addition, automated AI-based basal cell carcinoma subtyping and tumor thickness measurement were established. CONCLUSIONS AI-based methods can detect BCC with high accuracy in a routine clinical setting and significantly support dermatopathological work.
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Affiliation(s)
| | - Daniel Otero Baguer
- Center for Technical Mathematics (ZeTeM), University of Bremen, Bremen, Germany
| | - Maximilian Schmidt
- Center for Technical Mathematics (ZeTeM), University of Bremen, Bremen, Germany
| | - Klaus Georg Griewank
- Dermatopathologie bei Mainz, Nieder-Olm, Germany
- Department of Dermatology, University Hospital Essen, Essen, Germany
| | - Eva Hadaschik
- MVZ Dermatopathology Duisburg Essen, Essen, Germany
- Department of Dermatology, University Hospital Essen, Essen, Germany
| | - Sonja Hetzer
- MVZ Dermatopathology Duisburg Essen, Essen, Germany
| | | | | | - Philipp Jansen
- Department of Dermatology and Allergology, University Hospital Bonn, Bonn, Germany
| | - Peter Maass
- Center for Technical Mathematics (ZeTeM), University of Bremen, Bremen, Germany
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30
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Schlicker N, Langer M, Hirsch MC. [How trustworthy is artificial intelligence? : A model for the conflict between objectivity and subjectivity]. INNERE MEDIZIN (HEIDELBERG, GERMANY) 2023; 64:1051-1057. [PMID: 37737496 DOI: 10.1007/s00108-023-01602-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/18/2023] [Indexed: 09/23/2023]
Abstract
For the integration of artificial intelligence (AI) systems into medical processes it is decisive to address both the trustworthiness of these systems and the trust that physicians and patients have in those systems. Too much trust can result in physicians uncritically relying on this technology, while too little trust may result in physicians not taking advantage of the full potential of AI-based technology in making decisions. To strike a balance between these extremes it is crucial to correctly assess the trustworthiness of a system. Only in this way is it possible to decide whether or the system can be trusted or not. This article describes these relationships for the medical context. We show why trustworthiness and trust are important in the use of AI-based systems and how individuals can come to an accurate assessment of the trustworthiness of AI-based systems.
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Affiliation(s)
- Nadine Schlicker
- Institut für Künstliche Intelligenz in der Medizin, Philipps-Universität Marburg, Baldingerstr., 35043, Marburg, Deutschland.
| | - Markus Langer
- Fachbereich Psychologie, Arbeitseinheit Digitalisierung in psychologischen Handlungsfeldern, Philipps-Universität Marburg, Marburg, Deutschland
| | - Martin C Hirsch
- Institut für Künstliche Intelligenz in der Medizin, Philipps-Universität Marburg, Baldingerstr., 35043, Marburg, Deutschland
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31
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Davuluru SS, Jess AT, Kim JSB, Yoo K, Nguyen V, Xu BY. Identifying, Understanding, and Addressing Disparities in Glaucoma Care in the United States. Transl Vis Sci Technol 2023; 12:18. [PMID: 37889504 PMCID: PMC10617640 DOI: 10.1167/tvst.12.10.18] [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: 07/16/2023] [Accepted: 10/09/2023] [Indexed: 10/28/2023] Open
Abstract
Glaucoma is the leading cause of irreversible blindness worldwide, currently affecting around 80 million people. Glaucoma prevalence is rapidly rising in the United States due to an aging population. Despite recent advances in the diagnosis and treatment of glaucoma, significant disparities persist in disease detection, management, and outcomes among the diverse patient populations of the United States. Research on disparities is critical to identifying, understanding, and addressing societal and healthcare inequalities. Disparities research is especially important and impactful in the context of irreversible diseases such as glaucoma, where earlier detection and intervention are the primary approach to improving patient outcomes. In this article, we first review recent studies identifying disparities in glaucoma care that affect patient populations based on race, age, and gender. We then review studies elucidating and furthering our understanding of modifiable factors that contribute to these inequities, including socioeconomic status (particularly age and education), insurance product, and geographic region. Finally, we present work proposing potential strategies addressing disparities in glaucoma care, including teleophthalmology and artificial intelligence. We also discuss the presence of non-modifiable factors that contribute to differences in glaucoma burden and can confound the detection of glaucoma disparities. Translational Relevance By recognizing underlying causes and proposing potential solutions, healthcare providers, policymakers, and other stakeholders can work collaboratively to reduce the burden of glaucoma and improve visual health and clinical outcomes in vulnerable patient populations.
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Affiliation(s)
- Shaili S. Davuluru
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Alison T. Jess
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Kristy Yoo
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Van Nguyen
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Benjamin Y. Xu
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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32
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Heng JJY, Teo DB, Tan LF. The impact of Chat Generative Pre-trained Transformer (ChatGPT) on medical education. Postgrad Med J 2023; 99:1125-1127. [PMID: 37466157 DOI: 10.1093/postmj/qgad058] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 05/25/2023] [Accepted: 06/01/2023] [Indexed: 07/20/2023]
Abstract
Artificial intelligence (AI) in medicine is developing rapidly. The advent of Chat Generative Pre-trained Transformer (ChatGPT) has taken the world by storm with its potential uses and efficiencies. However, technology leaders, researchers, educators, and policy makers have also sounded the alarm on its potential harms and unintended consequences. AI will increasingly find its way into medicine and is a force of both disruption and innovation. We discuss the potential benefits and limitations of this new league of technology and how medical educators have to develop skills and curricula to best harness this innovative power.
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Affiliation(s)
- Jonathan J Y Heng
- Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore
| | - Desmond B Teo
- Chronic and Fast Programmes, Alexandra Hospital, 159964, Singapore
| | - L F Tan
- Healthy Ageing Programme, Alexandra Hospital, 159964, Singapore
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33
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Rodriguez HC, Rust B, Hansen PY, Maffulli N, Gupta M, Potty AG, Gupta A. Artificial Intelligence and Machine Learning in Rotator Cuff Tears. Sports Med Arthrosc Rev 2023; 31:67-72. [PMID: 37976127 DOI: 10.1097/jsa.0000000000000371] [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: 11/19/2023]
Abstract
Rotator cuff tears (RCTs) negatively impacts patient well-being. Artificial intelligence (AI) is emerging as a promising tool in medical decision-making. Within AI, deep learning allows to autonomously solve complex tasks. This review assesses the current and potential applications of AI in the management of RCT, focusing on diagnostic utility, challenges, and future perspectives. AI demonstrates promise in RCT diagnosis, aiding clinicians in interpreting complex imaging data. Deep learning frameworks, particularly convoluted neural networks architectures, exhibit remarkable diagnostic accuracy in detecting RCTs on magnetic resonance imaging. Advanced segmentation algorithms improve anatomic visualization and surgical planning. AI-assisted radiograph interpretation proves effective in ruling out full-thickness tears. Machine learning models predict RCT diagnosis and postoperative outcomes, enhancing personalized patient care. Challenges include small data sets and classification complexities, especially for partial thickness tears. Current applications of AI in RCT management are promising yet experimental. The potential of AI to revolutionize personalized, efficient, and accurate care for RCT patients is evident. The integration of AI with clinical expertise holds potential to redefine treatment strategies and optimize patient outcomes. Further research, larger data sets, and collaborative efforts are essential to unlock the transformative impact of AI in orthopedic surgery and RCT management.
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Affiliation(s)
- Hugo C Rodriguez
- Department of Orthopaedic Surgery, Larkin Community Hospital, South Miami
- Department of Orthopaedic Surgery, Hospital for Special Surgery Florida, West Palm Beach
| | - Brandon Rust
- Nova Southeastern University, Dr. Kiran Patel College of Osteopathic Medicine, Fort Lauderdale
| | - Payton Yerke Hansen
- Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL
| | - Nicola Maffulli
- Department of Musculoskeletal Disorders, School of Medicine and Surgery, University of Salerno, Fisciano
- San Giovanni di Dio e Ruggi D'Aragona Hospital "Clinica Ortopedica" Department, Hospital of Salerno, Salerno, Italy
- Barts and the London School of Medicine and Dentistry, Centre for Sports and Exercise Medicine, Queen Mary University of London, London
- School of Pharmacy and Bioengineering, Keele University School of Medicine, Stoke on Trent, UK
| | - Manu Gupta
- Polar Aesthetics Dental & Cosmetic Centre, Noida, Uttar Pradesh
| | - Anish G Potty
- South Texas Orthopaedic Research Institute (STORI Inc.), Laredo, TX
| | - Ashim Gupta
- Regenerative Orthopaedics, Noida, India
- South Texas Orthopaedic Research Institute (STORI Inc.), Laredo, TX
- Future Biologics
- BioIntegrate, Lawrenceville, GA
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Liu H, Zhuang Y, Song E, Xu X, Ma G, Cetinkaya C, Hung CC. A modality-collaborative convolution and transformer hybrid network for unpaired multi-modal medical image segmentation with limited annotations. Med Phys 2023; 50:5460-5478. [PMID: 36864700 DOI: 10.1002/mp.16338] [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: 11/28/2022] [Revised: 02/07/2023] [Accepted: 02/22/2023] [Indexed: 03/04/2023] Open
Abstract
BACKGROUND Multi-modal learning is widely adopted to learn the latent complementary information between different modalities in multi-modal medical image segmentation tasks. Nevertheless, the traditional multi-modal learning methods require spatially well-aligned and paired multi-modal images for supervised training, which cannot leverage unpaired multi-modal images with spatial misalignment and modality discrepancy. For training accurate multi-modal segmentation networks using easily accessible and low-cost unpaired multi-modal images in clinical practice, unpaired multi-modal learning has received comprehensive attention recently. PURPOSE Existing unpaired multi-modal learning methods usually focus on the intensity distribution gap but ignore the scale variation problem between different modalities. Besides, within existing methods, shared convolutional kernels are frequently employed to capture common patterns in all modalities, but they are typically inefficient at learning global contextual information. On the other hand, existing methods highly rely on a large number of labeled unpaired multi-modal scans for training, which ignores the practical scenario when labeled data is limited. To solve the above problems, we propose a modality-collaborative convolution and transformer hybrid network (MCTHNet) using semi-supervised learning for unpaired multi-modal segmentation with limited annotations, which not only collaboratively learns modality-specific and modality-invariant representations, but also could automatically leverage extensive unlabeled scans for improving performance. METHODS We make three main contributions to the proposed method. First, to alleviate the intensity distribution gap and scale variation problems across modalities, we develop a modality-specific scale-aware convolution (MSSC) module that can adaptively adjust the receptive field sizes and feature normalization parameters according to the input. Secondly, we propose a modality-invariant vision transformer (MIViT) module as the shared bottleneck layer for all modalities, which implicitly incorporates convolution-like local operations with the global processing of transformers for learning generalizable modality-invariant representations. Third, we design a multi-modal cross pseudo supervision (MCPS) method for semi-supervised learning, which enforces the consistency between the pseudo segmentation maps generated by two perturbed networks to acquire abundant annotation information from unlabeled unpaired multi-modal scans. RESULTS Extensive experiments are performed on two unpaired CT and MR segmentation datasets, including a cardiac substructure dataset derived from the MMWHS-2017 dataset and an abdominal multi-organ dataset consisting of the BTCV and CHAOS datasets. Experiment results show that our proposed method significantly outperforms other existing state-of-the-art methods under various labeling ratios, and achieves a comparable segmentation performance close to single-modal methods with fully labeled data by only leveraging a small portion of labeled data. Specifically, when the labeling ratio is 25%, our proposed method achieves overall mean DSC values of 78.56% and 76.18% in cardiac and abdominal segmentation, respectively, which significantly improves the average DSC value of two tasks by 12.84% compared to single-modal U-Net models. CONCLUSIONS Our proposed method is beneficial for reducing the annotation burden of unpaired multi-modal medical images in clinical applications.
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Affiliation(s)
- Hong Liu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Yuzhou Zhuang
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Enmin Song
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Xiangyang Xu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Guangzhi Ma
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Coskun Cetinkaya
- Center for Machine Vision and Security Research, Kennesaw State University, Kennesaw, Georgia, USA
| | - Chih-Cheng Hung
- Center for Machine Vision and Security Research, Kennesaw State University, Kennesaw, Georgia, USA
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Delsoz M, Madadi Y, Munir WM, Tamm B, Mehravaran S, Soleimani M, Djalilian A, Yousefi S. Performance of ChatGPT in Diagnosis of Corneal Eye Diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.25.23294635. [PMID: 37720035 PMCID: PMC10500623 DOI: 10.1101/2023.08.25.23294635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Introduction Assessing the capabilities of ChatGPT-4.0 and ChatGPT-3.5 for diagnosing corneal eye diseases based on case reports and compare with human experts. Methods We randomly selected 20 cases of corneal diseases including corneal infections, dystrophies, degenerations, and injuries from a publicly accessible online database from the University of Iowa. We then input the text of each case description into ChatGPT-4.0 and ChatGPT3.5 and asked for a provisional diagnosis. We finally evaluated the responses based on the correct diagnoses then compared with the diagnoses of three cornea specialists (Human experts) and evaluated interobserver agreements. Results The provisional diagnosis accuracy based on ChatGPT-4.0 was 85% (17 correct out of 20 cases) while the accuracy of ChatGPT-3.5 was 60% (12 correct cases out of 20). The accuracy of three cornea specialists were 100% (20 cases), 90% (18 cases), and 90% (18 cases), respectively. The interobserver agreement between ChatGPT-4.0 and ChatGPT-3.5 was 65% (13 cases) while the interobserver agreement between ChatGPT-4.0 and three cornea specialists were 85% (17 cases), 80% (16 cases), and 75% (15 cases), respectively. However, the interobserver agreement between ChatGPT-3.5 and each of three cornea specialists was 60% (12 cases). Conclusions The accuracy of ChatGPT-4.0 in diagnosing patients with various corneal conditions was markedly improved than ChatGPT-3.5 and promising for potential clinical integration.
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Affiliation(s)
- Mohammad Delsoz
- Hamilton Eye Institute, Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Yeganeh Madadi
- Hamilton Eye Institute, Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Wuqaas M Munir
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Brendan Tamm
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Shiva Mehravaran
- School of Computer, Mathematical, and Natural Sciences, Morgan State University, Baltimore, MD, USA
| | - Mohammad Soleimani
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, USA
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Djalilian
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Siamak Yousefi
- Hamilton Eye Institute, Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
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Gutiérrez Hidalgo B, Gómez Rivas J, de la Parra I, Marugán MJ, Serrano Á, Hermida Gutiérrez JF, Barrera J, Moreno-Sierra J. The Use of Radiomic Tools in Renal Mass Characterization. Diagnostics (Basel) 2023; 13:2743. [PMID: 37685281 PMCID: PMC10487148 DOI: 10.3390/diagnostics13172743] [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: 06/01/2023] [Revised: 07/26/2023] [Accepted: 08/07/2023] [Indexed: 09/10/2023] Open
Abstract
The incidence of renal mass detection has increased during recent decades, with an increased diagnosis of small renal masses, and a final benign diagnosis in some cases. To avoid unnecessary surgeries, there is an increasing interest in using radiomics tools to predict histological results, using radiological features. We performed a narrative review to evaluate the use of radiomics in renal mass characterization. Conventional images, such as computed tomography (CT) and magnetic resonance (MR), are the most common diagnostic tools in renal mass characterization. Distinguishing between benign and malignant tumors in small renal masses can be challenging using conventional methods. To improve subjective evaluation, the interest in using radiomics to obtain quantitative parameters from medical images has increased. Several studies have assessed this novel tool for renal mass characterization, comparing its ability to distinguish benign to malign tumors, the results in differentiating renal cell carcinoma subtypes, or the correlation with prognostic features, with other methods. In several studies, radiomic tools have shown a good accuracy in characterizing renal mass lesions. However, due to the heterogeneity in the radiomic model building, prospective and external validated studies are needed.
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Affiliation(s)
- Beatriz Gutiérrez Hidalgo
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - Juan Gómez Rivas
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - Irene de la Parra
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - María Jesús Marugán
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - Álvaro Serrano
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - Juan Fco Hermida Gutiérrez
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - Jerónimo Barrera
- Radiodiagnosis Department, Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain
| | - Jesús Moreno-Sierra
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
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Yarin Y, Kalaitzidou A, Bodrova K, Mösges R, Kalaidzidis Y. Validation of AI-based software for objectification of conjunctival provocation test. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. GLOBAL 2023; 2:100121. [PMID: 37779521 PMCID: PMC10509841 DOI: 10.1016/j.jacig.2023.100121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 03/19/2023] [Accepted: 04/07/2023] [Indexed: 10/03/2023]
Abstract
Background Provocation tests are widely used in allergology to objectively reveal patients' sensitivity to specific allergens. The objective quantification of an allergic reaction is a crucial characteristic of these tests. Because of the absence of objective quantitative measurements, the conjunctival provocation test (CPT) is a less frequently used method despite its sensitivity and simplicity. Objective We developed a new artificial intelligence (AI)-based method, called AllergoEye, for quantitative evaluation of conjunctival allergic reactions and validated it in a clinical study. Methods AllergoEye was implemented as a 2-component system. The first component is based on an Android smartphone camera for screening and imaging the patient's eye, and the second is personal computer-based for image analysis and quantification. For the validation of AllergoEye, an open-label, prospective, monocentric study was carried out on 41 patients. Standardized CPT was performed with sequential titration of grass allergens in 4 dilutions, with the reaction evaluated by subjective/qualitative symptom scores and by quantitative AllergoEye scores. Results AllergoEye demonstrated high sensitivity (98%) and specificity (90%) as compared with human estimation of allergic reaction. Tuning cutoff thresholds allowed us to increase the specificity of AllergoEye to 97%, at which point the correlation between detected sensitivity to allergen and specific IgE carrier-polymer system class becomes obvious. Strikingly, such correlation was not found with sensitivity to allergen detected on the basis of subjective and qualitative symptom scores. Conclusion The clinical validation demonstrated that AllergoEye is a sensitive and efficient instrument for objective measurement of allergic reactions in CPT for clinical studies as well as for routine therapy control.
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Affiliation(s)
- Yury Yarin
- Practice for ENT und Allergology, Dresden, Germany
| | | | - Kira Bodrova
- Practice for ENT und Allergology, Dresden, Germany
| | | | - Yannis Kalaidzidis
- Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany
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Starmer AJ, Michael MM, Spector ND, Riesenberg LA. Improving Handoffs in the Perioperative Environment: A Conceptual Framework of Key Theories, System Factors, Methods, and Core Interventions to Ensure Success. Jt Comm J Qual Patient Saf 2023:S1553-7250(23)00130-7. [PMID: 37423813 DOI: 10.1016/j.jcjq.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 06/06/2023] [Accepted: 06/07/2023] [Indexed: 07/11/2023]
Abstract
BACKGROUND Patient handoffs involve the transition of information and responsibility for care from one health care provider to another. They occur frequently during a patient's perioperative care continuum, potentially introducing communication errors that could result in harmful, even fatal consequences. The perioperative environment poses distinct challenges to team communication and patient safety, which in turn leaves the surgical patient uniquely vulnerable to adverse events. CONCEPTUAL FRAMEWORK The best way to achieve safe, coordinated handoffs throughout the perioperative continuum has yet to be established. However, a variety of theoretical principles, methods, and interventions have been used successfully in operative and nonoperative contexts among multiple disciplines. Informed by a literature review, the authors describe a conceptual framework for the development, implementation, and sustainment of a multimodal perioperative handoff improvement bundle. The conceptual framework presented here begins with overarching objectives for patient-centered handoff improvement efforts. The article outlines theoretical principles that could be used to guide and inform future multimodal interventions, as well as health care system factors to consider. Further, the authors propose employing data-driven quality improvement and research methodologies to conduct, measure, achieve, and sustain long-term success. Finally, this report describes essential evidence-based interventional components to employ. IMPLICATIONS Future efforts to improve handoff safety in the perioperative environment will require a comprehensive evidence-based approach. The authors believe the conceptual framework presented here outlines essential components for success. It integrates proven theoretical frameworks, consideration of system factors, data-driven iterative methods, and synergistic patient-centered interventions.
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Stafie CS, Sufaru IG, Ghiciuc CM, Stafie II, Sufaru EC, Solomon SM, Hancianu M. Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review. Diagnostics (Basel) 2023; 13:1995. [PMID: 37370890 DOI: 10.3390/diagnostics13121995] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.
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Affiliation(s)
- Celina Silvia Stafie
- Department of Preventive Medicine and Interdisciplinarity, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Irina-Georgeta Sufaru
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Cristina Mihaela Ghiciuc
- Department of Morpho-Functional Sciences II-Pharmacology and Clinical Pharmacology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Ingrid-Ioana Stafie
- Endocrinology Residency Program, Sf. Spiridon Clinical Emergency Hospital, Independentei 1, 700111 Iasi, Romania
| | | | - Sorina Mihaela Solomon
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Monica Hancianu
- Pharmacognosy-Phytotherapy, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
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Skalidis I, Cagnina A, Luangphiphat W, Mahendiran T, Muller O, Abbe E, Fournier S. ChatGPT takes on the European Exam in Core Cardiology: an artificial intelligence success story? EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2023; 4:279-281. [PMID: 37265864 PMCID: PMC10232281 DOI: 10.1093/ehjdh/ztad029] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 04/12/2023] [Accepted: 04/21/2023] [Indexed: 06/03/2023]
Abstract
Chat Generative Pre-trained Transformer (ChatGPT) is currently a trending topic worldwide triggering extensive debate about its predictive power, its potential uses, and its wider implications. Recent publications have demonstrated that ChatGPT can correctly answer questions from undergraduate exams such as the United States Medical Licensing Examination. We challenged it to answer questions from a more demanding, post-graduate exam-the European Exam in Core Cardiology (EECC), the final exam for the completion of specialty training in Cardiology in many countries. Our results demonstrate that ChatGPT succeeds in the EECC.
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Affiliation(s)
- Ioannis Skalidis
- Cardiology Department, University Hospital of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Aurelien Cagnina
- Cardiology Department, University Hospital of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Wongsakorn Luangphiphat
- Cardiology Department, University Hospital of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Thabo Mahendiran
- Cardiology Department, University Hospital of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland
- Institute of Mathematics and School of Computer and Communication Sciences, EPFL, EPFL FSB SMA, Station 8,1015 Lausanne, Switzerland
| | - Olivier Muller
- Cardiology Department, University Hospital of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Emmanuel Abbe
- Institute of Mathematics and School of Computer and Communication Sciences, EPFL, EPFL FSB SMA, Station 8,1015 Lausanne, Switzerland
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Statz GM, Evans AZ, Johnston SL, Adhaduk M, Mudireddy AR, Sonka M, Lee S, Barsotti EJ, Ricci F, Dipaola F, Johansson M, Sheldon RS, Thiruganasambandamoorthy V, Kenny RA, Bullis TC, Pasupula DK, Van Heukelom J, Gebska MA, Olshansky B. Can Artificial Intelligence Enhance Syncope Management?: A JACC: Advances Multidisciplinary Collaborative Statement. JACC. ADVANCES 2023; 2:100323. [PMID: 38939607 PMCID: PMC11198330 DOI: 10.1016/j.jacadv.2023.100323] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 02/24/2023] [Indexed: 06/29/2024]
Abstract
Syncope, a form of transient loss of consciousness, remains a complex medical condition for which adverse cardiovascular outcomes, including death, are of major concern but rarely occur. Current risk stratification algorithms have not completely delineated which patients benefit from hospitalization and specific interventions. Patients are often admitted unnecessarily and at high cost. Artificial intelligence (AI) and machine learning may help define the transient loss of consciousness event, diagnose the cause, assess short- and long-term risks, predict recurrence, and determine need for hospitalization and therapeutic intervention; however, several challenges remain, including medicolegal and ethical concerns. This collaborative statement, from a multidisciplinary group of clinicians, investigators, and scientists, focuses on the potential role of AI in syncope management with a goal to inspire creation of AI-derived clinical decision support tools that may improve patient outcomes, streamline diagnostics, and reduce health-care costs.
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Affiliation(s)
- Giselle M. Statz
- Division of Cardiovascular Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Aron Z. Evans
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Samuel L. Johnston
- Division of Cardiovascular Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Mehul Adhaduk
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Avinash R. Mudireddy
- The Iowa Initiative for Artificial Intelligence, University of Iowa, Iowa City, Iowa, USA
| | - Milan Sonka
- The Iowa Initiative for Artificial Intelligence, University of Iowa, Iowa City, Iowa, USA
| | - Sangil Lee
- Department of Emergency Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - E. John Barsotti
- Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, Iowa, USA
| | - Fabrizio Ricci
- Department of Neurosciences, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, University G. d’Annunzio, Chieti, Italy
| | - Franca Dipaola
- Internal Medicine, Syncope Unit, IRCCS Humanitas Research Hospital, Humanitas University, Rozzano, Milan, Italy
| | - Madeleine Johansson
- Department of Cardiology, Skåne University Hospital, Lund University, Malmo, Sweden
| | - Robert S. Sheldon
- Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | | | - Rose-Anne Kenny
- Department of Medical Gerontology, School of Medicine, Trinity College, Dublin, Ireland
| | - Tyler C. Bullis
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Deepak K. Pasupula
- Division of Cardiovascular Disease, Department of Internal Medicine, MercyOne North Iowa Heart Center, Mason City, Iowa, USA
| | - Jon Van Heukelom
- Department of Emergency Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Milena A. Gebska
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Brian Olshansky
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
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Valentín Bravo FJ, Mateos Álvarez E. Impact of artificial intelligence and language models in medicine. ARCHIVOS DE LA SOCIEDAD ESPANOLA DE OFTALMOLOGIA 2023:S2173-5794(23)00046-4. [PMID: 37031735 DOI: 10.1016/j.oftale.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Affiliation(s)
| | - E Mateos Álvarez
- Hospital Clínico Universitario de Valladolid, Valladolid, Castilla y León, Spain
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Agliata A, Giordano D, Bardozzo F, Bottiglieri S, Facchiano A, Tagliaferri R. Machine Learning as a Support for the Diagnosis of Type 2 Diabetes. Int J Mol Sci 2023; 24:ijms24076775. [PMID: 37047748 PMCID: PMC10095542 DOI: 10.3390/ijms24076775] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/31/2023] [Accepted: 04/03/2023] [Indexed: 04/07/2023] Open
Abstract
Diabetes is a chronic, metabolic disease characterized by high blood sugar levels. Among the main types of diabetes, type 2 is the most common. Early diagnosis and treatment can prevent or delay the onset of complications. Previous studies examined the application of machine learning techniques for prediction of the pathology, and here an artificial neural network shows very promising results as a possible valuable aid in the management and prevention of diabetes. Additionally, its superior ability for long-term predictions makes it an ideal choice for this field of study. We utilized machine learning methods to uncover previously undiscovered associations between an individual’s health status and the development of type 2 diabetes, with the goal of accurately predicting its onset or determining the individual’s risk level. Our study employed a binary classifier, trained on scratch, to identify potential nonlinear relationships between the onset of type 2 diabetes and a set of parameters obtained from patient measurements. Three datasets were utilized, i.e., the National Center for Health Statistics’ (NHANES) biennial survey, MIMIC-III and MIMIC-IV. These datasets were then combined to create a single dataset with the same number of individuals with and without type 2 diabetes. Since the dataset was balanced, the primary evaluation metric for the model was accuracy. The outcomes of this study were encouraging, with the model achieving accuracy levels of up to 86% and a ROC AUC value of 0.934. Further investigation is needed to improve the reliability of the model by considering multiple measurements from the same patient over time.
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Affiliation(s)
- Antonio Agliata
- Dipartimento di Scienze Aziendali, Management and Innovation Systems, Università degli Studi di Salerno, 84084 Fisciano, Italy
- BC Soft, Centro Direzionale, Via Taddeo da Sessa Isola F10, 80143 Napoli, Italy
| | - Deborah Giordano
- National Research Council, Institute of Food Science, Via Roma 64, 83100 Avellino, Italy
| | - Francesco Bardozzo
- Dipartimento di Scienze Aziendali, Management and Innovation Systems, Università degli Studi di Salerno, 84084 Fisciano, Italy
| | | | - Angelo Facchiano
- National Research Council, Institute of Food Science, Via Roma 64, 83100 Avellino, Italy
| | - Roberto Tagliaferri
- Dipartimento di Scienze Aziendali, Management and Innovation Systems, Università degli Studi di Salerno, 84084 Fisciano, Italy
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Tosaki T, Yamakawa M, Shiina T. A study on the optimal condition of ground truth area for liver tumor detection in ultrasound images using deep learning. J Med Ultrason (2001) 2023; 50:167-176. [PMID: 37014524 PMCID: PMC10182112 DOI: 10.1007/s10396-023-01301-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/16/2023] [Indexed: 04/05/2023]
Abstract
PURPOSE In recent years, efforts to apply artificial intelligence (AI) to the medical field have been growing. In general, a vast amount of high-quality training data is necessary to make great AI. For tumor detection AI, annotation quality is important. In diagnosis and detection of tumors using ultrasound images, humans use not only the tumor area but also the surrounding information, such as the back echo of the tumor. Therefore, we investigated changes in detection accuracy when changing the size of the region of interest (ROI, ground truth area) relative to liver tumors in the training data for the detection AI. METHODS We defined D/L as the ratio of the maximum diameter (D) of the liver tumor to the ROI size (L). We created training data by changing the D/L value, and performed learning and testing with YOLOv3. RESULTS Our results showed that the detection accuracy was highest when the training data were created with a D/L ratio between 0.8 and 1.0. In other words, it was found that the detection accuracy was improved by setting the ground true bounding box for detection AI training to be in contact with the tumor or slightly larger. We also found that when the D/L ratio was distributed in the training data, the wider the distribution, the lower the detection accuracy. CONCLUSIONS Therefore, we recommend that the detector be trained with the D/L value close to a certain value between 0.8 and 1.0 for liver tumor detection from ultrasound images.
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Affiliation(s)
- Taisei Tosaki
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Makoto Yamakawa
- Graduate School of Medicine, Kyoto University, Kyoto, Japan.
- SIT Research Laboratories, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo, 135-8548, Japan.
| | - Tsuyoshi Shiina
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
- SIT Research Laboratories, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo, 135-8548, Japan
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Lévi-Strauss T, Tortorici B, Lopez O, Viau P, Ouizeman DJ, Schall B, Adhoute X, Humbert O, Chevallier P, Gual P, Fillatre L, Anty R. Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma. Diagnostics (Basel) 2023; 13:diagnostics13071303. [PMID: 37046521 PMCID: PMC10093101 DOI: 10.3390/diagnostics13071303] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/23/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
Radiomics is a discipline that involves studying medical images through their digital data. Using “artificial intelligence” algorithms, radiomics utilizes quantitative and high-throughput analysis of an image’s textural richness to obtain relevant information for clinicians, from diagnosis assistance to therapeutic guidance. Exploitation of these data could allow for a more detailed characterization of each phenotype, for each patient, making radiomics a new biomarker of interest, highly promising in the era of precision medicine. Moreover, radiomics is non-invasive, cost-effective, and easily reproducible in time. In the field of oncology, it performs an analysis of the entire tumor, which is impossible with a single biopsy but is essential for understanding the tumor’s heterogeneity and is known to be closely related to prognosis. However, current results are sometimes less accurate than expected and often require the addition of non-radiomics data to create a performing model. To highlight the strengths and weaknesses of this new technology, we take the example of hepatocellular carcinoma and show how radiomics could facilitate its diagnosis in difficult cases, predict certain histological features, and estimate treatment response, whether medical or surgical.
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Affiliation(s)
- Thomas Lévi-Strauss
- Hepatology Unit, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France; (T.L.-S.)
| | - Bettina Tortorici
- Department of Diagnosis and Interventional Imaging, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France
| | - Olivier Lopez
- Department of Diagnosis and Interventional Imaging, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France
| | - Philippe Viau
- Department of Nuclear Medicine, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France
| | - Dann J. Ouizeman
- Hepatology Unit, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France; (T.L.-S.)
| | | | - Xavier Adhoute
- Saint Joseph Hospital, 26 Bd de Louvain, 13008 Marseille, France
| | - Olivier Humbert
- Centre Antoine-Lacassagne, Department of Nuclear Medicine, 33 Av. de Valombrose, 06100 Nice, France
- TIRO-UMR E 4320, Université Côte d’Azur, 06000 Nice, France
| | - Patrick Chevallier
- Department of Diagnosis and Interventional Imaging, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France
| | - Philippe Gual
- INSERM, U1065, C3M, Université Côte d’Azur, 06000 Nice, France
- Correspondence: (P.G.); (R.A.)
| | | | - Rodolphe Anty
- Hepatology Unit, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France; (T.L.-S.)
- INSERM, U1065, C3M, Université Côte d’Azur, 06000 Nice, France
- Correspondence: (P.G.); (R.A.)
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Shim JG, Lee EK, Oh EJ, Cho EA, Park J, Lee JH, Ahn JH. Predicting the risk of inappropriate depth of endotracheal intubation in pediatric patients using machine learning approaches. Sci Rep 2023; 13:5156. [PMID: 36991074 PMCID: PMC10057688 DOI: 10.1038/s41598-023-32122-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 03/22/2023] [Indexed: 03/31/2023] Open
Abstract
Endotracheal tube (ET) misplacement is common in pediatric patients, which can lead to the serious complication. It would be helpful if there is an easy-to-use tool to predict the optimal ET depth considering in each patient's characteristics. Therefore, we plan to develop a novel machine learning (ML) model to predict the appropriate ET depth in pediatric patients. This study retrospectively collected data from 1436 pediatric patients aged < 7 years who underwent chest x-ray examination in an intubated state. Patient data including age, sex, height weight, the internal diameter (ID) of the ET, and ET depth were collected from electronic medical records and chest x-ray. Among these, 1436 data were divided into training (70%, n = 1007) and testing (30%, n = 429) datasets. The training dataset was used to build the appropriate ET depth estimation model, while the test dataset was used to compare the model performance with the formula-based methods such as age-based method, height-based method and tube-ID method. The rate of inappropriate ET location was significantly lower in our ML model (17.9%) compared to formula-based methods (35.7%, 62.2%, and 46.6%). The relative risk [95% confidence interval, CI] of an inappropriate ET location compared to ML model in the age-based, height-based, and tube ID-based method were 1.99 [1.56-2.52], 3.47 [2.80-4.30], and 2.60 [2.07-3.26], respectively. In addition, compared to ML model, the relative risk of shallow intubation tended to be higher in the age-based method, whereas the risk of the deep or endobronchial intubation tended to be higher in the height-based and the tube ID-based method. The use of our ML model was able to predict optimal ET depth for pediatric patients only with basic patient information and reduce the risk of inappropriate ET placement. It will be helpful to clinicians unfamiliar with pediatric tracheal intubation to determine the appropriate ET depth.
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Affiliation(s)
- Jae-Geum Shim
- Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29, Saemoonan-Ro, Jongro-Gu, Seoul, 03181, Republic of Korea
| | - Eun Kyung Lee
- Department of Anesthesiology and Pain Medicine, Chung-Ang University Hospital, Chung-Ang University School of Medicine, Seoul, Republic of Korea
| | - Eun Jung Oh
- Department of Anesthesiology and Pain Medicine, Kwangmyeong Hospital, Chung-Ang University School of Medicine, Kwangmyeong, Republic of Korea
| | - Eun-Ah Cho
- Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29, Saemoonan-Ro, Jongro-Gu, Seoul, 03181, Republic of Korea
| | - Jiyeon Park
- Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29, Saemoonan-Ro, Jongro-Gu, Seoul, 03181, Republic of Korea
| | - Jun-Ho Lee
- Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29, Saemoonan-Ro, Jongro-Gu, Seoul, 03181, Republic of Korea
| | - Jin Hee Ahn
- Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29, Saemoonan-Ro, Jongro-Gu, Seoul, 03181, Republic of Korea.
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Artificial Intelligence in Surgical Learning. SURGERIES 2023. [DOI: 10.3390/surgeries4010010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023] Open
Abstract
(1) Background: Artificial Intelligence (AI) is transforming healthcare on all levels. While AI shows immense potential, the clinical implementation is lagging. We present a concise review of AI in surgical learning; (2) Methods: A non-systematic review of AI in surgical learning of the literature in English is provided; (3) Results: AI shows utility for all components of surgical competence within surgical learning. AI presents with great potential within robotic surgery specifically (4) Conclusions: Technology will evolve in ways currently unimaginable, presenting us with novel applications of AI and derivatives thereof. Surgeons must be open to new modes of learning to be able to implement all evidence-based applications of AI in the future. Systematic analyses of AI in surgical learning are needed.
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Optimization of Thyroid Volume Determination by Stitched 3D-Ultrasound Data Sets in Patients with Structural Thyroid Disease. Biomedicines 2023; 11:biomedicines11020381. [PMID: 36830918 PMCID: PMC9952922 DOI: 10.3390/biomedicines11020381] [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: 01/09/2023] [Revised: 01/23/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
Ultrasound (US) is the most important imaging method for the assessment of structural disorders of the thyroid. A precise volume determination is relevant for therapy planning and outcome monitoring. However, the accuracy of 2D-US is limited, especially in cases of organ enlargements and deformations. Software-based "stitching" of separately acquired 3D-US data revealed precise volume determination in thyroid phantoms. The purpose of this study is to investigate the feasibility and accuracy of 3D-US stitching in patients with structural thyroid disease. A total of 31 patients from the clinical routine were involved, receiving conventional 2D-US (conUS), sensor-navigated 3D-US (3DsnUS), mechanically-swept 3D-US (3DmsUS), and I-124-PET/CT as reference standard. Regarding 3DsnUS and 3DmsUS, separately acquired 3D-US images (per thyroid lobe) were merged to one comprehensive data set. Subsequently, anatomical correctness of the stitching process was analysed via secondary image fusion with the I-124-PET images. Volumetric determinations were conducted by the ellipsoid model (EM) on conUS and CT, and manually drawn segmental contouring (MC) on 3DsnUS, 3DmsUS, CT, and I-124-PET/CT. Mean volume of the thyroid glands was 44.1 ± 25.8 mL (I-124-PET-MC = reference). Highly significant correlations (all p < 0.0001) were observed for conUS-EM (r = 0.892), 3DsnUS-MC (r = 0.988), 3DmsUS-MC (r = 0.978), CT-EM (0.956), and CT-MC (0.986), respectively. The mean volume differences (standard deviations, limits of agreement) in comparison with the reference were -10.50 mL (±11.56 mL, -33.62 to 12.24), -3.74 mL (±3.74 mL, -11.39 to 3.78), and 0.62 mL (±4.79 mL, -8.78 to 10.01) for conUS-EM, 3DsnUS-MC, and 3DmsUS-MC, respectively. Stitched 3D-US data sets of the thyroid enable accurate volumetric determination even in enlarged and deformed organs. The main limitation of high time expenditure may be overcome by artificial intelligence approaches.
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Liu P, Lu L, Chen Y, Huo T, Xue M, Wang H, Fang Y, Xie Y, Xie M, Ye Z. Artificial intelligence to detect the femoral intertrochanteric fracture: The arrival of the intelligent-medicine era. Front Bioeng Biotechnol 2022; 10:927926. [PMID: 36147533 PMCID: PMC9486191 DOI: 10.3389/fbioe.2022.927926] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 08/04/2022] [Indexed: 12/09/2022] Open
Abstract
Objective: To explore a new artificial intelligence (AI)-aided method to assist the clinical diagnosis of femoral intertrochanteric fracture (FIF), and further compare the performance with human level to confirm the effect and feasibility of the AI algorithm.Methods: 700 X-rays of FIF were collected and labeled by two senior orthopedic physicians to set up the database, 643 for the training database and 57 for the test database. A Faster-RCNN algorithm was applied to be trained and detect the FIF on X-rays. The performance of the AI algorithm such as accuracy, sensitivity, miss diagnosis rate, specificity, misdiagnosis rate, and time consumption was calculated and compared with that of orthopedic attending physicians.Results: Compared with orthopedic attending physicians, the Faster-RCNN algorithm performed better in accuracy (0.88 vs. 0.84 ± 0.04), specificity (0.87 vs. 0.71 ± 0.08), misdiagnosis rate (0.13 vs. 0.29 ± 0.08), and time consumption (5 min vs. 18.20 ± 1.92 min). As for the sensitivity and missed diagnosis rate, there was no statistical difference between the AI and orthopedic attending physicians (0.89 vs. 0.87 ± 0.03 and 0.11 vs. 0.13 ± 0.03).Conclusion: The AI diagnostic algorithm is an available and effective method for the clinical diagnosis of FIF. It could serve as a satisfying clinical assistant for orthopedic physicians.
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Affiliation(s)
- Pengran Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lin Lu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yufei Chen
- Department of Orthopedics, The Second Affiliated Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Tongtong Huo
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mingdi Xue
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Honglin Wang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Fang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Xie
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mao Xie
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhewei Ye
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Kumar S, Kumar GS, Maitra SS, Malý P, Bharadwaj S, Sharma P, Dwivedi VD. Viral informatics: bioinformatics-based solution for managing viral infections. Brief Bioinform 2022; 23:6659740. [PMID: 35947964 DOI: 10.1093/bib/bbac326] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/26/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.
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Affiliation(s)
- Sanjay Kumar
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | - Geethu S Kumar
- Department of Life Science, School of Basic Science and Research, Sharda University, Greater Noida, Uttar Pradesh, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | | | - Petr Malý
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Shiv Bharadwaj
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Pradeep Sharma
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Vivek Dhar Dwivedi
- Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India.,Institute of Advanced Materials, IAAM, 59053 Ulrika, Sweden
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