1
|
Liang C, Pan S, Wu W, Chen F, Zhang C, Zhou C, Gao Y, Ruan X, Quan S, Zhao Q, Pan J. Glucocorticoid therapy for sepsis in the AI era: a survey on current and future approaches. Comput Struct Biotechnol J 2024; 24:292-305. [PMID: 38681133 PMCID: PMC11047203 DOI: 10.1016/j.csbj.2024.04.020] [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: 02/05/2024] [Revised: 04/06/2024] [Accepted: 04/07/2024] [Indexed: 05/01/2024] Open
Abstract
Sepsis, a life-threatening medical condition, manifests as new or worsening organ failures due to a dysregulated host response to infection. Many patients with sepsis have manifested a hyperinflammatory phenotype leading to the identification of inflammatory modulation by corticosteroids as a key treatment modality. However, the optimal use of corticosteroids in sepsis treatment remains a contentious subject, necessitating a deeper understanding of their physiological and pharmacological effects. Our study conducts a comprehensive review of randomized controlled trials (RCTs) focusing on traditional corticosteroid treatment in sepsis, alongside an analysis of evolving clinical guidelines. Additionally, we explore the emerging role of artificial intelligence (AI) in medicine, particularly in diagnosing, prognosticating, and treating sepsis. AI's advanced data processing capabilities reveal new avenues for enhancing corticosteroid therapeutic strategies in sepsis. The integration of AI in sepsis treatment has the potential to address existing gaps in knowledge, especially in the application of corticosteroids. Our findings suggest that combining corticosteroid therapy with AI-driven insights could lead to more personalized and effective sepsis treatments. This approach holds promise for improving clinical outcomes and presents a significant advancement in the management of this complex and often fatal condition.
Collapse
Affiliation(s)
- Chenglong Liang
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
- Wenzhou Medical University, Wenzhou 325000, China
- School of Nursing, Wenzhou Medical University, Wenzhou 325000, China
| | - Shuo Pan
- Wenzhou Medical University, Wenzhou 325000, China
| | - Wei Wu
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China
| | - Fanxuan Chen
- Wenzhou Medical University, Wenzhou 325000, China
- School of Biomedical Engineering, School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325000, China
| | - Chengxi Zhang
- School of Materials Science and Engineering, Shandong Jianzhu University, Jinan 250101, China
| | - Chen Zhou
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Yifan Gao
- Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Xiangyuan Ruan
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Shichao Quan
- Department of Big Data in Health Science, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China
| | - Jingye Pan
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
- Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
- Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province, Wenzhou 325000, China
- Wenzhou Key Laboratory of Critical Care and Artificial Intelligence, Wenzhou 325000, China
- Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization, Wenzhou 325000, China
| |
Collapse
|
2
|
Hur S, Yoo J, Min JY, Jeon YJ, Cho JH, Seo JY, Cho D, Kim K, Lee Y, Cha WC. Development, validation, and usability evaluation of machine learning algorithms for predicting personalized red blood cell demand among thoracic surgery patients. Int J Med Inform 2024; 191:105543. [PMID: 39084087 DOI: 10.1016/j.ijmedinf.2024.105543] [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: 03/18/2024] [Revised: 06/23/2024] [Accepted: 07/06/2024] [Indexed: 08/02/2024]
Abstract
INTRODUCTION Preparing appropriate red blood cells (RBCs) before surgery is crucial for improving both the efficacy of perioperative workflow and patient safety. In particular, thoracic surgery (TS) is a procedure that requires massive transfusion with high variability for each patient. Hence, the precise prediction of RBC requirements for individual patients is becoming increasingly important. This study aimed to 1) develop and validate a machine learning algorithm for personalized RBC predictions for TS patients and 2) assess the usability of a clinical decision support system (CDSS) integrating this artificial intelligence model. METHODS Adult patients who underwent TS between January 2016 and October 2021 were included in this study. Multiple models were developed by employing both traditional statistical- and machine-learning approaches. The primary outcome evaluated the model's performance in predicting RBC requirements through root mean square error and adjusted R2. Surgeons and informaticians determined the precision MSBOS-Thoracic Surgery (pMSBOS-TS) algorithm through a consensus process. The usability of the pMSBOS-TS was assessed using the System Usability Scale (SUS) survey with 60 clinicians. RESULTS We identified 7,843 cases (6,200 for training and 1,643 for test sets) of TSs. Among the models with variable performance indices, the extreme gradient boosting model was selected as the pMSBOS-TS algorithm. The pMSBOS-TS model showed statistically significant lower root mean square error (mean: 3.203 and 95% confidence interval [CI]: 3.186-3.220) compared to the calculated Maximum Surgical Blood Ordering Schedule (MSBOS) and a higher adjusted R2 (mean: 0.399 and 95% CI: 0.395-0.403) compared to the calculated MSBOS, while requiring approximately 200 fewer packs for RBC preparation compared to the calculated MSBOS. The SUS score of the pMSBOS-TS CDSS was 72.5 points, indicating good acceptability. CONCLUSIONS We successfully developed the pMSBOS-TS capable of predicting personalized RBC transfusion requirements for perioperative patients undergoing TS.
Collapse
Affiliation(s)
- Sujeong Hur
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea; AvoMD, Seoul, Republic of Korea
| | - Junsang Yoo
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Ji Young Min
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Yeong Jeong Jeon
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jong Ho Cho
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Ji Young Seo
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea; Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Duck Cho
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea; Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyunga Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea; Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Yura Lee
- Department of Biomedical Informatics, Asan Medical Center, Seoul, Republic of Korea
| | - Won Chul Cha
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea; Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Digital Innovation Center, Samsung Medical Center, Seoul, Republic of Korea.
| |
Collapse
|
3
|
Athar M. Potentials of artificial intelligence in familial hypercholesterolemia: Advances in screening, diagnosis, and risk stratification for early intervention and treatment. Int J Cardiol 2024; 412:132315. [PMID: 38972488 DOI: 10.1016/j.ijcard.2024.132315] [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: 01/13/2024] [Revised: 05/21/2024] [Accepted: 07/01/2024] [Indexed: 07/09/2024]
Abstract
Familial hypercholesterolemia (FH) poses a global health challenge due to high incidence rates and underdiagnosis, leading to increased risks of early-onset atherosclerosis and cardiovascular diseases. Early detection and treatment of FH is critical in reducing the risk of cardiovascular events and improving the long-term outcomes and quality of life for affected individuals and their families. Traditional therapeutic approaches revolve around lipid-lowering interventions, yet challenges persist, particularly in accurate and timely diagnosis. The current diagnostic landscape heavily relies on genetic testing of specific LDL-C metabolism genes, often limited to specialized centers. This constraint has led to the adoption of alternative clinical scores for FH diagnosis. However, the rapid advancements in artificial intelligence (AI) and machine learning (ML) present promising solutions to these diagnostic challenges. This review explores the intricacies of FH, highlighting the challenges that are encountered in the diagnosis and management of the disorder. The revolutionary potential of ML, particularly in large-scale population screening, is highlighted. Applications of ML in FH screening, diagnosis, and risk stratification are discussed, showcasing its ability to outperform traditional criteria. However, challenges and ethical considerations, including algorithmic stability, data quality, privacy, and consent issues, are crucial areas that require attention. The review concludes by emphasizing the significant promise of AI and ML in FH management while underscoring the need for ethical and practical vigilance to ensure responsible and effective integration into healthcare practices.
Collapse
Affiliation(s)
- Mohammad Athar
- Science and Technology Unit, Umm Al-Qura University, Makkah, Saudi Arabia; Department of Medical Genetics, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia.
| |
Collapse
|
4
|
Xuereb F, Portelli DJL. The knowledge and perception of patients in Malta towards artificial intelligence in medical imaging. J Med Imaging Radiat Sci 2024; 55:101743. [PMID: 39317135 DOI: 10.1016/j.jmir.2024.101743] [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: 06/03/2024] [Revised: 07/23/2024] [Accepted: 07/31/2024] [Indexed: 09/26/2024]
Abstract
INTRODUCTION Artificial intelligence (AI) is becoming increasingly implemented in radiology, especially in image reporting. Patients' perceptions about AI integration in medical imaging is a relatively unexplored area that has received limited investigation in the literature. This study aimed to determine current knowledge and perceptions of patients in Malta towards AI application in medical imaging. METHODS A cross-sectional study using a self-designed paper-based questionnaire, partly adapted with permission from two previous studies, was distributed in English or Maltese language amongst eligible outpatients attending medical imaging examinations across public hospitals in Malta and Gozo in March 2023. RESULTS 280 questionnaires were analysed, resulting in a 5.83 % confidence interval. 42.1 % of patients indicated basic AI knowledge, while 36.4 % reported minimal to no knowledge. Responses indicated favourable opinions towards the collaborative integration of humans and AI to improve healthcare. However, participants expressed preference for doctors to retain final-decision making when AI is used. For some statements, a statistically significant association was observed between patients' perception of AI-based technology and their gender, age, and educational background. Essentially, 92.1 % expressed the importance of being informed whenever AI is to be utilised in their care. DISCUSSION As key stakeholders, patients should be actively involved when AI technology is used. Informing patients about the use of AI in medical imaging is important to cultivate trust, address ethical concerns, and help ensure that AI integration in healthcare systems aligns with patients' values and needs. CONCLUSION This study highlights the need to enhance AI literacy amongst patients, possibly though awareness campaigns or educational programmes. Additionally, clear policies relating to the use of AI in medical imaging and how such AI use is communicated to patients are necessary.
Collapse
Affiliation(s)
- Francesca Xuereb
- Department of Radiography, Faculty of Health Sciences, University of Malta, Msida, Malta.
| | - Dr Jonathan L Portelli
- Department of Radiography, Faculty of Health Sciences, University of Malta, Msida, Malta.
| |
Collapse
|
5
|
Kang S, Shon B, Park EY, Jeong S, Kim EK. Diagnostic accuracy of dental caries detection using ensemble techniques in deep learning with intraoral camera images. PLoS One 2024; 19:e0310004. [PMID: 39241044 PMCID: PMC11379315 DOI: 10.1371/journal.pone.0310004] [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: 11/02/2023] [Accepted: 08/22/2024] [Indexed: 09/08/2024] Open
Abstract
Camera image-based deep learning (DL) techniques have achieved promising results in dental caries screening. To apply the intraoral camera image-based DL technique for dental caries detection and assess its diagnostic performance, we employed the ensemble technique in the image classification task. 2,682 intraoral camera images were used as the dataset for image classification according to dental caries presence and caries-lesion localization using DL models such as ResNet-50, Inception-v3, Inception-ResNet-v2, and Faster R-convolutional neural network according to diagnostic study design. 534 participants whose mean age [SD] was 47.67 [±13.94] years were enrolled. The dataset was divided into training (56.0%), validation (14.0%), and test subset (30.0%) annotated by one experienced dentist as a reference standard about dental caries detection and lesion location. The confusion matrix, area under the receiver operating characteristic curve (AUROC), and average precision (AP) were evaluated for performance analysis. In the end-to-end dental caries image classification, the ensemble DL models had consistently improved performance, in which as the best results, the ensemble model of Inception-ResNet-v2 achieved 0.94 of AUROC and 0.97 of AP. On the other hand, the explainable model achieved 0.91 of AUROC and 0.96 of AP after the ensemble application. For dental caries classification using intraoral camera images, the application of ensemble techniques exhibited consistently improved performance regardless of the DL models. Furthermore, the trial to create an explainable DL model based on carious lesion detection yielded favorable results.
Collapse
Affiliation(s)
- Sohee Kang
- Department of Dentistry, College of Medicine, Yeungnam University, Daegu, South Korea
| | - Byungeun Shon
- Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, South Korea
- Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Eun Young Park
- Department of Dentistry, College of Medicine, Yeungnam University, Daegu, South Korea
| | - Sungmoon Jeong
- Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, South Korea
- Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Eun-Kyong Kim
- Department of Dental Hygiene, College of Science and Technology, Kyungpook National University, Sangju, South Korea
| |
Collapse
|
6
|
Anawati A, Fleming H, Mertz M, Bertrand J, Dumond J, Myles S, Leblanc J, Ross B, Lamoureux D, Patel D, Carrier R, Cameron E. Artificial intelligence and social accountability in the Canadian health care landscape: A rapid literature review. PLOS DIGITAL HEALTH 2024; 3:e0000597. [PMID: 39264934 PMCID: PMC11392241 DOI: 10.1371/journal.pdig.0000597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/14/2024]
Abstract
BACKGROUND Situated within a larger project entitled "Exploring the Need for a Uniquely Different Approach in Northern Ontario: A Study of Socially Accountable Artificial Intelligence," this rapid review provides a broad look into how social accountability as an equity-oriented health policy strategy is guiding artificial intelligence (AI) across the Canadian health care landscape, particularly for marginalized regions and populations. This review synthesizes existing literature to answer the question: How is AI present and impacted by social accountability across the health care landscape in Canada? METHODOLOGY A multidisciplinary expert panel with experience in diverse health care roles and computer sciences was assembled from multiple institutions in Northern Ontario to guide the study design and research team. A search strategy was developed that broadly reflected the concepts of social accountability, AI and health care in Canada. EMBASE and Medline databases were searched for articles, which were reviewed for inclusion by 2 independent reviewers. Search results, a description of the studies, and a thematic analysis of the included studies were reported as the primary outcome. PRINCIPAL FINDINGS The search strategy yielded 679 articles of which 36 relevant studies were included. There were no studies identified that were guided by a comprehensive, equity-oriented social accountability strategy. Three major themes emerged from the thematic analysis: (1) designing equity into AI; (2) policies and regulations for AI; and (3) the inclusion of community voices in the implementation of AI in health care. Across the 3 main themes, equity, marginalized populations, and the need for community and partner engagement were frequently referenced, which are key concepts of a social accountability strategy. CONCLUSION The findings suggest that unless there is a course correction, AI in the Canadian health care landscape will worsen the digital divide and health inequity. Social accountability as an equity-oriented strategy for AI could catalyze many of the changes required to prevent a worsening of the digital divide caused by the AI revolution in health care in Canada and should raise concerns for other global contexts.
Collapse
Affiliation(s)
- Alex Anawati
- Dr. Gilles Arcand Centre for Health Equity, NOSM University, Thunder Bay/Sudbury, Ontario, Canada
- Clinical Sciences Division, NOSM University, Sudbury/Thunder Bay, Ontario, Canada
- Health Sciences North, Sudbury, Ontario, Canada
| | - Holly Fleming
- Dr. Gilles Arcand Centre for Health Equity, NOSM University, Thunder Bay/Sudbury, Ontario, Canada
| | - Megan Mertz
- Dr. Gilles Arcand Centre for Health Equity, NOSM University, Thunder Bay/Sudbury, Ontario, Canada
| | - Jillian Bertrand
- NOSM University, UME Learner, Sudbury/Thunder Bay, Ontario, Canada
| | - Jennifer Dumond
- Health Sciences Library, NOSM University, Sudbury/Thunder Bay, Ontario, Canada
| | - Sophia Myles
- School of Sociological and Anthropological Studies, University of Ottawa, Ottawa, Ontario, Canada
- School of Kinesiology and Health Sciences, Laurentian University, Sudbury, Ontario, Canada
| | - Joseph Leblanc
- Dr. Gilles Arcand Centre for Health Equity, NOSM University, Thunder Bay/Sudbury, Ontario, Canada
- Human Sciences Division, NOSM University, Sudbury/Thunder Bay, Ontario, Canada
| | - Brian Ross
- Medical Sciences Division, NOSM University, Sudbury/Thunder Bay, Ontario, Canada
| | - Daniel Lamoureux
- NOSM University, UME Learner, Sudbury/Thunder Bay, Ontario, Canada
| | - Div Patel
- NOSM University, UME Learner, Sudbury/Thunder Bay, Ontario, Canada
| | | | - Erin Cameron
- Dr. Gilles Arcand Centre for Health Equity, NOSM University, Thunder Bay/Sudbury, Ontario, Canada
- Human Sciences Division, NOSM University, Sudbury/Thunder Bay, Ontario, Canada
| |
Collapse
|
7
|
Kalidindi S. The Role of Artificial Intelligence in the Diagnosis of Melanoma. Cureus 2024; 16:e69818. [PMID: 39308840 PMCID: PMC11415605 DOI: 10.7759/cureus.69818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2024] [Indexed: 09/25/2024] Open
Abstract
The incidence of melanoma, the most aggressive form of skin cancer, continues to rise globally, particularly among fair-skinned populations (type I and II). Early detection is crucial for improving patient outcomes, and recent advancements in artificial intelligence (AI) have shown promise in enhancing the accuracy and efficiency of melanoma diagnosis and management. This review examines the role of AI in skin lesion diagnostics, highlighting two main approaches: machine learning, particularly convolutional neural networks (CNNs), and expert systems. AI techniques have demonstrated high accuracy in classifying dermoscopic images, often matching or surpassing dermatologists' performance. Integrating AI into dermatology has improved tasks, such as lesion classification, segmentation, and risk prediction, facilitating earlier and more accurate interventions. Despite these advancements, challenges remain, including biases in training data, interpretability issues, and integration of AI into clinical workflows. Ensuring diverse data representation and maintaining high standards of image quality are essential for reliable AI performance. Future directions involve the development of more sophisticated models, such as vision-language and multimodal models, and federated learning to address data privacy and generalizability concerns. Continuous validation and ethical integration of AI into clinical practice are vital for realizing its full potential for improving melanoma diagnosis and patient care.
Collapse
Affiliation(s)
- Sadhana Kalidindi
- Clinical Research, Apollo Radiology International Academy, Hyderabad, IND
| |
Collapse
|
8
|
Bratan T, Schneider D, Funer F, Heyen NB, Klausen A, Liedtke W, Lipprandt M, Salloch S, Langanke M. [Supporting medical and nursing activities with AI: recommendations for responsible design and use]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2024; 67:1039-1046. [PMID: 39017712 PMCID: PMC11349829 DOI: 10.1007/s00103-024-03918-1] [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: 12/14/2023] [Accepted: 06/12/2024] [Indexed: 07/18/2024]
Abstract
Clinical decision support systems (CDSS) based on artificial intelligence (AI) are complex socio-technical innovations and are increasingly being used in medicine and nursing to improve the overall quality and efficiency of care, while also addressing limited financial and human resources. However, in addition to such intended clinical and organisational effects, far-reaching ethical, social and legal implications of AI-based CDSS on patient care and nursing are to be expected. To date, these normative-social implications have not been sufficiently investigated. The BMBF-funded project DESIREE (DEcision Support In Routine and Emergency HEalth Care: Ethical and Social Implications) has developed recommendations for the responsible design and use of clinical decision support systems. This article focuses primarily on ethical and social aspects of AI-based CDSS that could have a negative impact on patient health. Our recommendations are intended as additions to existing recommendations and are divided into the following action fields with relevance across all stakeholder groups: development, clinical use, information and consent, education and training, and (accompanying) research.
Collapse
Affiliation(s)
- Tanja Bratan
- Competence Center Neue Technologien, Fraunhofer-Institut für System- und Innovationsforschung ISI, Breslauer Straße 48, 76139, Karlsruhe, Deutschland.
| | - Diana Schneider
- Competence Center Neue Technologien, Fraunhofer-Institut für System- und Innovationsforschung ISI, Breslauer Straße 48, 76139, Karlsruhe, Deutschland
| | - Florian Funer
- Institut für Ethik, Geschichte und Philosophie der Medizin, Medizinische Hochschule Hannover (MHH), Hannover, Deutschland
- Institut für Ethik und Geschichte der Medizin, Eberhard Karls Universität Tübingen, Tübingen, Deutschland
| | - Nils B Heyen
- Competence Center Neue Technologien, Fraunhofer-Institut für System- und Innovationsforschung ISI, Breslauer Straße 48, 76139, Karlsruhe, Deutschland
| | - Andrea Klausen
- Uniklinik RWTH Aachen, Institut für Medizinische Informatik, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen, Aachen, Deutschland
| | - Wenke Liedtke
- Theologische Fakultät, Universität Greifswald, Greifswald, Deutschland
| | - Myriam Lipprandt
- Uniklinik RWTH Aachen, Institut für Medizinische Informatik, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen, Aachen, Deutschland
| | - Sabine Salloch
- Institut für Ethik, Geschichte und Philosophie der Medizin, Medizinische Hochschule Hannover (MHH), Hannover, Deutschland
| | - Martin Langanke
- Angewandte Ethik/Fachbereich Soziale Arbeit, Evangelische Hochschule Rheinland-Westfalen-Lippe, Bochum, Deutschland
| |
Collapse
|
9
|
Mehari M, Sibih Y, Dada A, Chang SM, Wen PY, Molinaro AM, Chukwueke UN, Budhu JA, Jackson S, McFaline-Figueroa JR, Porter A, Hervey-Jumper SL. Enhancing neuro-oncology care through equity-driven applications of artificial intelligence. Neuro Oncol 2024:noae127. [PMID: 39159285 DOI: 10.1093/neuonc/noae127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/21/2024] Open
Abstract
The disease course and clinical outcome for brain tumor patients depend not only on the molecular and histological features of the tumor but also on the patient's demographics and social determinants of health. While current investigations in neuro-oncology have broadly utilized artificial intelligence (AI) to enrich tumor diagnosis and more accurately predict treatment response, postoperative complications, and survival, equity-driven applications of AI have been limited. However, AI applications to advance health equity in the broader medical field have the potential to serve as practical blueprints to address known disparities in neuro-oncologic care. In this consensus review, we will describe current applications of AI in neuro-oncology, postulate viable AI solutions for the most pressing inequities in neuro-oncology based on broader literature, propose a framework for the effective integration of equity into AI-based neuro-oncology research, and close with the limitations of AI.
Collapse
Affiliation(s)
- Mulki Mehari
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| | - Youssef Sibih
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| | - Abraham Dada
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| | - Susan M Chang
- Division of Neuro-Oncology, University of California San Francisco and Weill Institute for Neurosciences, San Francisco, California, USA
| | - Patrick Y Wen
- Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Annette M Molinaro
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| | - Ugonma N Chukwueke
- Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Joshua A Budhu
- Department of Neurology, Memorial Sloan Kettering Cancer Center, Department of Neurology, Weill Cornell Medicine, Joan & Sanford I. Weill Medical College of Cornell University, New York, New York, USA
| | - Sadhana Jackson
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, Pediatric Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - J Ricardo McFaline-Figueroa
- Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Alyx Porter
- Division of Neuro-Oncology, Department of Neurology, Mayo Clinic, Phoenix, Arizona, USA
| | - Shawn L Hervey-Jumper
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| |
Collapse
|
10
|
Deng J, Qin Y. Current Status, Hotspots, and Prospects of Artificial Intelligence in Ophthalmology: A Bibliometric Analysis (2003-2023). Ophthalmic Epidemiol 2024:1-14. [PMID: 39146462 DOI: 10.1080/09286586.2024.2373956] [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: 03/16/2024] [Revised: 06/01/2024] [Accepted: 06/18/2024] [Indexed: 08/17/2024]
Abstract
PURPOSE Artificial intelligence (AI) has gained significant attention in ophthalmology. This paper reviews, classifies, and summarizes the research literature in this field and aims to provide readers with a detailed understanding of the current status and future directions, laying a solid foundation for further research and decision-making. METHODS Literature was retrieved from the Web of Science database. Bibliometric analysis was performed using VOSviewer, CiteSpace, and the R package Bibliometrix. RESULTS The study included 3,377 publications from 4,035 institutions in 98 countries. China and the United States had the most publications. Sun Yat-sen University is a leading institution. Translational Vision Science & Technology"published the most articles, while "Ophthalmology" had the most co-citations. Among 13,145 researchers, Ting DSW had the most publications and citations. Keywords included "Deep learning," "Diabetic retinopathy," "Machine learning," and others. CONCLUSION The study highlights the promising prospects of AI in ophthalmology. Automated eye disease screening, particularly its core technology of retinal image segmentation and recognition, has become a research hotspot. AI is also expanding to complex areas like surgical assistance, predictive models. Multimodal AI, Generative Adversarial Networks, and ChatGPT have driven further technological innovation. However, implementing AI in ophthalmology also faces many challenges, including technical, regulatory, and ethical issues, and others. As these challenges are overcome, we anticipate more innovative applications, paving the way for more effective and safer eye disease treatments.
Collapse
Affiliation(s)
- Jie Deng
- First Clinical College of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, China
- Graduate School, Hunan University of Chinese Medicine, Changsha, Hunan, China
| | - YuHui Qin
- First Clinical College of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, China
- Graduate School, Hunan University of Chinese Medicine, Changsha, Hunan, China
| |
Collapse
|
11
|
Wang J, Liu G, Zhou C, Cui X, Wang W, Wang J, Huang Y, Jiang J, Wang Z, Tang Z, Zhang A, Cui D. Application of artificial intelligence in cancer diagnosis and tumor nanomedicine. NANOSCALE 2024; 16:14213-14246. [PMID: 39021117 DOI: 10.1039/d4nr01832j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Cancer is a major health concern due to its high incidence and mortality rates. Advances in cancer research, particularly in artificial intelligence (AI) and deep learning, have shown significant progress. The swift evolution of AI in healthcare, especially in tools like computer-aided diagnosis, has the potential to revolutionize early cancer detection. This technology offers improved speed, accuracy, and sensitivity, bringing a transformative impact on cancer diagnosis, treatment, and management. This paper provides a concise overview of the application of artificial intelligence in the realms of medicine and nanomedicine, with a specific emphasis on the significance and challenges associated with cancer diagnosis. It explores the pivotal role of AI in cancer diagnosis, leveraging structured, unstructured, and multimodal fusion data. Additionally, the article delves into the applications of AI in nanomedicine sensors and nano-oncology drugs. The fundamentals of deep learning and convolutional neural networks are clarified, underscoring their relevance to AI-driven cancer diagnosis. A comparative analysis is presented, highlighting the accuracy and efficiency of traditional methods juxtaposed with AI-based approaches. The discussion not only assesses the current state of AI in cancer diagnosis but also delves into the challenges faced by AI in this context. Furthermore, the article envisions the future development direction and potential application of artificial intelligence in cancer diagnosis, offering a hopeful prospect for enhanced cancer detection and improved patient prognosis.
Collapse
Affiliation(s)
- Junhao Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Guan Liu
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Cheng Zhou
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Xinyuan Cui
- Imaging Department of Rui Jin Hospital, Medical School of Shanghai Jiao Tong University, Shanghai, China
| | - Wei Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Jiulin Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Yixin Huang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Jinlei Jiang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Zhitao Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Zengyi Tang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Amin Zhang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China.
| | - Daxiang Cui
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
- School of Medicine, Henan University, Henan, China
| |
Collapse
|
12
|
Burns C, Bakaj A, Berishaj A, Hristidis V, Deak P, Equils O. Use of Generative AI for Improving Health Literacy in Reproductive Health: Case Study. JMIR Form Res 2024; 8:e59434. [PMID: 38986153 PMCID: PMC11336497 DOI: 10.2196/59434] [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: 04/11/2024] [Revised: 06/18/2024] [Accepted: 07/10/2024] [Indexed: 07/12/2024] Open
Abstract
BACKGROUND Patients find technology tools to be more approachable for seeking sensitive health-related information, such as reproductive health information. The inventive conversational ability of artificial intelligence (AI) chatbots, such as ChatGPT (OpenAI Inc), offers a potential means for patients to effectively locate answers to their health-related questions digitally. OBJECTIVE A pilot study was conducted to compare the novel ChatGPT with the existing Google Search technology for their ability to offer accurate, effective, and current information regarding proceeding action after missing a dose of oral contraceptive pill. METHODS A sequence of 11 questions, mimicking a patient inquiring about the action to take after missing a dose of an oral contraceptive pill, were input into ChatGPT as a cascade, given the conversational ability of ChatGPT. The questions were input into 4 different ChatGPT accounts, with the account holders being of various demographics, to evaluate potential differences and biases in the responses given to different account holders. The leading question, "what should I do if I missed a day of my oral contraception birth control?" alone was then input into Google Search, given its nonconversational nature. The results from the ChatGPT questions and the Google Search results for the leading question were evaluated on their readability, accuracy, and effective delivery of information. RESULTS The ChatGPT results were determined to be at an overall higher-grade reading level, with a longer reading duration, less accurate, less current, and with a less effective delivery of information. In contrast, the Google Search resulting answer box and snippets were at a lower-grade reading level, shorter reading duration, more current, able to reference the origin of the information (transparent), and provided the information in various formats in addition to text. CONCLUSIONS ChatGPT has room for improvement in accuracy, transparency, recency, and reliability before it can equitably be implemented into health care information delivery and provide the potential benefits it poses. However, AI may be used as a tool for providers to educate their patients in preferred, creative, and efficient ways, such as using AI to generate accessible short educational videos from health care provider-vetted information. Larger studies representing a diverse group of users are needed.
Collapse
Affiliation(s)
- Christina Burns
- MiOra, Encino, CA, United States
- University of California San Diego, San Diego, CA, United States
| | - Angela Bakaj
- MiOra, Encino, CA, United States
- Institute for Management & Innovation, University of Toronto, Toronto, ON, Canada
| | - Amonda Berishaj
- MiOra, Encino, CA, United States
- College of Professional Studies, Northeastern University, Boston, MA, United States
| | - Vagelis Hristidis
- Computer Science and Engineering, University of California Riverside, Riverside, CA, United States
| | - Pamela Deak
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, San Diego, CA, United States
| | | |
Collapse
|
13
|
Zhang X, Zhang D, Zhang X, Zhang X. Artificial intelligence applications in the diagnosis and treatment of bacterial infections. Front Microbiol 2024; 15:1449844. [PMID: 39165576 PMCID: PMC11334354 DOI: 10.3389/fmicb.2024.1449844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2024] [Accepted: 07/04/2024] [Indexed: 08/22/2024] Open
Abstract
The diagnosis and treatment of bacterial infections in the medical and public health field in the 21st century remain significantly challenging. Artificial Intelligence (AI) has emerged as a powerful new tool in diagnosing and treating bacterial infections. AI is rapidly revolutionizing epidemiological studies of infectious diseases, providing effective early warning, prevention, and control of outbreaks. Machine learning models provide a highly flexible way to simulate and predict the complex mechanisms of pathogen-host interactions, which is crucial for a comprehensive understanding of the nature of diseases. Machine learning-based pathogen identification technology and antimicrobial drug susceptibility testing break through the limitations of traditional methods, significantly shorten the time from sample collection to the determination of result, and greatly improve the speed and accuracy of laboratory testing. In addition, AI technology application in treating bacterial infections, particularly in the research and development of drugs and vaccines, and the application of innovative therapies such as bacteriophage, provides new strategies for improving therapy and curbing bacterial resistance. Although AI has a broad application prospect in diagnosing and treating bacterial infections, significant challenges remain in data quality and quantity, model interpretability, clinical integration, and patient privacy protection. To overcome these challenges and, realize widespread application in clinical practice, interdisciplinary cooperation, technology innovation, and policy support are essential components of the joint efforts required. In summary, with continuous advancements and in-depth application of AI technology, AI will enable doctors to more effectivelyaddress the challenge of bacterial infection, promoting the development of medical practice toward precision, efficiency, and personalization; optimizing the best nursing and treatment plans for patients; and providing strong support for public health safety.
Collapse
Affiliation(s)
- Xiaoyu Zhang
- First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Deng Zhang
- Department of Infectious Diseases, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Xifan Zhang
- First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Xin Zhang
- First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China
| |
Collapse
|
14
|
Inouye K, Petrosyan A, Moskalensky L, Thankam FG. Artificial intelligence in therapeutic management of hyperlipidemic ocular pathology. Exp Eye Res 2024; 245:109954. [PMID: 38838975 DOI: 10.1016/j.exer.2024.109954] [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: 10/10/2023] [Revised: 04/09/2024] [Accepted: 06/02/2024] [Indexed: 06/07/2024]
Abstract
Hyperlipidemia has many ocular manifestations, the most prevalent being retinal vascular occlusion. Hyperlipidemic lesions and occlusions to the vessels supplying the retina result in permanent blindness, necessitating prompt detection and treatment. Retinal vascular occlusion is diagnosed using different imaging modalities, including optical coherence tomography angiography. These diagnostic techniques obtain images representing the blood flow through the retinal vessels, providing an opportunity for AI to utilize image recognition to detect blockages and abnormalities before patients present with symptoms. AI is already being used as a non-invasive method to detect retinal vascular occlusions and other vascular pathology, as well as predict treatment outcomes. As providers see an increase in patients presenting with new retinal vascular occlusions, the use of AI to detect and treat these conditions has the potential to improve patient outcomes and reduce the financial burden on the healthcare system. This article comprehends the implications of AI in the current management strategies of retinal vascular occlusion (RVO) in hyperlipidemia and the recent developments of AI technology in the management of ocular diseases.
Collapse
Affiliation(s)
- Keiko Inouye
- Department of Translational Research, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, USA
| | - Aelita Petrosyan
- Department of Translational Research, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, USA
| | - Liana Moskalensky
- Department of Translational Research, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, USA
| | - Finosh G Thankam
- Department of Translational Research, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, USA.
| |
Collapse
|
15
|
Szabó V, Szabó BT, Orhan K, Veres DS, Manulis D, Ezhov M, Sanders A. Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs. J Dent 2024; 147:105105. [PMID: 38821394 DOI: 10.1016/j.jdent.2024.105105] [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: 10/10/2023] [Revised: 05/21/2024] [Accepted: 05/28/2024] [Indexed: 06/02/2024] Open
Abstract
OBJECTIVES This study aimed to assess the reliability of AI-based system that assists the healthcare processes in the diagnosis of caries on intraoral radiographs. METHODS The proximal surfaces of the 323 selected teeth on the intraoral radiographs were evaluated by two independent observers using an AI-based (Diagnocat) system. The presence or absence of carious lesions was recorded during Phase 1. After 4 months, the AI-aided human observers evaluated the same radiographs (Phase 2), and the advanced convolutional neural network (CNN) reassessed the radiographic data (Phase 3). Subsequently, data reflecting human disagreements were excluded (Phase 4). For each phase, the Cohen and Fleiss kappa values, as well as the sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy of Diagnocat, were calculated. RESULTS During the four phases, the range of Cohen kappa values between the human observers and Diagnocat were κ=0.66-1, κ=0.58-0.7, and κ=0.49-0.7. The Fleiss kappa values were κ=0.57-0.8. The sensitivity, specificity and diagnostic accuracy values ranged between 0.51-0.76, 0.88-0.97 and 0.76-0.86, respectively. CONCLUSIONS The Diagnocat CNN supports the evaluation of intraoral radiographs for caries diagnosis, as determined by consensus between human and AI system observers. CLINICAL SIGNIFICANCE Our study may aid in the understanding of deep learning-based systems developed for dental imaging modalities for dentists and contribute to expanding the body of results in the field of AI-supported dental radiology..
Collapse
Affiliation(s)
- Viktor Szabó
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, Budapest, Hungary
| | - Bence Tamás Szabó
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, Budapest, Hungary.
| | - Kaan Orhan
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, Budapest, Hungary; Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey; Medical Design Application, and Research Center (MEDITAM), Ankara University, Ankara, Turkey
| | - Dániel Sándor Veres
- Department of Biophysics and Radiation Biology, Semmelweis University, Budapest, Hungary
| | | | | | | |
Collapse
|
16
|
Li J, Kot WY, McGrath CP, Chan BWA, Ho JWK, Zheng LW. Diagnostic accuracy of artificial intelligence assisted clinical imaging in the detection of oral potentially malignant disorders and oral cancer: a systematic review and meta-analysis. Int J Surg 2024; 110:5034-5046. [PMID: 38652301 PMCID: PMC11325952 DOI: 10.1097/js9.0000000000001469] [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: 01/10/2024] [Accepted: 03/30/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND The objective of this study is to examine the application of artificial intelligence (AI) algorithms in detecting oral potentially malignant disorders (OPMD) and oral cancerous lesions, and to evaluate the accuracy variations among different imaging tools employed in these diagnostic processes. MATERIALS AND METHODS A systematic search was conducted in four databases: Embase, Web of Science, PubMed, and Scopus. The inclusion criteria included studies using machine learning algorithms to provide diagnostic information on specific oral lesions, prospective or retrospective design, and inclusion of OPMD. Sensitivity and specificity analyses were also required. Forest plots were generated to display overall diagnostic odds ratio (DOR), sensitivity, specificity, negative predictive values, and summary receiver operating characteristic (SROC) curves. Meta-regression analysis was conducted to examine potential differences among different imaging tools. RESULTS The overall DOR for AI-based screening of OPMD and oral mucosal cancerous lesions from normal mucosa was 68.438 (95% CI= [39.484-118.623], I2 =86%). The area under the SROC curve was 0.938, indicating excellent diagnostic performance. AI-assisted screening showed a sensitivity of 89.9% (95% CI= [0.866-0.925]; I2 =81%), specificity of 89.2% (95% CI= [0.851-0.922], I2 =79%), and a high negative predictive value of 89.5% (95% CI= [0.851-0.927], I2 =96%). Meta-regression analysis revealed no significant difference among the three image tools. After generating a GOSH plot, the DOR was calculated to be 49.30, and the area under the SROC curve was 0.877. Additionally, sensitivity, specificity, and negative predictive value were 90.5% (95% CI [0.873-0.929], I2 =4%), 87.0% (95% CI [0.813-0.912], I2 =49%) and 90.1% (95% CI [0.860-0.931], I2 =57%), respectively. Subgroup analysis showed that clinical photography had the highest diagnostic accuracy. CONCLUSIONS AI-based detection using clinical photography shows a high DOR and is easily accessible in the current era with billions of phone subscribers globally. This indicates that there is significant potential for AI to enhance the diagnostic capabilities of general practitioners to the level of specialists by utilizing clinical photographs, without the need for expensive specialized imaging equipment.
Collapse
Affiliation(s)
- JingWen Li
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong
| | - Wai Ying Kot
- Faculty of Dentistry, The University of Hong Kong
| | - Colman Patrick McGrath
- Division of Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong
| | - Bik Wan Amy Chan
- Department of Anatomical and Cellular Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong
| | - Joshua Wing Kei Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, People's Republic of China
| | - Li Wu Zheng
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong
| |
Collapse
|
17
|
El Hachimy I, Kabelma D, Echcharef C, Hassani M, Benamar N, Hajji N. A comprehensive survey on the use of deep learning techniques in glioblastoma. Artif Intell Med 2024; 154:102902. [PMID: 38852314 DOI: 10.1016/j.artmed.2024.102902] [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: 10/12/2023] [Revised: 04/28/2024] [Accepted: 06/02/2024] [Indexed: 06/11/2024]
Abstract
Glioblastoma, characterized as a grade 4 astrocytoma, stands out as the most aggressive brain tumor, often leading to dire outcomes. The challenge of treating glioblastoma is exacerbated by the convergence of genetic mutations and disruptions in gene expression, driven by alterations in epigenetic mechanisms. The integration of artificial intelligence, inclusive of machine learning algorithms, has emerged as an indispensable asset in medical analyses. AI is becoming a necessary tool in medicine and beyond. Current research on Glioblastoma predominantly revolves around non-omics data modalities, prominently including magnetic resonance imaging, computed tomography, and positron emission tomography. Nonetheless, the assimilation of omic data-encompassing gene expression through transcriptomics and epigenomics-offers pivotal insights into patients' conditions. These insights, reciprocally, hold significant value in refining diagnoses, guiding decision- making processes, and devising efficacious treatment strategies. This survey's core objective encompasses a comprehensive exploration of noteworthy applications of machine learning methodologies in the domain of glioblastoma, alongside closely associated research pursuits. The study accentuates the deployment of artificial intelligence techniques for both non-omics and omics data, encompassing a range of tasks. Furthermore, the survey underscores the intricate challenges posed by the inherent heterogeneity of Glioblastoma, delving into strategies aimed at addressing its multifaceted nature.
Collapse
Affiliation(s)
| | | | | | - Mohamed Hassani
- Cancer Division, Faculty of medicine, Department of Biomolecular Medicine, Imperial College, London, United Kingdom
| | - Nabil Benamar
- Moulay Ismail University of Meknes, Meknes, Morocco; Al Akhawayn University in Ifrane, Ifrane, Morocco.
| | - Nabil Hajji
- Cancer Division, Faculty of medicine, Department of Biomolecular Medicine, Imperial College, London, United Kingdom; Department of Medical Biochemistry, Molecular Biology and Immunology, School of Medicine, Virgen Macarena University Hospital, University of Seville, Seville, Spain
| |
Collapse
|
18
|
Rabindranath M, Naghibzadeh M, Zhao X, Holdsworth S, Brudno M, Sidhu A, Bhat M. Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold? Transplantation 2024; 108:1700-1708. [PMID: 39042768 DOI: 10.1097/tp.0000000000004876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.
Collapse
Affiliation(s)
- Madhumitha Rabindranath
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
| | - Maryam Naghibzadeh
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Xun Zhao
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Sandra Holdsworth
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Michael Brudno
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Aman Sidhu
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Mamatha Bhat
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
19
|
Elste J, Saini A, Mejia-Alvarez R, Mejía A, Millán-Pacheco C, Swanson-Mungerson M, Tiwari V. Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions. Biomolecules 2024; 14:911. [PMID: 39199298 PMCID: PMC11352483 DOI: 10.3390/biom14080911] [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: 06/13/2024] [Revised: 07/11/2024] [Accepted: 07/23/2024] [Indexed: 09/01/2024] Open
Abstract
A highly critical event in a virus's life cycle is successfully entering a given host. This process begins when a viral glycoprotein interacts with a target cell receptor, which provides the molecular basis for target virus-host cell interactions for novel drug discovery. Over the years, extensive research has been carried out in the field of virus-host cell interaction, generating a massive number of genetic and molecular data sources. These datasets are an asset for predicting virus-host interactions at the molecular level using machine learning (ML), a subset of artificial intelligence (AI). In this direction, ML tools are now being applied to recognize patterns in these massive datasets to predict critical interactions between virus and host cells at the protein-protein and protein-sugar levels, as well as to perform transcriptional and translational analysis. On the other end, deep learning (DL) algorithms-a subfield of ML-can extract high-level features from very large datasets to recognize the hidden patterns within genomic sequences and images to develop models for rapid drug discovery predictions that address pathogenic viruses displaying heightened affinity for receptor docking and enhanced cell entry. ML and DL are pivotal forces, driving innovation with their ability to perform analysis of enormous datasets in a highly efficient, cost-effective, accurate, and high-throughput manner. This review focuses on the complexity of virus-host cell interactions at the molecular level in light of the current advances of ML and AI in viral pathogenesis to improve new treatments and prevention strategies.
Collapse
Affiliation(s)
- James Elste
- Department of Microbiology & Immunology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA; (J.E.); (M.S.-M.)
| | - Akash Saini
- Hinsdale Central High School, 5500 S Grant St, Hinsdale, IL 60521, USA;
| | - Rafael Mejia-Alvarez
- Department of Physiology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA;
| | - Armando Mejía
- Departamento de Biotechnology, Universidad Autónoma Metropolitana-Iztapalapa, Ciudad de Mexico 09340, Mexico;
| | - Cesar Millán-Pacheco
- Facultad de Farmacia, Universidad Autónoma del Estado de Morelos, Av. Universidad No. 1001, Col Chamilpa, Cuernavaca 62209, Mexico;
| | - Michelle Swanson-Mungerson
- Department of Microbiology & Immunology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA; (J.E.); (M.S.-M.)
| | - Vaibhav Tiwari
- Department of Microbiology & Immunology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA; (J.E.); (M.S.-M.)
| |
Collapse
|
20
|
Feng X, Wu W, Bi Q. Reform of teaching and practice of the integrated teaching method BOPPPS-PBL in the course "clinical haematological test technique". BMC MEDICAL EDUCATION 2024; 24:773. [PMID: 39030580 PMCID: PMC11264887 DOI: 10.1186/s12909-024-05765-9] [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: 04/16/2024] [Accepted: 07/11/2024] [Indexed: 07/21/2024]
Abstract
BACKGROUND In order to meet the demand for laboratory talents in the clinical laboratory industry and address the current curriculum characteristics and shortcomings of the teaching mode of "Clinical Hematology Laboratory Technology", we investigated the effectiveness of the bridge-in, objective, pre-assessment, participatory learning, post-assessment, and summary model combined with problem-based learning (BOPPPS-PBL) in undergraduate teaching of this course. METHOD Seventy students majoring in Medical Laboratory Technology from the Army Medical University in the past 5 years have been selected and divided into two groups with the same teaching content and time. The control group (2015 and 2016 grades) used traditional teaching methods, while the experimental group (2017, 2018 and 2019 grades) used the BOPPPS-PBL model. After class, diverse evaluation methods were used to analyze the formative and summative exam scores of the two groups of students. RESULTS After the reform, students performed significantly better in exams than before. In addition, the new teaching methods have had a positive impact, with students demonstrating high motivation for self-directed learning and problem-solving abilities. CONCLUSION Compared to traditional teaching methods. The BOPPPS-PBL integrated case study education model is a relatively effective teaching method to improve students' problem-solving ability and comprehensive practical ability.
Collapse
Affiliation(s)
- Xinrui Feng
- Department of Clinical Hematology, College of Pharmacy and Laboratory Medicine Science, Army Medical University, Chongqing, 400038, China
| | - Weiru Wu
- Department of Clinical Hematology, College of Pharmacy and Laboratory Medicine Science, Army Medical University, Chongqing, 400038, China
| | - Qinghua Bi
- Department of Clinical Hematology, College of Pharmacy and Laboratory Medicine Science, Army Medical University, Chongqing, 400038, China.
| |
Collapse
|
21
|
Weatherall T, Avsar P, Nugent L, Moore Z, McDermott JH, Sreenan S, Wilson H, McEvoy NL, Derwin R, Chadwick P, Patton D. The impact of machine learning on the prediction of diabetic foot ulcers - A systematic review. J Tissue Viability 2024:S0965-206X(24)00109-8. [PMID: 39019690 DOI: 10.1016/j.jtv.2024.07.004] [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: 01/11/2024] [Revised: 06/24/2024] [Accepted: 07/10/2024] [Indexed: 07/19/2024]
Abstract
INTRODUCTION Globally, diabetes mellitus poses a significant health challenge as well as the associated complications of diabetes, such as diabetic foot ulcers (DFUs). The early detection of DFUs is important in the healing process and machine learning may be able to help inform clinical staff during the treatment process. METHODS A PRISMA-informed search of the literature was completed via the Cochrane Library and MEDLINE (OVID), EMBASE, CINAHL Plus and Scopus databases for reports published in English and in the last ten years. The primary outcome of interest was the impact of machine learning on the prediction of DFUs. The secondary outcome was the statistical performance measures reported. Data were extracted using a predesigned data extraction tool. Quality appraisal was undertaken using the evidence-based librarianship critical appraisal tool. RESULTS A total of 18 reports met the inclusion criteria. Nine reports proposed models to identify two classes, either healthy skin or a DFU. Nine reports proposed models to predict the progress of DFUs, for example, classing infection versus non-infection, or using wound characteristics to predict healing. A variety of machine learning techniques were proposed. Where reported, sensitivity = 74.53-98 %, accuracy = 64.6-99.32 %, precision = 62.9-99 %, and the F-measure = 52.05-99.0 %. CONCLUSIONS A variety of machine learning models were suggested to successfully classify DFUs from healthy skin, or to inform the prediction of DFUs. The proposed machine learning models may have the potential to inform the clinical practice of managing DFUs and may help to improve outcomes for individuals with DFUs. Future research may benefit from the development of a standard device and algorithm that detects, diagnoses and predicts the progress of DFUs.
Collapse
Affiliation(s)
- Teagan Weatherall
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
| | - Pinar Avsar
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
| | - Linda Nugent
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland; Fakeeh College of Medical Sciences, Jeddah, Saudi Arabia.
| | - Zena Moore
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland; Fakeeh College of Medical Sciences, Jeddah, Saudi Arabia; School of Nursing and Midwifery, Griffith University, Southport, Queensland, Australia; Lida Institute, Shanghai, China; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia; Department of Public Health, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; University of Wales, Cardiff, UK; National Health and Medical Research Council Centre of Research Excellence in Wiser Wound Care, Menzies Health Institute Queensland, Southport, Queensland, Australia.
| | - John H McDermott
- Department of Endocrinology, Royal College of Surgeons in Ireland, Connolly Hospital Blanchardstown, Dublin, Ireland.
| | - Seamus Sreenan
- Department of Endocrinology, Royal College of Surgeons in Ireland, Connolly Hospital Blanchardstown, Dublin, Ireland.
| | - Hannah Wilson
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
| | - Natalie L McEvoy
- School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
| | - Rosemarie Derwin
- School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
| | - Paul Chadwick
- Birmingham City University, Birmingham, UK; Spectral MD, London, UK.
| | - Declan Patton
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland; Fakeeh College of Medical Sciences, Jeddah, Saudi Arabia; School of Nursing and Midwifery, Griffith University, Southport, Queensland, Australia; Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, New South Wales, Australia.
| |
Collapse
|
22
|
Calogero AE, Crafa A, Cannarella R, Saleh R, Shah R, Agarwal A. Artificial intelligence in andrology - fact or fiction: essential takeaway for busy clinicians. Asian J Androl 2024:00129336-990000000-00203. [PMID: 38978280 DOI: 10.4103/aja202431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 03/25/2024] [Indexed: 07/10/2024] Open
Abstract
ABSTRACT Artificial intelligence (AI) is revolutionizing the current approach to medicine. AI uses machine learning algorithms to predict the success of therapeutic procedures or assist the clinician in the decision-making process. To date, machine learning studies in the andrological field have mainly focused on prostate cancer imaging and management. However, an increasing number of studies are documenting the use of AI to assist clinicians in decision-making and patient management in andrological diseases such as varicocele or sexual dysfunction. Additionally, machine learning applications are being employed to enhance success rates in assisted reproductive techniques (ARTs). This article offers the clinicians as well as the researchers with a brief overview of the current use of AI in andrology, highlighting the current state-of-the-art scientific evidence, the direction in which the research is going, and the strengths and limitations of this approach.
Collapse
Affiliation(s)
- Aldo E Calogero
- Department of Clinical and Experimental Medicine, University of Catania, Catania 95123, Italy
- Global Andrology Forum, Moreland Hills, OH 44022, USA
| | - Andrea Crafa
- Department of Clinical and Experimental Medicine, University of Catania, Catania 95123, Italy
- Global Andrology Forum, Moreland Hills, OH 44022, USA
| | - Rossella Cannarella
- Department of Clinical and Experimental Medicine, University of Catania, Catania 95123, Italy
- Global Andrology Forum, Moreland Hills, OH 44022, USA
- Glickman Urological and Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Ramadan Saleh
- Global Andrology Forum, Moreland Hills, OH 44022, USA
- Department of Dermatology, Venereology and Andrology, Faculty of Medicine, Sohag University, Sohag 82524, Egypt
- Ajyal IVF Center, Ajyal Hospital, Sohag 82511, Egypt
| | - Rupin Shah
- Global Andrology Forum, Moreland Hills, OH 44022, USA
- Division of Andrology, Department of Urology, Lilavati Hospital and Research Centre, Mumbai 400050, India
| | - Ashok Agarwal
- Global Andrology Forum, Moreland Hills, OH 44022, USA
- Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| |
Collapse
|
23
|
Maita KC, Avila FR, Torres-Guzman RA, Garcia JP, De Sario Velasquez GD, Borna S, Brown SA, Haider CR, Ho OS, Forte AJ. The usefulness of artificial intelligence in breast reconstruction: a systematic review. Breast Cancer 2024; 31:562-571. [PMID: 38619786 DOI: 10.1007/s12282-024-01582-6] [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: 03/29/2023] [Accepted: 03/30/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND Artificial Intelligence (AI) offers an approach to predictive modeling. The model learns to determine specific patterns of undesirable outcomes in a dataset. Therefore, a decision-making algorithm can be built based on these patterns to prevent negative results. This systematic review aimed to evaluate the usefulness of AI in breast reconstruction. METHODS A systematic review was conducted in August 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. MEDLINE, EMBASE, SCOPUS, and Google Scholar online databases were queried to capture all publications studying the use of artificial intelligence in breast reconstruction. RESULTS A total of 23 studies were full text-screened after removing duplicates, and twelve articles fulfilled our inclusion criteria. The Machine Learning algorithms applied for neuropathic pain, lymphedema diagnosis, microvascular abdominal flap failure, donor site complications associated to muscle sparing Transverse Rectus Abdominis flap, surgical complications, financial toxicity, and patient-reported outcomes after breast surgery demonstrated that AI is a helpful tool to accurately predict patient results. In addition, one study used Computer Vision technology to assist in Deep Inferior Epigastric Perforator Artery detection for flap design, considerably reducing the preoperative time compared to manual identification. CONCLUSIONS In breast reconstruction, AI can help the surgeon by optimizing the perioperative patients' counseling to predict negative outcomes, allowing execution of timely interventions and reducing the postoperative burden, which leads to obtaining the most successful results and improving patient satisfaction.
Collapse
Affiliation(s)
- Karla C Maita
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | - Francisco R Avila
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | | | - John P Garcia
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | | | - Sahar Borna
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | - Sally A Brown
- Department of Administration, Mayo Clinic, Jacksonville, FL, USA
| | - Clifton R Haider
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Olivia S Ho
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | - Antonio Jorge Forte
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA.
| |
Collapse
|
24
|
González-Pérez Y, Montero Delgado A, Martinez Sesmero JM. [Translated article] Introducing artificial intelligence to hospital pharmacy departments. FARMACIA HOSPITALARIA 2024; 48 Suppl 1:TS35-TS44. [PMID: 39097375 DOI: 10.1016/j.farma.2024.04.001] [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: 09/14/2023] [Revised: 01/03/2024] [Accepted: 02/14/2024] [Indexed: 08/05/2024] Open
Abstract
Artificial intelligence is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, Artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks, or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. Artificial intelligence has been introduced in biomedicine, accelerating processes, improving accuracy and efficiency, and improving patient care. By using Artificial intelligence algorithms and machine learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. Artificial intelligence integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master Artificial intelligence will play a crucial role in this transformation.
Collapse
Affiliation(s)
- Yared González-Pérez
- Servicio de Farmacia, Hospital Universitario de Canarias, San Cristóbal de La Laguna, Spain.
| | - Alfredo Montero Delgado
- Servicio de Farmacia, Hospital Nuestra Señora de la Candelaria, Santa Cruz de Tenerife, Spain
| | | |
Collapse
|
25
|
Davis JMK, Niazi MKK, Ricker AB, Tavolara TE, Robinson JN, Annanurov B, Smith K, Mantha R, Hwang J, Shrestha R, Iannitti DA, Martinie JB, Baker EH, Gurcan MN, Vrochides D. Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging. J Surg Oncol 2024; 130:93-101. [PMID: 38712939 DOI: 10.1002/jso.27673] [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: 10/21/2023] [Revised: 04/13/2024] [Accepted: 04/28/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND AND OBJECTIVES Deep learning models (DLMs) are applied across domains of health sciences to generate meaningful predictions. DLMs make use of neural networks to generate predictions from discrete data inputs. This study employs DLM on prechemotherapy cross-sectional imaging to predict patients' response to neoadjuvant chemotherapy. METHODS Adult patients with colorectal liver metastasis who underwent surgery after neoadjuvant chemotherapy were included. A DLM was trained on computed tomography images using attention-based multiple-instance learning. A logistic regression model incorporating clinical parameters of the Fong clinical risk score was used for comparison. Both model performances were benchmarked against the Response Evaluation Criteria in Solid Tumors criteria. A receiver operating curve was created and resulting area under the curve (AUC) was determined. RESULTS Ninety-five patients were included, with 33,619 images available for study inclusion. Ninety-five percent of patients underwent 5-fluorouracil-based chemotherapy with oxaliplatin and/or irinotecan. Sixty percent of the patients were categorized as chemotherapy responders (30% reduction in tumor diameter). The DLM had an AUC of 0.77. The AUC for the clinical model was 0.41. CONCLUSIONS Image-based DLM for prediction of response to neoadjuvant chemotherapy in patients with colorectal cancer liver metastases was superior to a clinical-based model. These results demonstrate potential to identify nonresponders to chemotherapy and guide select patients toward earlier curative resection.
Collapse
Affiliation(s)
- Joshua M K Davis
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Muhammad Khalid Khan Niazi
- Center for Artificial Intelligence Research and the Clinical Image Analysis Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Ansley B Ricker
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Thomas E Tavolara
- Center for Artificial Intelligence Research and the Clinical Image Analysis Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Jordan N Robinson
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Bayram Annanurov
- Center for Artificial Intelligence Research and the Clinical Image Analysis Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Kaylee Smith
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Rohit Mantha
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Jimmy Hwang
- Department of Medical Oncology, Atrium Health Carolinas Medical Center, Levine Cancer Institute, Charlotte, North Carolina, USA
| | - Ruchi Shrestha
- Department of Radiology, Atrium Health, Charlotte, North Carolina, USA
| | - David A Iannitti
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - John B Martinie
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Erin H Baker
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Metin N Gurcan
- Center for Artificial Intelligence Research and the Clinical Image Analysis Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Dionisios Vrochides
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| |
Collapse
|
26
|
Meral G, Ateş S, Günay S, Öztürk A, Kuşdoğan M. Comparative analysis of ChatGPT, Gemini and emergency medicine specialist in ESI triage assessment. Am J Emerg Med 2024; 81:146-150. [PMID: 38728938 DOI: 10.1016/j.ajem.2024.05.001] [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: 03/11/2024] [Revised: 04/21/2024] [Accepted: 05/02/2024] [Indexed: 05/12/2024] Open
Abstract
INTRODUCTION The term Artificial Intelligence (AI) was first coined in the 1960s and has made significant progress up to the present day. During this period, numerous AI applications have been developed. GPT-4 and Gemini are two of the best-known of these AI models. As a triage system The Emergency Severity Index (ESI) is currently one of the most commonly used for effective patient triage in the emergency department. The aim of this study is to evaluate the performance of GPT-4, Gemini, and emergency medicine specialists in ESI triage against each other; furthermore, it aims to contribute to the literature on the usability of these AI programs in emergency department triage. METHODS Our study was conducted between February 1, 2024, and February 29, 2024, among emergency medicine specialists in Turkey, as well as with GPT-4 and Gemini. Ten emergency medicine specialists were included in our study but as a limitation the emergency medicine specialists participating in the study do not frequently use the ESI triage model in daily practice. In the first phase of our study, 100 case examples related to adult or trauma patients were extracted from the sample and training cases found in the ESI Implementation Handbook. In the second phase of our study, the provided responses were categorized into three groups: correct triage, over-triage, and under-triage. In the third phase of our study, the questions were categorized according to the correct triage responses. RESULTS In the results of our study, a statistically significant difference was found between the three groups in terms of correct triage, over-triage, and under-triage (p < 0.001). GPT-4 was found to have the highest correct triage rate with an average of 70.60 (±3.74), while Gemini had the highest over-triage rate with an average of 35.2 (±2.93) (p < 0.001). The highest under-triage rate was observed in emergency medicine specialists (32.90 (±11.83)). In the ESI 1-2 class, Gemini had a correct triage rate of 87.77%, GPT-4 had 85.11%, and emergency medicine specialists had 49.33%. CONCLUSION In conclusion, our study shows that both GPT-4 and Gemini can accurately triage critical and urgent patients in ESI 1&2 groups at a high rate. Furthermore, GPT-4 has been more successful in ESI triage for all patients. These results suggest that GPT-4 and Gemini could assist in accurate ESI triage of patients in emergency departments.
Collapse
Affiliation(s)
- Gürbüz Meral
- Department of Emergency Medicine, Specialist in Emergency Medicine, Hitit University Çorum Erol Olçok Education and Research Hospital, Çorum, Turkey.
| | - Serdal Ateş
- Department of Emergency Medicine, Specialist in Emergency Medicine, Hitit University Çorum Erol Olçok Education and Research Hospital, Çorum, Turkey
| | - Serkan Günay
- Department of Emergency Medicine, Specialist in Emergency Medicine, Hitit University Çorum Erol Olçok Education and Research Hospital, Çorum, Turkey
| | - Ahmet Öztürk
- Department of Emergency Medicine, Specialist in Emergency Medicine, Hitit University Çorum Erol Olçok Education and Research Hospital, Çorum, Turkey
| | - Mikail Kuşdoğan
- Department of Emergency Medicine, Specialist in Emergency Medicine, Hitit University Çorum Erol Olçok Education and Research Hospital, Çorum, Turkey
| |
Collapse
|
27
|
Feng X, Xu K, Luo MJ, Chen H, Yang Y, He Q, Song C, Li R, Wu Y, Wang H, Tham YC, Ting DSW, Lin H, Wong TY, Lam DSC. Latest developments of generative artificial intelligence and applications in ophthalmology. Asia Pac J Ophthalmol (Phila) 2024; 13:100090. [PMID: 39128549 DOI: 10.1016/j.apjo.2024.100090] [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: 06/08/2024] [Revised: 07/30/2024] [Accepted: 08/07/2024] [Indexed: 08/13/2024] Open
Abstract
The emergence of generative artificial intelligence (AI) has revolutionized various fields. In ophthalmology, generative AI has the potential to enhance efficiency, accuracy, personalization and innovation in clinical practice and medical research, through processing data, streamlining medical documentation, facilitating patient-doctor communication, aiding in clinical decision-making, and simulating clinical trials. This review focuses on the development and integration of generative AI models into clinical workflows and scientific research of ophthalmology. It outlines the need for development of a standard framework for comprehensive assessments, robust evidence, and exploration of the potential of multimodal capabilities and intelligent agents. Additionally, the review addresses the risks in AI model development and application in clinical service and research of ophthalmology, including data privacy, data bias, adaptation friction, over interdependence, and job replacement, based on which we summarized a risk management framework to mitigate these concerns. This review highlights the transformative potential of generative AI in enhancing patient care, improving operational efficiency in the clinical service and research in ophthalmology. It also advocates for a balanced approach to its adoption.
Collapse
Affiliation(s)
- Xiaoru Feng
- School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University, Beijing, China; Institute for Hospital Management, Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Kezheng Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Ming-Jie Luo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Haichao Chen
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Yangfan Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Qi He
- Research Centre of Big Data and Artificial Research for Medicine, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Chenxin Song
- Research Centre of Big Data and Artificial Research for Medicine, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Ruiyao Li
- Research Centre of Big Data and Artificial Research for Medicine, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - You Wu
- Institute for Hospital Management, Tsinghua Medicine, Tsinghua University, Beijing, China; School of Basic Medical Sciences, Tsinghua Medicine, Tsinghua University, Beijing, China; Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
| | - Haibo Wang
- Research Centre of Big Data and Artificial Research for Medicine, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
| | - Yih Chung Tham
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Daniel Shu Wei Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, Singapore; Byers Eye Institute, Stanford University, Palo Alto, CA, USA
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China; Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou, China
| | - Tien Yin Wong
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua Medicine, Tsinghua University, Beijing, China; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Dennis Shun-Chiu Lam
- The International Eye Research Institute, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China; The C-MER International Eye Care Group, Hong Kong, Hong Kong, China
| |
Collapse
|
28
|
González-Pérez Y, Montero Delgado A, Martinez Sesmero JM. Approaching artificial intelligence to Hospital Pharmacy. FARMACIA HOSPITALARIA 2024; 48 Suppl 1:S35-S44. [PMID: 39097366 DOI: 10.1016/j.farma.2024.02.007] [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: 09/14/2023] [Revised: 01/03/2024] [Accepted: 02/14/2024] [Indexed: 08/05/2024] Open
Abstract
Artificial intelligence (AI) is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. AI has been introduced in biomedicine, accelerating processes, improving safety and efficiency, and improving patient care. By using AI algorithms and Machine Learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. AI integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master AI will play a crucial role in this transformation.
Collapse
Affiliation(s)
- Yared González-Pérez
- Servicio de Farmacia, Hospital Universitario de Canarias, San Cristóbal de La Laguna, España.
| | - Alfredo Montero Delgado
- Servicio de Farmacia, Hospital Nuestra Señora de la Candelaria, Santa Cruz de Tenerife, España
| | | |
Collapse
|
29
|
Zhang J, Fang J, Xu Y, Si G. How AI and Robotics Will Advance Interventional Radiology: Narrative Review and Future Perspectives. Diagnostics (Basel) 2024; 14:1393. [PMID: 39001283 PMCID: PMC11241154 DOI: 10.3390/diagnostics14131393] [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: 04/28/2024] [Revised: 06/20/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024] Open
Abstract
The rapid advancement of artificial intelligence (AI) and robotics has led to significant progress in various medical fields including interventional radiology (IR). This review focuses on the research progress and applications of AI and robotics in IR, including deep learning (DL), machine learning (ML), and convolutional neural networks (CNNs) across specialties such as oncology, neurology, and cardiology, aiming to explore potential directions in future interventional treatments. To ensure the breadth and depth of this review, we implemented a systematic literature search strategy, selecting research published within the last five years. We conducted searches in databases such as PubMed and Google Scholar to find relevant literature. Special emphasis was placed on selecting large-scale studies to ensure the comprehensiveness and reliability of the results. This review summarizes the latest research directions and developments, ultimately analyzing their corresponding potential and limitations. It furnishes essential information and insights for researchers, clinicians, and policymakers, potentially propelling advancements and innovations within the domains of AI and IR. Finally, our findings indicate that although AI and robotics technologies are not yet widely applied in clinical settings, they are evolving across multiple aspects and are expected to significantly improve the processes and efficacy of interventional treatments.
Collapse
Affiliation(s)
- Jiaming Zhang
- Department of Radiology, Clinical Medical College, Southwest Medical University, Luzhou 646699, China; (J.Z.); (J.F.)
| | - Jiayi Fang
- Department of Radiology, Clinical Medical College, Southwest Medical University, Luzhou 646699, China; (J.Z.); (J.F.)
| | - Yanneng Xu
- Department of Radiology, Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou 646699, China;
| | - Guangyan Si
- Department of Radiology, Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou 646699, China;
| |
Collapse
|
30
|
Özcan F, Örücü Atar M, Köroğlu Ö, Yılmaz B. Assessment of the reliability and usability of ChatGPT in response to spinal cord injury questions. J Spinal Cord Med 2024:1-6. [PMID: 38860862 DOI: 10.1080/10790268.2024.2361551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
OBJECTIVE The use of artificial intelligence chatbots to obtain information about patients' diseases is increasing. This study aimed to determine the reliability and usability of ChatGPT for spinal cord injury-related questions. METHODS Three raters simultaneously evaluated a total of 47 questions on a 7-point Likert scale for reliability and usability, based on the three most frequently searched keywords in Google Trends ('general information', 'complications' and 'treatment'). RESULTS Inter-rater Cronbach α scores indicated substantial agreement for both reliability and usability scores (α between 0.558 and 0.839, and α between 0.373 and 0.772, respectively). The highest mean reliability score was for 'complications' (mean 5.38). The lowest average was for the 'general information' section (mean 4.20). The 'treatment' had the highest mean scores for the usability (mean 5.87) and the lowest mean value was recorded in the 'general information' section (mean 4.80). CONCLUSION The answers given by ChatGPT to questions related to spinal cord injury were reliable and useful. Nevertheless, it should be kept in mind that ChatGPT may provide incorrect or incomplete information, especially in the 'general information' section, which may mislead patients and their relatives.
Collapse
Affiliation(s)
- Fatma Özcan
- Department of Physical Medicine and Rehabilitation, University of Health Sciences, Gaziler Physical Medicine and Rehabilitation Training and Research Hospital, Ankara, Turkey
| | - Merve Örücü Atar
- Department of Physical Medicine and Rehabilitation, University of Health Sciences, Gaziler Physical Medicine and Rehabilitation Training and Research Hospital, Ankara, Turkey
| | - Özlem Köroğlu
- Department of Physical Medicine and Rehabilitation, University of Health Sciences, Gaziler Physical Medicine and Rehabilitation Training and Research Hospital, Ankara, Turkey
| | - Bilge Yılmaz
- Department of Physical Medicine and Rehabilitation, University of Health Sciences, Gaziler Physical Medicine and Rehabilitation Training and Research Hospital, Ankara, Turkey
| |
Collapse
|
31
|
Mickley JP, Kaji ES, Khosravi B, Mulford KL, Taunton MJ, Wyles CC. Overview of Artificial Intelligence Research Within Hip and Knee Arthroplasty. Arthroplast Today 2024; 27:101396. [PMID: 39071822 PMCID: PMC11282426 DOI: 10.1016/j.artd.2024.101396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 03/14/2024] [Accepted: 04/02/2024] [Indexed: 07/30/2024] Open
Abstract
Hip and knee arthroplasty are high-volume procedures undergoing rapid growth. The large volume of procedures generates a vast amount of data available for next-generation analytics. Techniques in the field of artificial intelligence (AI) can assist in large-scale pattern recognition and lead to clinical insights. AI methodologies have become more prevalent in orthopaedic research. This review will first describe an overview of AI in the medical field, followed by a description of the 3 arthroplasty research areas in which AI is commonly used (risk modeling, automated radiographic measurements, arthroplasty registry construction). Finally, we will discuss the next frontier of AI research focusing on model deployment and uncertainty quantification.
Collapse
Affiliation(s)
- John P. Mickley
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Elizabeth S. Kaji
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Bardia Khosravi
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Kellen L. Mulford
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Michael J. Taunton
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Cody C. Wyles
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
- Department of Clinical Anatomy, Mayo Clinic, Rochester, MN, USA
| |
Collapse
|
32
|
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.
Collapse
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
| |
Collapse
|
33
|
Zeng S, Qing Q, Xu W, Yu S, Zheng M, Tan H, Peng J, Huang J. Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors. Front Med (Lausanne) 2024; 11:1365524. [PMID: 38784235 PMCID: PMC11111965 DOI: 10.3389/fmed.2024.1365524] [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: 01/04/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024] Open
Abstract
Precision medicine, characterized by the personalized integration of a patient's genetic blueprint and clinical history, represents a dynamic paradigm in healthcare evolution. The emerging field of personalized anesthesia is at the intersection of genetics and anesthesiology, where anesthetic care will be tailored to an individual's genetic make-up, comorbidities and patient-specific factors. Genomics and biomarkers can provide more accurate anesthetic protocols, while artificial intelligence can simplify anesthetic procedures and reduce anesthetic risks, and real-time monitoring tools can improve perioperative safety and efficacy. The aim of this paper is to present and summarize the applications of these related fields in anesthesiology by reviewing them, exploring the potential of advanced technologies in the implementation and development of personalized anesthesia, realizing the future integration of new technologies into clinical practice, and promoting multidisciplinary collaboration between anesthesiology and disciplines such as genomics and artificial intelligence.
Collapse
Affiliation(s)
- Shiyue Zeng
- Zhuzhou Clinical College, Jishou University, Jishou, China
| | - Qi Qing
- Zhuzhou Clinical College, Jishou University, Jishou, China
| | - Wei Xu
- Department of Anesthesiology, Zhuzhou Central Hospital, Zhuzhou, China
| | - Simeng Yu
- Zhuzhou Clinical College, Jishou University, Jishou, China
| | - Mingzhi Zheng
- Department of Anesthesiology, Zhuzhou Central Hospital, Zhuzhou, China
| | - Hongpei Tan
- Department of Radiology, Third Xiangya Hospital, Central South University, Changsha, China
| | - Junmin Peng
- Department of Anesthesiology, Zhuzhou Central Hospital, Zhuzhou, China
| | - Jing Huang
- Department of Anesthesiology, Zhuzhou Central Hospital, Zhuzhou, China
| |
Collapse
|
34
|
Islam W, Abdoli N, Alam TE, Jones M, Mutembei BM, Yan F, Tang Q. A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients. Diagnostics (Basel) 2024; 14:954. [PMID: 38732368 PMCID: PMC11083029 DOI: 10.3390/diagnostics14090954] [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: 01/10/2024] [Revised: 04/26/2024] [Accepted: 04/28/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND At the time of cancer diagnosis, it is crucial to accurately classify malignant gastric tumors and the possibility that patients will survive. OBJECTIVE This study aims to investigate the feasibility of identifying and applying a new feature extraction technique to predict the survival of gastric cancer patients. METHODS A retrospective dataset including the computed tomography (CT) images of 135 patients was assembled. Among them, 68 patients survived longer than three years. Several sets of radiomics features were extracted and were incorporated into a machine learning model, and their classification performance was characterized. To improve the classification performance, we further extracted another 27 texture and roughness parameters with 2484 superficial and spatial features to propose a new feature pool. This new feature set was added into the machine learning model and its performance was analyzed. To determine the best model for our experiment, Random Forest (RF) classifier, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) (four of the most popular machine learning models) were utilized. The models were trained and tested using the five-fold cross-validation method. RESULTS Using the area under ROC curve (AUC) as an evaluation index, the model that was generated using the new feature pool yields AUC = 0.98 ± 0.01, which was significantly higher than the models created using the traditional radiomics feature set (p < 0.04). RF classifier performed better than the other machine learning models. CONCLUSIONS This study demonstrated that although radiomics features produced good classification performance, creating new feature sets significantly improved the model performance.
Collapse
Affiliation(s)
- Warid Islam
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (W.I.); (N.A.)
| | - Neman Abdoli
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (W.I.); (N.A.)
| | - Tasfiq E. Alam
- School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA;
| | - Meredith Jones
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.J.); (B.M.M.); (F.Y.)
| | - Bornface M. Mutembei
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.J.); (B.M.M.); (F.Y.)
| | - Feng Yan
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.J.); (B.M.M.); (F.Y.)
| | - Qinggong Tang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.J.); (B.M.M.); (F.Y.)
| |
Collapse
|
35
|
Tyler S, Olis M, Aust N, Patel L, Simon L, Triantafyllidis C, Patel V, Lee DW, Ginsberg B, Ahmad H, Jacobs RJ. Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review. Cureus 2024; 16:e59906. [PMID: 38854295 PMCID: PMC11158416 DOI: 10.7759/cureus.59906] [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: 04/10/2024] [Accepted: 05/08/2024] [Indexed: 06/11/2024] Open
Abstract
The integration of artificial intelligence (AI) and machine learning (ML) in healthcare has become a major point of interest and raises the question of its impact on the emergency department (ED) triaging process. AI's capacity to emulate human cognitive processes coupled with advancements in computing has shown positive outcomes in various aspects of healthcare but little is known about the use of AI in triaging patients in ED. AI algorithms may allow for earlier diagnosis and intervention; however, overconfident answers may present dangers to patients. The purpose of this review was to explore comprehensively recently published literature regarding the effect of AI and ML in ED triage and identify research gaps. A systemized search was conducted in September 2023 using the electronic databases EMBASE, Ovid MEDLINE, and Web of Science. To meet inclusion criteria, articles had to be peer-reviewed, written in English, and based on primary data research studies published in US journals 2013-2023. Other criteria included 1) studies with patients needing to be admitted to hospital EDs, 2) AI must have been used when triaging a patient, and 3) patient outcomes must be represented. The search was conducted using controlled descriptors from the Medical Subject Headings (MeSH) that included the terms "artificial intelligence" OR "machine learning" AND "emergency ward" OR "emergency care" OR "emergency department" OR "emergency room" AND "patient triage" OR "triage" OR "triaging." The search initially identified 1,142 citations. After a rigorous, systemized screening process and critical appraisal of the evidence, 29 studies were selected for the final review. The findings indicated that 1) ML models consistently demonstrated superior discrimination abilities compared to conventional triage systems, 2) the integration of AI into the triage process yielded significant enhancements in predictive accuracy, disease identification, and risk assessment, 3) ML accurately determined the necessity of hospitalization for patients requiring urgent attention, and 4) ML improved resource allocation and quality of patient care, including predicting length of stay. The suggested superiority of ML models in prioritizing patients in the ED holds the potential to redefine triage precision.
Collapse
Affiliation(s)
- Samantha Tyler
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Matthew Olis
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Nicole Aust
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Love Patel
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Leah Simon
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Catherine Triantafyllidis
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Vijay Patel
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Dong Won Lee
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Brendan Ginsberg
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Hiba Ahmad
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Robin J Jacobs
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| |
Collapse
|
36
|
Hirani R, Noruzi K, Khuram H, Hussaini AS, Aifuwa EI, Ely KE, Lewis JM, Gabr AE, Smiley A, Tiwari RK, Etienne M. Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities. Life (Basel) 2024; 14:557. [PMID: 38792579 PMCID: PMC11122160 DOI: 10.3390/life14050557] [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: 03/11/2024] [Revised: 04/22/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a powerful tool in healthcare significantly impacting practices from diagnostics to treatment delivery and patient management. This article examines the progress of AI in healthcare, starting from the field's inception in the 1960s to present-day innovative applications in areas such as precision medicine, robotic surgery, and drug development. In addition, the impact of the COVID-19 pandemic on the acceleration of the use of AI in technologies such as telemedicine and chatbots to enhance accessibility and improve medical education is also explored. Looking forward, the paper speculates on the promising future of AI in healthcare while critically addressing the ethical and societal considerations that accompany the integration of AI technologies. Furthermore, the potential to mitigate health disparities and the ethical implications surrounding data usage and patient privacy are discussed, emphasizing the need for evolving guidelines to govern AI's application in healthcare.
Collapse
Affiliation(s)
- Rahim Hirani
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
- Graduate School of Biomedical Sciences, New York Medical College, Valhalla, NY 10595, USA
| | - Kaleb Noruzi
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
| | - Hassan Khuram
- College of Medicine, Drexel University, Philadelphia, PA 19129, USA
| | - Anum S. Hussaini
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Esewi Iyobosa Aifuwa
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
| | - Kencie E. Ely
- Kirk Kerkorian School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89106, USA
| | - Joshua M. Lewis
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
| | - Ahmed E. Gabr
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
| | - Abbas Smiley
- School of Medicine and Dentistry, University of Rochester, Rochester, NY 14642, USA
| | - Raj K. Tiwari
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
- Graduate School of Biomedical Sciences, New York Medical College, Valhalla, NY 10595, USA
| | - Mill Etienne
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
- Department of Neurology, New York Medical College, Valhalla, NY 10595, USA
| |
Collapse
|
37
|
Barea Mendoza JA, Valiente Fernandez M, Pardo Fernandez A, Gómez Álvarez J. Current perspectives on the use of artificial intelligence in critical patient safety. Med Intensiva 2024:S2173-5727(24)00080-8. [PMID: 38677902 DOI: 10.1016/j.medine.2024.04.002] [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: 12/19/2023] [Accepted: 03/11/2024] [Indexed: 04/29/2024]
Abstract
Intensive Care Units (ICUs) have undergone enhancements in patient safety, and artificial intelligence (AI) emerges as a disruptive technology offering novel opportunities. While the published evidence is limited and presents methodological issues, certain areas show promise, such as decision support systems, detection of adverse events, and prescription error identification. The application of AI in safety may pursue predictive or diagnostic objectives. Implementing AI-based systems necessitates procedures to ensure secure assistance, addressing challenges including trust in such systems, biases, data quality, scalability, and ethical and confidentiality considerations. The development and application of AI demand thorough testing, encompassing retrospective data assessments, real-time validation with prospective cohorts, and efficacy demonstration in clinical trials. Algorithmic transparency and explainability are essential, with active involvement of clinical professionals being crucial in the implementation process.
Collapse
Affiliation(s)
- Jesús Abelardo Barea Mendoza
- UCI de Trauma y Emergencias. Servicio de Medicina Intensiva. Hospital Universitario 12 de Octubre. Instituto de Investigación Hospital 12 de Octubre, Spain.
| | - Marcos Valiente Fernandez
- UCI de Trauma y Emergencias. Servicio de Medicina Intensiva. Hospital Universitario 12 de Octubre. Instituto de Investigación Hospital 12 de Octubre, Spain
| | | | - Josep Gómez Álvarez
- Hospital Universitari de Tarragona Joan XXIII. Universitat Rovira i Virgili. Institut d'Investigació Sanitària Pere i Virgili, Tarragona, Spain
| |
Collapse
|
38
|
Bsisu I, Alqassieh R, Aloweidi A, Abu-Humdan A, Subuh A, Masarweh D. Attitudes of Jordanian Anesthesiologists and Anesthesia Residents towards Artificial Intelligence: A Cross-Sectional Study. J Pers Med 2024; 14:447. [PMID: 38793029 PMCID: PMC11121815 DOI: 10.3390/jpm14050447] [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: 03/02/2024] [Revised: 03/29/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Success in integrating artificial intelligence (AI) in anesthesia depends on collaboration with anesthesiologists, respecting their expertise, and understanding their opinions. The aim of this study was to illustrate the confidence in AI integration in perioperative anesthetic care among Jordanian anesthesiologists and anesthesia residents working at tertiary teaching hospitals. This cross-sectional study was conducted via self-administered online questionnaire and includes 118 responses from 44 anesthesiologists and 74 anesthesia residents. We used a five-point Likert scale to investigate the confidence in AI's role in different aspects of the perioperative period. A significant difference was found between anesthesiologists and anesthesia residents in confidence in the role of AI in operating room logistics and management, with an average score of 3.6 ± 1.3 among residents compared to 2.9 ± 1.4 among specialists (p = 0.012). The role of AI in event prediction under anesthesia scored 3.5 ± 1.4 among residents compared to 2.9 ± 1.4 among specialists (p = 0.032) and the role of AI in decision-making in anesthetic complications 3.3 ± 1.4 among residents and 2.8 ± 1.4 among specialists (p = 0.034). Also, 65 (55.1%) were concerned that the integration of AI will lead to less human-human interaction, while 81 (68.6%) believed that AI-based technology will lead to more adherence to guidelines. In conclusion, AI has the potential to be a revolutionary tool in anesthesia, and hesitancy towards increased dependency on this technology is decreasing with newer generations of practitioners.
Collapse
Affiliation(s)
- Isam Bsisu
- Department of Anesthesia and Intensive Care, School of Medicine, The University of Jordan, Amman 11942, Jordan; (A.A.); (A.A.-H.); (D.M.)
- UCSF Center for Health Equity in Surgery and Anesthesia, San Francisco, CA 94158, USA
- Department of Anesthesia and Intensive Care, Arab Medical Center, Amman 11181, Jordan
| | - Rami Alqassieh
- Department of General Surgery and Anesthesia and Urology, Faculty of Medicine, The Hashemite University, Zarqa 13133, Jordan;
| | - Abdelkarim Aloweidi
- Department of Anesthesia and Intensive Care, School of Medicine, The University of Jordan, Amman 11942, Jordan; (A.A.); (A.A.-H.); (D.M.)
| | - Abdulrahman Abu-Humdan
- Department of Anesthesia and Intensive Care, School of Medicine, The University of Jordan, Amman 11942, Jordan; (A.A.); (A.A.-H.); (D.M.)
| | - Aseel Subuh
- Department of Internal Medicine, School of Medicine, The University of Jordan, Amman 11942, Jordan;
| | - Deema Masarweh
- Department of Anesthesia and Intensive Care, School of Medicine, The University of Jordan, Amman 11942, Jordan; (A.A.); (A.A.-H.); (D.M.)
| |
Collapse
|
39
|
Yuan L, An L, Zhu Y, Duan C, Kong W, Jiang P, Yu QQ. Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT. Cancer Manag Res 2024; 16:361-375. [PMID: 38699652 PMCID: PMC11063459 DOI: 10.2147/cmar.s451871] [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: 11/29/2023] [Accepted: 04/16/2024] [Indexed: 05/05/2024] Open
Abstract
As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.
Collapse
Affiliation(s)
- Lili Yuan
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Lin An
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Yandong Zhu
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Chongling Duan
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Weixiang Kong
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Pei Jiang
- Translational Pharmaceutical Laboratory, Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Qing-Qing Yu
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| |
Collapse
|
40
|
Katwaroo AR, Adesh VS, Lowtan A, Umakanthan S. The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine. Postgrad Med J 2024; 100:289-296. [PMID: 38159301 DOI: 10.1093/postmj/qgad135] [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: 10/27/2023] [Accepted: 12/02/2023] [Indexed: 01/03/2024]
Abstract
In the evolution of modern medicine, artificial intelligence (AI) has been proven to provide an integral aspect of revolutionizing clinical diagnosis, drug discovery, and patient care. With the potential to scrutinize colossal amounts of medical data, radiological and histological images, and genomic data in healthcare institutions, AI-powered systems can recognize, determine, and associate patterns and provide impactful insights that would be strenuous and challenging for clinicians to detect during their daily clinical practice. The outcome of AI-mediated search offers more accurate, personalized patient diagnoses, guides in research for new drug therapies, and provides a more effective multidisciplinary treatment plan that can be implemented for patients with chronic diseases. Among the many promising applications of AI in modern medicine, medical imaging stands out distinctly as an area with tremendous potential. AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity. This allows for earlier diagnosis and treatment, which can significantly impact patient outcomes. This review provides a comprehensive insight into diagnostic, therapeutic, and ethical issues with the advent of AI in modern medicine.
Collapse
Affiliation(s)
- Arun Rabindra Katwaroo
- Department of Medicine, Trinidad Institute of Medical Technology, St Augustine, Trinidad and Tobago
| | | | - Amrita Lowtan
- Department of Preclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St. Augustine, Trinidad and Tobago
| | - Srikanth Umakanthan
- Department of Paraclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St. Augustine, Trinidad and Tobago
| |
Collapse
|
41
|
Huguet N, Chen J, Parikh RB, Marino M, Flocke SA, Likumahuwa-Ackman S, Bekelman J, DeVoe JE. Applying Machine Learning Techniques to Implementation Science. Online J Public Health Inform 2024; 16:e50201. [PMID: 38648094 DOI: 10.2196/50201] [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: 06/22/2023] [Revised: 11/15/2023] [Accepted: 03/14/2024] [Indexed: 04/25/2024] Open
Abstract
Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.
Collapse
Affiliation(s)
- Nathalie Huguet
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Jinying Chen
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Data Science Core, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- iDAPT Implementation Science Center for Cancer Control, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Ravi B Parikh
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Miguel Marino
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Susan A Flocke
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Sonja Likumahuwa-Ackman
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Justin Bekelman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, United States
| | - Jennifer E DeVoe
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| |
Collapse
|
42
|
Zemelka-Wiacek M, Agache I, Akdis CA, Akdis M, Casale TB, Dramburg S, Jahnz-Różyk K, Kosowska A, Matricardi PM, Pfaar O, Shamji MH, Jutel M. Hot topics in allergen immunotherapy, 2023: Current status and future perspective. Allergy 2024; 79:823-842. [PMID: 37984449 DOI: 10.1111/all.15945] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/10/2023] [Accepted: 11/04/2023] [Indexed: 11/22/2023]
Abstract
The importance of allergen immunotherapy (AIT) is multifaceted, encompassing both clinical and quality-of-life improvements and cost-effectiveness in the long term. Key mechanisms of allergen tolerance induced by AIT include changes in memory type allergen-specific T- and B-cell responses towards a regulatory phenotype with decreased Type 2 responses, suppression of allergen-specific IgE and increased IgG1 and IgG4, decreased mast cell and eosinophil numbers in allergic tissues and increased activation thresholds. The potential of novel patient enrolment strategies for AIT is taking into account recent advances in biomarkers discoveries, molecular allergy diagnostics and mobile health applications contributing to a personalized approach enhancement that can increase AIT efficacy and compliance. Artificial intelligence can help manage and interpret complex and heterogeneous data, including big data from omics and non-omics research, potentially predict disease subtypes, identify biomarkers and monitor patient responses to AIT. Novel AIT preparations, such as synthetic compounds, innovative carrier systems and adjuvants, are also of great promise. Advances in clinical trial models, including adaptive, complex and hybrid designs as well as real-world evidence, allow more flexibility and cost reduction. The analyses of AIT cost-effectiveness show a clear long-term advantage compared to pharmacotherapy. Important research questions, such as defining clinical endpoints, biomarkers of patient selection and efficacy, mechanisms and the modulation of the placebo effect and alternatives to conventional field trials, including allergen exposure chamber studies are still to be elucidated. This review demonstrates that AIT is still in its growth phase and shows immense development prospects.
Collapse
Affiliation(s)
| | - Ioana Agache
- Faculty of Medicine, Transylvania University, Brasov, Romania
| | - Cezmi A Akdis
- Swiss Institute of Allergy and Asthma Research (SIAF), University Zurich, Davos, Switzerland
| | - Mübeccel Akdis
- Swiss Institute of Allergy and Asthma Research (SIAF), University Zurich, Davos, Switzerland
| | - Thomas B Casale
- Departments of Medicine and Pediatrics and Division of Allergy and Immunology, Joy McCann Culverhouse Clinical Research Center, University of South Florida, Tampa, Florida, USA
| | - Stephanie Dramburg
- Department of Pediatric Respiratory Care, Immunology and Critical Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Karina Jahnz-Różyk
- Department of Internal Diseases, Pneumonology, Allergology and Clinical Immunology, Military Institute of Medicine-National Research Institute, Warsaw, Poland
| | - Anna Kosowska
- Department of Clinical Immunology, Wroclaw Medical University, Wroclaw, Poland
- ALL-MED Medical Research Institute, Wroclaw, Poland
| | - Paolo M Matricardi
- Department of Pediatric Respiratory Care, Immunology and Critical Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Oliver Pfaar
- Section of Rhinology and Allergy, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Marburg, Philipps-Universität Marburg, Marburg, Germany
| | - Mohamed H Shamji
- Allergy and Clinical Immunology, National Heart and Lung Institute, Imperial College London, London, UK
| | - Marek Jutel
- Department of Clinical Immunology, Wroclaw Medical University, Wroclaw, Poland
- ALL-MED Medical Research Institute, Wroclaw, Poland
| |
Collapse
|
43
|
Huang HYR, Wireko AA, Miteu GD, Khan A, Roy S, Ferreira T, Garg T, Aji N, Haroon F, Zakariya F, Alshareefy Y, Pujari AG, Madani D, Papadakis M. Advancements and progress in juvenile idiopathic arthritis: A Review of pathophysiology and treatment. Medicine (Baltimore) 2024; 103:e37567. [PMID: 38552102 PMCID: PMC10977530 DOI: 10.1097/md.0000000000037567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 02/20/2024] [Indexed: 04/02/2024] Open
Abstract
Juvenile idiopathic arthritis (JIA) is a chronic clinical condition characterized by arthritic features in children under the age of 16, with at least 6 weeks of active symptoms. The etiology of JIA remains unknown, and it is associated with prolonged synovial inflammation and structural joint damage influenced by environmental and genetic factors. This review aims to enhance the understanding of JIA by comprehensively analyzing relevant literature. The focus lies on current diagnostic and therapeutic approaches and investigations into the pathoaetiologies using diverse research modalities, including in vivo animal models and large-scale genome-wide studies. We aim to elucidate the multifactorial nature of JIA with a strong focus towards genetic predilection, while proposing potential strategies to improve therapeutic outcomes and enhance diagnostic risk stratification in light of recent advancements. This review underscores the need for further research due to the idiopathic nature of JIA, its heterogeneous phenotype, and the challenges associated with biomarkers and diagnostic criteria. Ultimately, this contribution seeks to advance the knowledge and promote effective management strategies in JIA.
Collapse
Affiliation(s)
- Helen Ye Rim Huang
- Faculty of Medicine and Health Science, Royal College of Surgeons in Ireland, Dublin, Ireland
| | | | - Goshen David Miteu
- School of Biosciences, Biotechnology, University of Nottingham, Nottingham, UK
- Department of Biochemistry, Caleb University Lagos, Lagos, Nigeria
| | - Adan Khan
- Kent and Medway Medical School, Canterbury, Kent, UK
| | - Sakshi Roy
- School of Medicine, Queen’s University Belfast, Belfast, Northern Ireland, UK
| | - Tomas Ferreira
- School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Tulika Garg
- Government Medical College and Hospital Chandigarh, Chandigarh, India
| | - Narjiss Aji
- Faculty of Medicine and Pharmacy of Rabat, Rabat, Morocco
| | - Faaraea Haroon
- Faculty of Public Health, Health Services Academy, Islamabad, Pakistan
| | - Farida Zakariya
- Faculty of Pharmaceutical Sciences, Ahmadu Bello University Zaria, Zaria, Nigeria
| | - Yasir Alshareefy
- School of Medicine, Trinity College Dublin, The University of Dublin, Dublin, Ireland
| | - Anushka Gurunath Pujari
- Faculty of Medicine and Health Science, Royal College of Surgeons in Ireland, Dublin, Ireland
- Department of Kinesiology, Faculty of Science, McMaster University, Hamilton, Ontario, Canada
| | - Djabir Madani
- UCD Lochlann Quinn School of Business and Sutherland School of Law, University College Dublin, Dublin, Ireland
| | - Marios Papadakis
- Department of Surgery II, University Hospital Witten-Herdecke, University of Witten-Herdecke, Wuppertal, Germany
| |
Collapse
|
44
|
Huffman N, Pasqualini I, Khan ST, Klika AK, Deren ME, Jin Y, Kunze KN, Piuzzi NS. Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review. JBJS Rev 2024; 12:01874474-202403000-00006. [PMID: 38466797 DOI: 10.2106/jbjs.rvw.23.00232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.
Collapse
Affiliation(s)
- Nickelas Huffman
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | | | - Shujaa T Khan
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Alison K Klika
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Matthew E Deren
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Yuxuan Jin
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York
| | - Nicolas S Piuzzi
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
- Department of Biomedical Engineering, Cleveland Clinic Foundation, Cleveland, Ohio
| |
Collapse
|
45
|
Butt SR, Soulat A, Lal PM, Fakhor H, Patel SK, Ali MB, Arwani S, Mohan A, Majumder K, Kumar V, Tejwaney U, Kumar S. Impact of artificial intelligence on the diagnosis, treatment and prognosis of endometrial cancer. Ann Med Surg (Lond) 2024; 86:1531-1539. [PMID: 38463097 PMCID: PMC10923372 DOI: 10.1097/ms9.0000000000001733] [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: 06/26/2023] [Accepted: 01/08/2024] [Indexed: 03/12/2024] Open
Abstract
Endometrial cancer is one of the most prevalent tumours in females and holds an 83% survival rate within 5 years of diagnosis. Hypoestrogenism is a major risk factor for the development of endometrial carcinoma (EC) therefore two major types are derived, type 1 being oestrogen-dependent and type 2 being oestrogen independent. Surgery, chemotherapeutic drugs, and radiation therapy are only a few of the treatment options for EC. Treatment of gynaecologic malignancies greatly depends on diagnosis or prognostic prediction. Diagnostic imaging data and clinical course prediction are the two core pillars of artificial intelligence (AI) applications. One of the most popular imaging techniques for spotting preoperative endometrial cancer is MRI, although this technique can only produce qualitative data. When used to classify patients, AI improves the effectiveness of visual feature extraction. In general, AI has the potential to enhance the precision and effectiveness of endometrial cancer diagnosis and therapy. This review aims to highlight the current status of applications of AI in endometrial cancer and provide a comprehensive understanding of how recent advancements in AI have assisted clinicians in making better diagnosis and improving prognosis of endometrial cancer. Still, additional study is required to comprehend its strengths and limits fully.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Anmol Mohan
- Karachi Medical and Dental College, Karachi, Pakistan
| | | | | | | | | |
Collapse
|
46
|
Dong R, Liu Y, Zhang Y, Chen Y. Enhanced morphological assessment based on interocular asymmetry analysis for keratoconus detection. Graefes Arch Clin Exp Ophthalmol 2024; 262:913-926. [PMID: 37792068 DOI: 10.1007/s00417-023-06250-7] [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: 05/15/2023] [Revised: 08/09/2023] [Accepted: 09/19/2023] [Indexed: 10/05/2023] Open
Abstract
PURPOSE To clarify the interocular asymmetry of corneal morphological descriptors and evaluate its discriminant ability of keratoconus (KC). METHODS This retrospective study recruited 344 normal participants and 290 KC patients, randomized to training and validation datasets. Interocular correlation and agreement were evaluated on 44 corneal morphological descriptors derived from Schiempflug tomography. Logistic regression models were constructed using binocular data and of which diagnostic performance was evaluated using the area under receiver operating characteristics curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). RESULTS Interocular agreement of corneal descriptors is better in the normal than in KC except for dimensions of cornea and anterior chamber. The interocular asymmetry increases along with the severity of KC. Interocular asymmetry in maximum anterior keratometry, mean anterior keratometry and higher-order aberrations of anterior surface show high AUC above 0.950. Binocular logistic regression index reaches an AUC of 0.963 with high specificity (95.2%) and brings gain to monocular parameters in distinguishing the normal eyes from KC (NRI = 0.080 (0.042 ~ 0.118), P < 0.001) and IDI = 0.071 (0.049 ~ 0.092), P < 0.001). Interocular asymmetry benefits even more in subclinical keratoconus (SKC) detection reflected by NRI (0.4784 (0.2703-0.6865), P < 0.001) and IDI (0.2680 (0.1495-0.3866), P < 0.001) measures. CONCLUSION Interocular asymmetry is a well-characterized feature of KC and related to the severity. It is feasible to apply the interocular asymmetry in diagnosis of KC and SKC as a replenishment of monocular parameters and in progression tracking.
Collapse
Affiliation(s)
- Ruilan Dong
- Department of Ophthalmology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Peking University Third Hospital, Beijing, China
- Peking University Institute of Laser Medicine, Beijing, China
| | - Yan Liu
- Department of Ophthalmology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Peking University Third Hospital, Beijing, China
- Peking University Institute of Laser Medicine, Beijing, China
| | - Yu Zhang
- Department of Ophthalmology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Peking University Third Hospital, Beijing, China
- Peking University Institute of Laser Medicine, Beijing, China
| | - Yueguo Chen
- Department of Ophthalmology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China.
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Peking University Third Hospital, Beijing, China.
- Peking University Institute of Laser Medicine, Beijing, China.
| |
Collapse
|
47
|
Lu Z, Morita M, Yeager TS, Lyu Y, Wang SY, Wang Z, Fan G. Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders. Diagnostics (Basel) 2024; 14:420. [PMID: 38396459 PMCID: PMC10888253 DOI: 10.3390/diagnostics14040420] [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: 01/22/2024] [Revised: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open
Abstract
Flow cytometry is a vital diagnostic tool for hematologic and immunologic disorders, but manual analysis is prone to variation and time-consuming. Over the last decade, artificial intelligence (AI) has advanced significantly. In this study, we developed and validated an AI-assisted flow cytometry workflow using 379 clinical cases from 2021, employing a 3-tube, 10-color flow panel with 21 antibodies for primary immunodeficiency diseases and related immunological disorders. The AI software (DeepFlow™, version 2.1.1) is fully automated, reducing analysis time to under 5 min per case. It interacts with hematopatholoists for manual gating adjustments when necessary. Using proprietary multidimensional density-phenotype coupling algorithm, the AI model accurately classifies and enumerates T, B, and NK cells, along with important immune cell subsets, including CD4+ helper T cells, CD8+ cytotoxic T cells, CD3+/CD4-/CD8- double-negative T cells, and class-switched or non-switched B cells. Compared to manual analysis with hematopathologist-determined lymphocyte subset percentages as the gold standard, the AI model exhibited a strong correlation (r > 0.9) across lymphocyte subsets. This study highlights the accuracy and efficiency of AI-assisted flow cytometry in diagnosing immunological disorders in a clinical setting, providing a transformative approach within a concise timeframe.
Collapse
Affiliation(s)
- Zhengchun Lu
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | - Mayu Morita
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | - Tyler S. Yeager
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | - Yunpeng Lyu
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | - Sophia Y. Wang
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | | | - Guang Fan
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| |
Collapse
|
48
|
Boonstra MJ, Weissenbacher D, Moore JH, Gonzalez-Hernandez G, Asselbergs FW. Artificial intelligence: revolutionizing cardiology with large language models. Eur Heart J 2024; 45:332-345. [PMID: 38170821 PMCID: PMC10834163 DOI: 10.1093/eurheartj/ehad838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 12/01/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
Natural language processing techniques are having an increasing impact on clinical care from patient, clinician, administrator, and research perspective. Among others are automated generation of clinical notes and discharge letters, medical term coding for billing, medical chatbots both for patients and clinicians, data enrichment in the identification of disease symptoms or diagnosis, cohort selection for clinical trial, and auditing purposes. In the review, an overview of the history in natural language processing techniques developed with brief technical background is presented. Subsequently, the review will discuss implementation strategies of natural language processing tools, thereby specifically focusing on large language models, and conclude with future opportunities in the application of such techniques in the field of cardiology.
Collapse
Affiliation(s)
- Machteld J Boonstra
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
| | - Davy Weissenbacher
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Folkert W Asselbergs
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| |
Collapse
|
49
|
Kenig N, Monton Echeverria J, De la Ossa L. Identification of Key Breast Features Using a Neural Network: Applications of Machine Learning in the Clinical Setting of Plastic Surgery. Plast Reconstr Surg 2024; 153:273e-280e. [PMID: 37104483 DOI: 10.1097/prs.0000000000010603] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
BACKGROUND In plastic surgery, evaluation of breast symmetry is an important aspect of clinical practice. Computer programs have been developed for this purpose, but most of them require operator input. Artificial intelligence has been introduced into many aspects of medicine. In plastic surgery, automated neural networks for breast evaluation could improve quality of care. In this work, the authors evaluate the identification of breast features with an ad hoc trained neural network. METHODS An ad hoc convolutional neural network was developed on the YOLOV3 platform to detect key features of the breast that are commonly used in plastic surgery for symmetry evaluation. The program was trained with 200 frontal photographs of patients who underwent breast surgery and was tested on 47 frontal images of patients who underwent breast reconstruction after breast cancer surgery. RESULTS The program was able to detect key features in 97.74% of cases (boundaries of the breast in 94 of 94 cases, the nipple-areola complex in 94 of 94 cases, and the suprasternal notch in 41 of 47 cases). Mean time of detection was 0.52 seconds. CONCLUSIONS The ad hoc neural network was successful in localizing key breast features, with a total detection rate of 97.74%. Neural networks and machine learning have the potential to improve the evaluation of breast symmetry in plastic surgery by automated and quick detection of features used by surgeons in practice. More studies and development are needed to further knowledge in this area.
Collapse
Affiliation(s)
- Nitzan Kenig
- From the Department of Plastic Surgery, Albacete University Hospital
| | | | - Luis De la Ossa
- Department of Computer Engineering, University of Castilla-La Mancha
| |
Collapse
|
50
|
Zarei P, Ghasemi F. The Application of Artificial Intelligence and Drug Repositioning for the Identification of Fibroblast Growth Factor Receptor Inhibitors: A Review. Adv Biomed Res 2024; 13:9. [PMID: 38525398 PMCID: PMC10958741 DOI: 10.4103/abr.abr_170_23] [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: 05/18/2023] [Revised: 08/24/2023] [Accepted: 09/03/2023] [Indexed: 03/26/2024] Open
Abstract
Artificial intelligence talks about modeling intelligent behavior through a computer with the least human involvement. Drug repositioning techniques based on artificial intelligence accelerate the research process and decrease the cost of experimental studies. Dysregulation of fibroblast growth factor (FGF) receptors as the tyrosine kinase family of receptors plays a vital role in a wide range of malignancies. Because of their functional significance, they were considered promising drug targets for the therapy of various cancers. This review has summarized small molecules capable of inhibiting FGF receptors that progressed using artificial intelligence and repositioning drugs examined in clinical trials associated with cancer therapy. This review is based on a literature search in PubMed, Web of Science, Scopus EMBASE, and Google Scholar databases to gather the necessary information in each chapter by employing keywords like artificial intelligence, computational drug design, drug repositioning, and FGF receptor inhibitors. To achieve this goal, a spacious literature review of human studies in these fields-published over the last 20 decades-was performed. According to published reports, nonselective FGF receptor inhibitors can be used for cancer management, and multitarget kinase inhibitors are the first drug class approved due to more advanced clinical studies. For example, AZD4547 and BGJ398 are gradually entering the consumption cycle and are good options as combined treatments. Artificial intelligence and drug repositioning methods can help preselect suitable drug targets more successfully for future inhibition of carcinogenicity.
Collapse
Affiliation(s)
- Parvin Zarei
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Fahimeh Ghasemi
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| |
Collapse
|