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Cogno N, Axenie C, Bauer R, Vavourakis V. Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation. Cancer Biol Ther 2024; 25:2344600. [PMID: 38678381 PMCID: PMC11057625 DOI: 10.1080/15384047.2024.2344600] [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: 10/30/2023] [Accepted: 04/15/2024] [Indexed: 04/29/2024] Open
Abstract
Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.
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Affiliation(s)
- Nicolò Cogno
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Institute for Condensed Matter Physics, Technische Universit¨at Darmstadt, Darmstadt, Germany
| | - Cristian Axenie
- Computer Science Department and Center for Artificial Intelligence, Technische Hochschule Nürnberg Georg Simon Ohm, Nuremberg, Germany
| | - Roman Bauer
- Nature Inspired Computing and Engineering Research Group, Computer Science Research Centre, University of Surrey, Guildford, UK
| | - Vasileios Vavourakis
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
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2
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Cassee FR, Bleeker EA, Durand C, Exner T, Falk A, Friedrichs S, Heunisch E, Himly M, Hofer S, Hofstätter N, Hristozov D, Nymark P, Pohl A, Soeteman-Hernández LG, Suarez-Merino B, Valsami-Jones E, Groenewold M. Roadmap towards safe and sustainable advanced and innovative materials. (Outlook for 2024-2030). Comput Struct Biotechnol J 2024; 25:105-126. [PMID: 38974014 PMCID: PMC11225617 DOI: 10.1016/j.csbj.2024.05.018] [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: 04/24/2024] [Revised: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 07/09/2024] Open
Abstract
The adoption of innovative advanced materials holds vast potential, contingent upon addressing safety and sustainability concerns. The European Commission advocates the integration of Safe and Sustainable by Design (SSbD) principles early in the innovation process to streamline market introduction and mitigate costs. Within this framework, encompassing ecological, social, and economic factors is paramount. The NanoSafety Cluster (NSC) delineates key safety and sustainability areas, pinpointing unresolved issues and research gaps to steer the development of safe(r) materials. Leveraging FAIR data management and integration, alongside the alignment of regulatory aspects, fosters informed decision-making and innovation. Integrating circularity and sustainability mandates clear guidance, ensuring responsible innovation at every stage. Collaboration among stakeholders, anticipation of regulatory demands, and a commitment to sustainability are pivotal for translating SSbD into tangible advancements. Harmonizing standards and test guidelines, along with regulatory preparedness through an exchange platform, is imperative for governance and market readiness. By adhering to these principles, the effective and sustainable deployment of innovative materials can be realized, propelling positive transformation and societal acceptance.
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Affiliation(s)
- Flemming R. Cassee
- National Institute of Public Health and the Environment (RIVM), the Netherlands & Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Eric A.J. Bleeker
- National Institute of Public Health and the Environment (RIVM), the Netherlands
| | | | | | - Andreas Falk
- BioNanoNet Forschungsgesellschaft mbH (BNN), Austria
| | | | | | | | | | | | | | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institute, Solna, Sweden
| | - Anna Pohl
- Federal Institute for Occupational Safety and Health (BAuA), Germany
| | | | | | - Eugenia Valsami-Jones
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Monique Groenewold
- National Institute of Public Health and the Environment (RIVM), the Netherlands
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3
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Rahman ABS, Ta HT, Najjar L, Azadmanesh A, Gönul AS. DepressionEmo: A novel dataset for multilabel classification of depression emotions. J Affect Disord 2024; 366:445-458. [PMID: 39214375 DOI: 10.1016/j.jad.2024.08.013] [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: 01/03/2024] [Revised: 07/04/2024] [Accepted: 08/09/2024] [Indexed: 09/04/2024]
Abstract
Emotions are integral to human social interactions, with diverse responses elicited by various situational contexts. Particularly, the prevalence of negative emotional states has been correlated with negative outcomes for mental health, necessitating a comprehensive analysis of their occurrence and impact on individuals. In this paper, we introduce a novel dataset named DepressionEmo designed to detect 8 emotions associated with depression by 6037 examples of long Reddit user posts. This dataset was created through a majority vote over inputs by zero-shot classifications from pre-trained models and validating the quality by annotators and ChatGPT, exhibiting an acceptable level of inter-rater reliability between annotators. The correlation between emotions, and linguistic analysis are conducted on DepressionEmo. Besides, we provide several text classification methods classified into two groups: machine learning methods such as SVM, XGBoost, and LightGBM; and deep learning methods such as BERT, BART, GAN-BERT, and T5. Despite achieving the same F1 Macro score of 0.76 as BART, the pretrained BERT model, bert-base-uncased, stands out as the most efficient model in our experiments due to its lower number of parameters. Across all emotions, the highest F1 Macro value is achieved by suicide intent, indicating a certain value of our dataset in identifying emotions in individuals with depression symptoms through text analysis. The curated dataset is publicly available at: https://github.com/abuBakarSiddiqurRahman/DepressionEmo.
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Affiliation(s)
| | - Hoang-Thang Ta
- Department of Information Technology, Dalat University, Da Lat, Lam Dong, Vietnam.
| | - Lotfollah Najjar
- College of Information Science and Technology, University of Nebraska Omaha, USA.
| | - Azad Azadmanesh
- College of Information Science and Technology, University of Nebraska Omaha, USA.
| | - Ali Saffet Gönul
- Department of Psychiatry, Ege University, Bornova, Izmir, Turkey.
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4
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Tafavvoghi M, Bongo LA, Shvetsov N, Busund LTR, Møllersen K. Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review. J Pathol Inform 2024; 15:100363. [PMID: 38405160 PMCID: PMC10884505 DOI: 10.1016/j.jpi.2024.100363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/24/2023] [Accepted: 01/23/2024] [Indexed: 02/27/2024] Open
Abstract
Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and robust deep learning models. In this scoping review, we identified the publicly available datasets of breast H&E-stained whole-slide images (WSIs) that can be used to develop deep learning algorithms. We systematically searched 9 scientific literature databases and 9 research data repositories and found 17 publicly available datasets containing 10 385 H&E WSIs of breast cancer. Moreover, we reported image metadata and characteristics for each dataset to assist researchers in selecting proper datasets for specific tasks in breast cancer computational pathology. In addition, we compiled 2 lists of breast H&E patches and private datasets as supplementary resources for researchers. Notably, only 28% of the included articles utilized multiple datasets, and only 14% used an external validation set, suggesting that the performance of other developed models may be susceptible to overestimation. The TCGA-BRCA was used in 52% of the selected studies. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms. There is also a lack of consistent metadata reporting of breast WSI datasets that can be an issue in developing accurate deep learning models, indicating the necessity of establishing explicit guidelines for documenting breast WSI dataset characteristics and metadata.
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Affiliation(s)
- Masoud Tafavvoghi
- Department of Community Medicine, Uit The Arctic University of Norway, Tromsø, Norway
| | - Lars Ailo Bongo
- Department of Computer Science, Uit The Arctic University of Norway, Tromsø, Norway
| | - Nikita Shvetsov
- Department of Computer Science, Uit The Arctic University of Norway, Tromsø, Norway
| | | | - Kajsa Møllersen
- Department of Community Medicine, Uit The Arctic University of Norway, Tromsø, Norway
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5
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Kalejaye L, Wu IE, Terry T, Lai PK. DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability. Comput Struct Biotechnol J 2024; 23:2220-2229. [PMID: 38827232 PMCID: PMC11140563 DOI: 10.1016/j.csbj.2024.05.029] [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: 03/02/2024] [Revised: 05/15/2024] [Accepted: 05/16/2024] [Indexed: 06/04/2024] Open
Abstract
Therapeutic antibody development faces challenges due to high viscosities and aggregation tendencies. The spatial charge map (SCM) and spatial aggregation propensity (SAP) are computational techniques that aid in predicting viscosity and aggregation, respectively. These methods rely on structural data derived from molecular dynamics (MD) simulations, which are computationally demanding. DeepSCM, a deep learning surrogate model based on sequence information to predict SCM, was recently developed to screen high-concentration antibody viscosity. This study further utilized a dataset of 20,530 antibody sequences to train a convolutional neural network deep learning surrogate model called Deep Spatial Properties (DeepSP). DeepSP directly predicts SAP and SCM scores in different domains of antibody variable regions based solely on their sequences without performing MD simulations. The linear correlation coefficient between DeepSP scores and MD-derived scores for 30 properties achieved values between 0.76 and 0.96 with an average of 0.87. DeepSP descriptors were employed as features to build machine learning models to predict the aggregation rate of 21 antibodies, and the performance is similar to the results obtained from the previous study using MD simulations. This result demonstrates that the DeepSP approach significantly reduces the computational time required compared to MD simulations. The DeepSP model enables the rapid generation of 30 structural properties that can also be used as features in other research to train machine learning models for predicting various antibody stability using sequences only. DeepSP is freely available as an online tool via https://deepspwebapp.onrender.com and the codes and parameters are freely available at https://github.com/Lailabcode/DeepSP.
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Affiliation(s)
- Lateefat Kalejaye
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken 07030, NJ, United States
| | - I-En Wu
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken 07030, NJ, United States
| | - Taylor Terry
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken 07030, NJ, United States
| | - Pin-Kuang Lai
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken 07030, NJ, United States
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Tian Tran J, Burghall A, Blydt-Hansen T, Cammer A, Goldberg A, Hamiwka L, Johnson C, Kehler C, Phan V, Rosaasen N, Ruhl M, Strong J, Teoh CW, Wichart J, Mansell H. Exploring the ability of ChatGPT to create quality patient education resources about kidney transplant. PATIENT EDUCATION AND COUNSELING 2024; 129:108400. [PMID: 39232336 DOI: 10.1016/j.pec.2024.108400] [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: 03/06/2024] [Revised: 07/08/2024] [Accepted: 08/08/2024] [Indexed: 09/06/2024]
Abstract
BACKGROUND Chat Generative Pre-trained Transformer (ChatGPT) is a language model that may have the potential to revolutionize health care. The study purpose was to test whether ChatGPT could be used to create educational brochures about kidney transplant tailored for three target audiences: caregivers, teens and children. METHODS Using a list of 25 educational topics, standardized prompts were employed to ensure content consistency in ChatGPT generation. An expert panel assessed the accuracy of the content by rating agreement on a Likert scale (1 = <25 % agreement; and 5 = 100 % agreement). The understandability, actionability and readability of the brochures were assessed using the Patient Education Materials Assessment Tool for printable materials (PEMAT-P) and standard readability scales. A caregiver and patient reviewed and provided written feedback. RESULTS We found mean understandability scores of 69 %, 66 %, and 73 % for caregiver, teen, and child brochures respectively, with 90.7 % of the ChatGPT generated brochures scoring 40 % on the actionability scale. Generated caregiver and teen materials achieved readability levels of grades 9-14, while child-specific brochures achieved readability levels of grades 6-11. Brochures were formatted appropriately but lacked depth. CONCLUSION ChatGPT demonstrates potential for rapidly generating patient education materials; however, challenges remain in ensuring content specificity. We share the lessons learned to assist other healthcare providers with using this technology.
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Affiliation(s)
- Jacqueline Tian Tran
- College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Canada
| | - Ashley Burghall
- College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Canada
| | - Tom Blydt-Hansen
- Division of Nephrology, Department of Pediatrics, University of British Columbia, Vancouver, Canada
| | - Allison Cammer
- College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Canada
| | - Aviva Goldberg
- Section of Nephrology, Department of Pediatrics and Child Health, Children's Hospital, HSC, Winnipeg, Canada; Max Rady College of Medicine, University of Manitoba, Winnipeg, Canada
| | - Lorraine Hamiwka
- Section of Nephrology, Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | | | | | - Véronique Phan
- Division of Nephrology, Department of Paediatrics, CHU Ste Justine, Université de Montréal, Montréal, Canada
| | - Nicola Rosaasen
- College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Canada
| | - Michelle Ruhl
- Division of Nephrology, Department of Pediatrics, Stollery Children's Hospital, University of Alberta, Edmonton, Canada
| | - Julie Strong
- Section of Nephrology, Department of Pediatrics and Child Health, Children's Hospital, HSC, Winnipeg, Canada
| | - Chia Wei Teoh
- Division of Nephrology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Jenny Wichart
- Department of Pharmacy, Alberta Health Services, Calgary, Canada
| | - Holly Mansell
- College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Canada.
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7
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Rezapour M, Yazdinejad M, Rajabi Kouchi F, Habibi Baghi M, Khorrami Z, Khavanin Zadeh M, Pourbaghi E, Rezapour H. Text mining of hypertension researches in the west Asia region: a 12-year trend analysis. Ren Fail 2024; 46:2337285. [PMID: 38616180 PMCID: PMC11018045 DOI: 10.1080/0886022x.2024.2337285] [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: 01/08/2024] [Accepted: 03/27/2024] [Indexed: 04/16/2024] Open
Abstract
More than half of the world population lives in Asia and hypertension (HTN) is the most prevalent risk factor found in Asia. There are numerous articles published about HTN in Eastern Mediterranean Region (EMRO) and artificial intelligence (AI) methods can analyze articles and extract top trends in each country. Present analysis uses Latent Dirichlet allocation (LDA) as an algorithm of topic modeling (TM) in text mining, to obtain subjective topic-word distribution from the 2790 studies over the EMRO. The period of checked studied is last 12 years and results of LDA analyses show that HTN researches published in EMRO discuss on changes in BP and the factors affecting it. Among the countries in the region, most of these articles are related to I.R Iran and Egypt, which have an increasing trend from 2017 to 2018 and reached the highest level in 2021. Meanwhile, Iraq and Lebanon have been conducting research since 2010. The EMRO word cloud illustrates 'BMI', 'mortality', 'age', and 'meal', which represent important indicators, dangerous outcomes of high BP, and gender of HTN patients in EMRO, respectively.
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Affiliation(s)
- Mohammad Rezapour
- Faculty Member of the Iranian Ministry of Science, Research and Technology, Tehran, Iran
| | | | - Faezeh Rajabi Kouchi
- Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
| | | | - Zahra Khorrami
- Ophthalmic Epidemiology Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Morteza Khavanin Zadeh
- Hasheminejad Kidney Center, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Elmira Pourbaghi
- Faculty of Advanced Sciences and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Hassan Rezapour
- Department of Transportation and Urban Infrastructure Studies, Morgan State University, Baltimore, MD, USA
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8
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Odugbemi AI, Nyirenda C, Christoffels A, Egieyeh SA. Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors. Comput Struct Biotechnol J 2024; 23:2964-2977. [PMID: 39148608 PMCID: PMC11326494 DOI: 10.1016/j.csbj.2024.07.003] [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: 04/16/2024] [Revised: 07/03/2024] [Accepted: 07/03/2024] [Indexed: 08/17/2024] Open
Abstract
Artificial Intelligence is transforming drug discovery, particularly in the hit identification phase of therapeutic compounds. One tool that has been instrumental in this transformation is Quantitative Structure-Activity Relationship (QSAR) analysis. This computer-aided drug design tool uses machine learning to predict the biological activity of new compounds based on the numerical representation of chemical structures against various biological targets. With diabetes mellitus becoming a significant health challenge in recent times, there is intense research interest in modulating antidiabetic drug targets. α-Glucosidase is an antidiabetic target that has gained attention due to its ability to suppress postprandial hyperglycaemia, a key contributor to diabetic complications. This review explored a detailed approach to developing QSAR models, focusing on strategies for generating input variables (molecular descriptors) and computational approaches ranging from classical machine learning algorithms to modern deep learning algorithms. We also highlighted studies that have used these approaches to develop predictive models for α-glucosidase inhibitors to modulate this critical antidiabetic drug target.
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Affiliation(s)
- Adeshina I Odugbemi
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town 7535, South Africa
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town 7535, South Africa
- National Institute for Theoretical and Computational Sciences (NITheCS), South Africa
| | - Clement Nyirenda
- Department of Computer Science, University of the Western Cape, Cape Town 7535, South Africa
| | - Alan Christoffels
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town 7535, South Africa
- Africa Centres for Disease Control and Prevention, African Union, Addis Ababa, Ethiopia
| | - Samuel A Egieyeh
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town 7535, South Africa
- National Institute for Theoretical and Computational Sciences (NITheCS), South Africa
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9
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Weber S, Wyszynski M, Godefroid M, Plattfaut R, Niehaves B. How do medical professionals make sense (or not) of AI? A social-media-based computational grounded theory study and an online survey. Comput Struct Biotechnol J 2024; 24:146-159. [PMID: 38434249 PMCID: PMC10904922 DOI: 10.1016/j.csbj.2024.02.009] [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: 11/30/2023] [Revised: 02/14/2024] [Accepted: 02/14/2024] [Indexed: 03/05/2024] Open
Abstract
To investigate opinions and attitudes of medical professionals towards adopting AI-enabled healthcare technologies in their daily business, we used a mixed-methods approach. Study 1 employed a qualitative computational grounded theory approach analyzing 181 Reddit threads in the several subreddits of r/medicine. By utilizing an unsupervised machine learning clustering method, we identified three key themes: (1) consequences of AI, (2) physician-AI relationship, and (3) a proposed way forward. In particular Reddit posts related to the first two themes indicated that the medical professionals' fear of being replaced by AI and skepticism toward AI played a major role in the argumentations. Moreover, the results suggest that this fear is driven by little or moderate knowledge about AI. Posts related to the third theme focused on factual discussions about how AI and medicine have to be designed to become broadly adopted in health care. Study 2 quantitatively examined the relationship between the fear of AI, knowledge about AI, and medical professionals' intention to use AI-enabled technologies in more detail. Results based on a sample of 223 medical professionals who participated in the online survey revealed that the intention to use AI technologies increases with increasing knowledge about AI and that this effect is moderated by the fear of being replaced by AI.
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Affiliation(s)
- Sebastian Weber
- University of Bremen, Digital Public, Bibliothekstr. 1, 28359 Bremen, Germany
| | - Marc Wyszynski
- University of Bremen, Digital Public, Bibliothekstr. 1, 28359 Bremen, Germany
| | - Marie Godefroid
- University of Siegen, Information Systems, Kohlbettstr. 15, 57072 Siegen, Germany
| | - Ralf Plattfaut
- University of Duisburg-Essen, Information Systems and Transformation Management, Universitätsstr. 9, 45141 Essen, Germany
| | - Bjoern Niehaves
- University of Bremen, Digital Public, Bibliothekstr. 1, 28359 Bremen, Germany
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10
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Xu X, Yang Y, Tan X, Zhang Z, Wang B, Yang X, Weng C, Yu R, Zhao Q, Quan S. Hepatic encephalopathy post-TIPS: Current status and prospects in predictive assessment. Comput Struct Biotechnol J 2024; 24:493-506. [PMID: 39076168 PMCID: PMC11284497 DOI: 10.1016/j.csbj.2024.07.008] [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: 04/14/2024] [Revised: 07/05/2024] [Accepted: 07/05/2024] [Indexed: 07/31/2024] Open
Abstract
Transjugular intrahepatic portosystemic shunt (TIPS) is an essential procedure for the treatment of portal hypertension but can result in hepatic encephalopathy (HE), a serious complication that worsens patient outcomes. Investigating predictors of HE after TIPS is essential to improve prognosis. This review analyzes risk factors and compares predictive models, weighing traditional scores such as Child-Pugh, Model for End-Stage Liver Disease (MELD), and albumin-bilirubin (ALBI) against emerging artificial intelligence (AI) techniques. While traditional scores provide initial insights into HE risk, they have limitations in dealing with clinical complexity. Advances in machine learning (ML), particularly when integrated with imaging and clinical data, offer refined assessments. These innovations suggest the potential for AI to significantly improve the prediction of post-TIPS HE. The study provides clinicians with a comprehensive overview of current prediction methods, while advocating for the integration of AI to increase the accuracy of post-TIPS HE assessments. By harnessing the power of AI, clinicians can better manage the risks associated with TIPS and tailor interventions to individual patient needs. Future research should therefore prioritize the development of advanced AI frameworks that can assimilate diverse data streams to support clinical decision-making. The goal is not only to more accurately predict HE, but also to improve overall patient care and quality of life.
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Affiliation(s)
- Xiaowei Xu
- Department of Gastroenterology Nursing Unit, Ward 192, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Yun Yang
- School of Nursing, Wenzhou Medical University, Wenzhou 325001, China
| | - Xinru Tan
- The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou 325001, China
| | - Ziyang Zhang
- School of Clinical Medicine, Guizhou Medical University, Guiyang 550025, China
| | - Boxiang Wang
- The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou 325001, China
| | - Xiaojie Yang
- Wenzhou Medical University Renji College, Wenzhou 325000, China
| | - Chujun Weng
- The Fourth Affiliated Hospital Zhejiang University School of Medicine, Yiwu 322000, China
| | - Rongwen Yu
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325000, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China
| | - Shichao Quan
- Department of Big Data in Health Science, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
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11
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Kwok KO, Huynh T, Wei WI, Wong SYS, Riley S, Tang A. Utilizing large language models in infectious disease transmission modelling for public health preparedness. Comput Struct Biotechnol J 2024; 23:3254-3257. [PMID: 39286528 PMCID: PMC11402906 DOI: 10.1016/j.csbj.2024.08.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 08/07/2024] [Accepted: 08/07/2024] [Indexed: 09/19/2024] Open
Abstract
Introduction OpenAI's ChatGPT, a Large Language Model (LLM), is a powerful tool across domains, designed for text and code generation, fostering collaboration, especially in public health. Investigating the role of this advanced LLM chatbot in assisting public health practitioners in shaping disease transmission models to inform infection control strategies, marks a new era in infectious disease epidemiology research. This study used a case study to illustrate how ChatGPT collaborates with a public health practitioner in co-designing a mathematical transmission model. Methods Using natural conversation, the practitioner initiated a dialogue involving an iterative process of code generation, refinement, and debugging with ChatGPT to develop a model to fit 10 days of prevalence data to estimate two key epidemiological parameters: i) basic reproductive number (Ro) and ii) final epidemic size. Verification and validation processes are conducted to ensure the accuracy and functionality of the final model. Results ChatGPT developed a validated transmission model which replicated the epidemic curve and gave estimates of Ro of 4.19 (95 % CI: 4.13- 4.26) and a final epidemic size of 98.3 % of the population within 60 days. It highlighted the advantages of using maximum likelihood estimation with Poisson distribution over least squares method. Conclusion Integration of LLM in medical research accelerates model development, reducing technical barriers for health practitioners, democratizing access to advanced modeling and potentially enhancing pandemic preparedness globally, particularly in resource-constrained populations.
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Affiliation(s)
- Kin On Kwok
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
- Hong Kong Institute of Asia-Pacific Studies, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Tom Huynh
- School of Science, Engineering and Technology, RMIT University, Viet Nam
| | - Wan In Wei
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Samuel Y S Wong
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, United Kingdom
- School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, United Kingdom
| | - Arthur Tang
- School of Science, Engineering and Technology, RMIT University, Viet Nam
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12
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Muhammad D, Bendechache M. Unveiling the black box: A systematic review of Explainable Artificial Intelligence in medical image analysis. Comput Struct Biotechnol J 2024; 24:542-560. [PMID: 39252818 PMCID: PMC11382209 DOI: 10.1016/j.csbj.2024.08.005] [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: 06/05/2024] [Revised: 08/07/2024] [Accepted: 08/07/2024] [Indexed: 09/11/2024] Open
Abstract
This systematic literature review examines state-of-the-art Explainable Artificial Intelligence (XAI) methods applied to medical image analysis, discussing current challenges and future research directions, and exploring evaluation metrics used to assess XAI approaches. With the growing efficiency of Machine Learning (ML) and Deep Learning (DL) in medical applications, there's a critical need for adoption in healthcare. However, their "black-box" nature, where decisions are made without clear explanations, hinders acceptance in clinical settings where decisions have significant medicolegal consequences. Our review highlights the advanced XAI methods, identifying how they address the need for transparency and trust in ML/DL decisions. We also outline the challenges faced by these methods and propose future research directions to improve XAI in healthcare. This paper aims to bridge the gap between cutting-edge computational techniques and their practical application in healthcare, nurturing a more transparent, trustworthy, and effective use of AI in medical settings. The insights guide both research and industry, promoting innovation and standardisation in XAI implementation in healthcare.
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Affiliation(s)
- Dost Muhammad
- ADAPT Research Centre, School of Computer Science, University of Galway, Galway, Ireland
| | - Malika Bendechache
- ADAPT Research Centre, School of Computer Science, University of Galway, Galway, Ireland
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Huang G, Li Y, Jameel S, Long Y, Papanastasiou G. From explainable to interpretable deep learning for natural language processing in healthcare: How far from reality? Comput Struct Biotechnol J 2024; 24:362-373. [PMID: 38800693 PMCID: PMC11126530 DOI: 10.1016/j.csbj.2024.05.004] [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: 11/10/2023] [Revised: 05/03/2024] [Accepted: 05/03/2024] [Indexed: 05/29/2024] Open
Abstract
Deep learning (DL) has substantially enhanced natural language processing (NLP) in healthcare research. However, the increasing complexity of DL-based NLP necessitates transparent model interpretability, or at least explainability, for reliable decision-making. This work presents a thorough scoping review of explainable and interpretable DL in healthcare NLP. The term "eXplainable and Interpretable Artificial Intelligence" (XIAI) is introduced to distinguish XAI from IAI. Different models are further categorized based on their functionality (model-, input-, output-based) and scope (local, global). Our analysis shows that attention mechanisms are the most prevalent emerging IAI technique. The use of IAI is growing, distinguishing it from XAI. The major challenges identified are that most XIAI does not explore "global" modelling processes, the lack of best practices, and the lack of systematic evaluation and benchmarks. One important opportunity is to use attention mechanisms to enhance multi-modal XIAI for personalized medicine. Additionally, combining DL with causal logic holds promise. Our discussion encourages the integration of XIAI in Large Language Models (LLMs) and domain-specific smaller models. In conclusion, XIAI adoption in healthcare requires dedicated in-house expertise. Collaboration with domain experts, end-users, and policymakers can lead to ready-to-use XIAI methods across NLP and medical tasks. While challenges exist, XIAI techniques offer a valuable foundation for interpretable NLP algorithms in healthcare.
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Affiliation(s)
- Guangming Huang
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, United Kingdom
| | - Yingya Li
- Harvard Medical School and Boston Children's Hospital, Boston, 02115, United States
| | - Shoaib Jameel
- Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, United Kingdom
| | - Yunfei Long
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, United Kingdom
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14
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Suárez A, Jiménez J, Llorente de Pedro M, Andreu-Vázquez C, Díaz-Flores García V, Gómez Sánchez M, Freire Y. Beyond the Scalpel: Assessing ChatGPT's potential as an auxiliary intelligent virtual assistant in oral surgery. Comput Struct Biotechnol J 2024; 24:46-52. [PMID: 38162955 PMCID: PMC10755495 DOI: 10.1016/j.csbj.2023.11.058] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/28/2023] [Accepted: 11/28/2023] [Indexed: 01/03/2024] Open
Abstract
AI has revolutionized the way we interact with technology. Noteworthy advances in AI algorithms and large language models (LLM) have led to the development of natural generative language (NGL) systems such as ChatGPT. Although these LLM can simulate human conversations and generate content in real time, they face challenges related to the topicality and accuracy of the information they generate. This study aimed to assess whether ChatGPT-4 could provide accurate and reliable answers to general dentists in the field of oral surgery, and thus explore its potential as an intelligent virtual assistant in clinical decision making in oral surgery. Thirty questions related to oral surgery were posed to ChatGPT4, each question repeated 30 times. Subsequently, a total of 900 responses were obtained. Two surgeons graded the answers according to the guidelines of the Spanish Society of Oral Surgery, using a three-point Likert scale (correct, partially correct/incomplete, and incorrect). Disagreements were arbitrated by an experienced oral surgeon, who provided the final grade Accuracy was found to be 71.7%, and consistency of the experts' grading across iterations, ranged from moderate to almost perfect. ChatGPT-4, with its potential capabilities, will inevitably be integrated into dental disciplines, including oral surgery. In the future, it could be considered as an auxiliary intelligent virtual assistant, though it would never replace oral surgery experts. Proper training and verified information by experts will remain vital to the implementation of the technology. More comprehensive research is needed to ensure the safe and successful application of AI in oral surgery.
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Affiliation(s)
- Ana Suárez
- Department of Pre-Clinic Dentistry, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Calle Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain
| | - Jaime Jiménez
- Department of Clinic Dentistry, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Calle Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain
| | - María Llorente de Pedro
- Department of Pre-Clinic Dentistry, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Calle Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain
| | - Cristina Andreu-Vázquez
- Department of Veterinary Medicine, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Calle Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain
| | - Víctor Díaz-Flores García
- Department of Pre-Clinic Dentistry, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Calle Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain
| | - Margarita Gómez Sánchez
- Department of Pre-Clinic Dentistry, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Calle Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain
| | - Yolanda Freire
- Department of Pre-Clinic Dentistry, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Calle Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain
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15
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Guo J, Qin H, Zhou Y, Chen X, Nan L, Huang H. Fast Building Instance Proxy Reconstruction for Large Urban Scenes. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024; 46:7267-7282. [PMID: 38625775 DOI: 10.1109/tpami.2024.3388371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2024]
Abstract
Digitalization of large-scale urban scenes (in particular buildings) has been a long-standing open problem, which attributes to the challenges in data acquisition, such as incomplete scene coverage, lack of semantics, low efficiency, and low reliability in path planning. In this paper, we address these challenges in urban building reconstruction from aerial images, and we propose an effective workflow and a few novel algorithms for efficient 3D building instance proxy reconstruction for large urban scenes. Specifically, we propose a novel learning-based approach to instance segmentation of urban buildings from aerial images followed by a voting-based algorithm to fuse the multi-view instance information to a sparse point cloud (reconstructed using a standard Structure from Motion pipeline). Our method enables effective instance segmentation of the building instances from the point cloud. We also introduce a layer-based surface reconstruction method dedicated to the 3D reconstruction of building proxies from extremely sparse point clouds. Extensive experiments on both synthetic and real-world aerial images of large urban scenes have demonstrated the effectiveness of our approach. The generated scene proxy models can already provide a promising 3D surface representation of the buildings in large urban scenes, and when applied to aerial path planning, the instance-enhanced building proxy models can significantly improve data completeness and accuracy, yielding highly detailed 3D building models.
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Zhang C, Chen X. Letter to the editor, "Evaluating the accuracy of ChatGPT-4 in predicting ASA scores: A prospective multicentric study ChatGPT-4 in ASA score prediction". J Clin Anesth 2024; 98:111571. [PMID: 39180866 DOI: 10.1016/j.jclinane.2024.111571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Accepted: 07/29/2024] [Indexed: 08/27/2024]
Affiliation(s)
- Chenghong Zhang
- Department of Anesthesia, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xinzhong Chen
- Department of Anesthesia, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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17
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Divya R, Shantha Selva Kumari R. Multi-instance learning attention model for amyloid quantification of brain sub regions in longitudinal cognitive decline. Brain Res 2024; 1842:149103. [PMID: 38955250 DOI: 10.1016/j.brainres.2024.149103] [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: 11/09/2023] [Revised: 05/21/2024] [Accepted: 06/26/2024] [Indexed: 07/04/2024]
Abstract
Amyloid PET scans help in identifying the beta-amyloid deposition in different brain regions. The purpose of this study is to develop a deep learning model that can automate the task of finding amyloid deposition in different regions of the brain only by using PET scan and without the corresponding MRI scan. 2647 18F-Florbetapir PET scans are collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) from multiple centres taken over a period. A deep learning model based on multi-instance learning and attention is proposed which is trained and validated using 80% of the scans and the remaining 20% of the scans are used for testing the model. The performance of the model is validated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The proposed model is further tested upon an external dataset consisting of 1413 18F-Florbetapir PET scans from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) study. The proposed model achieves MAE of 0.0243 and RMSE of 0.0320 for summary Standardized Uptake Value Ratio (SUVR) based on composite reference region for ADNI test set. When tested on the A4-study dataset, the proposed model achieves MAE of 0.038 and RMSE of 0.0495 for summary SUVR based on the composite region. The results show that the proposed model provides less MAE and RMSE when compared with existing models. A graphical user interface is developed based on the proposed model where the predictions are made by selecting the files of 18F-Florbetapir PET scans.
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Affiliation(s)
- R Divya
- Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi 626 005, Tamil Nadu, India.
| | - R Shantha Selva Kumari
- Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi 626 005, Tamil Nadu, India.
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Anaya F, Prasad R, Bashour M, Yaghmour R, Alameh A, Balakumaran K. Evaluating ChatGPT platform in delivering heart failure educational material: A comparison with the leading national cardiology institutes. Curr Probl Cardiol 2024; 49:102797. [PMID: 39159709 DOI: 10.1016/j.cpcardiol.2024.102797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2024] [Accepted: 08/16/2024] [Indexed: 08/21/2024]
Abstract
BACKGROUND Patient education plays a crucial role in improving the quality of life for patients with heart failure. As artificial intelligence continues to advance, new chatbots are emerging as valuable tools across various aspects of life. One prominent example is ChatGPT, a widely used chatbot among the public. Our study aims to evaluate the readability of ChatGPT answers for common patients' questions about heart failure. METHODS We performed a comparative analysis between ChatGPT responses and existing heart failure educational materials from top US cardiology institutes. Validated readability calculators were employed to assess and compare the reading difficulty and grade level of the materials. Furthermore, blind assessment using The Patient Education Materials Assessment Tool (PEMAT) was done by four advanced heart failure attendings to evaluate the readability and actionability of each resource. RESULTS Our study revealed that responses generated by ChatGPT were longer and more challenging to read compared to other materials. Additionally, these responses were written at a higher educational level (undergraduate and 9-10th grade), similar to those from the Heart Failure Society of America. Despite achieving a competitive PEMAT readability score (75 %), surpassing the American Heart Association score (68 %), ChatGPT's actionability score was the lowest (66.7 %) among all materials included in our study. CONCLUSION Despite its current limitations, artificial intelligence chatbots has the potential to revolutionize the field of patient education especially given theirs ongoing improvements. However, further research is necessary to ensure the integrity and reliability of these chatbots before endorsing them as reliable resources for patient education.
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Affiliation(s)
- Firas Anaya
- Department of Medicine, Metrohealth Medical Center, Cleveland, OH, USA; Case Western Reserve University, Cleveland, OH, USA.
| | - Rahul Prasad
- Cleveland Clinic Akron General Hospital, Akron, OH, USA.
| | - Marla Bashour
- Department of Medicine, Metrohealth Medical Center, Cleveland, OH, USA; Case Western Reserve University, Cleveland, OH, USA.
| | - Raghad Yaghmour
- Department of Medicine, Metrohealth Medical Center, Cleveland, OH, USA.
| | - Anas Alameh
- Hear and Vascular Center, Metrohealth Medical Center, Cleveland, OH, USA; Case Western Reserve University, Cleveland, OH, USA.
| | - Kathir Balakumaran
- Hear and Vascular Center, Metrohealth Medical Center, Cleveland, OH, USA; Case Western Reserve University, Cleveland, OH, USA.
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Shi C, Cheng L, Yu Y, Chen S, Dai Y, Yang J, Zhang H, Chen J, Geng N. Multi-omics integration analysis: Tools and applications in environmental toxicology. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 360:124675. [PMID: 39103035 DOI: 10.1016/j.envpol.2024.124675] [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: 05/16/2024] [Revised: 07/08/2024] [Accepted: 08/03/2024] [Indexed: 08/07/2024]
Abstract
Nowadays, traditional single-omics study is not enough to explain the causality between molecular alterations and toxicity endpoints for environmental pollutants. With the development of high-throughput sequencing technology and high-resolution mass spectrometry technology, the integrative analysis of multi-omics has become an efficient strategy to understand holistic biological mechanisms and to uncover the regulation network in specific biological processes. This review summarized sample preparation methods, integration analysis tools and the application of multi-omics integration analyses in environmental toxicology field. Currently, omics methods have been widely applied being as the sensitivity of early biological response, especially for low-dose and long-term exposure to environmental pollutants. Integrative omics can reveal the overall changes of genes, proteins, and/or metabolites in the cells, tissues or organisms, which provide new insights into revealing the overall toxicity effects, screening the toxic targets, and exploring the underlying molecular mechanism of pollutants.
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Affiliation(s)
- Chengcheng Shi
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China; College of Environmental Science and Engineering, Dalian Maritime University, Dalian, 116026, China
| | - Lin Cheng
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
| | - Ying Yu
- College of Environmental Science and Engineering, Dalian Maritime University, Dalian, 116026, China
| | - Shuangshuang Chen
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China; College of Environmental Science and Engineering, Dalian Maritime University, Dalian, 116026, China
| | - Yubing Dai
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
| | - Jiajia Yang
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China; College of Materials Science and Engineering, Hebei University of Engineering, Handan, 056038, China
| | - Haijun Zhang
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
| | - Jiping Chen
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
| | - Ningbo Geng
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China.
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20
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Levin C, Naimi E, Saban M. Evaluating GenAI systems to combat mental health issues in healthcare workers: An integrative literature review. Int J Med Inform 2024; 191:105566. [PMID: 39079316 DOI: 10.1016/j.ijmedinf.2024.105566] [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: 04/03/2024] [Revised: 06/10/2024] [Accepted: 07/21/2024] [Indexed: 09/07/2024]
Abstract
BACKGROUND Mental health issues among healthcare workers remain a serious problem globally. Recent surveys continue to report high levels of depression, anxiety, burnout and other conditions amongst various occupational groups. Novel approaches are needed to support clinician well-being. OBJECTIVE This integrative literature review aims to explore the current state of research examining the use of generative artificial intelligence (GenAI) and machine learning (ML) systems to predict mental health issues and identify associated risk factors amongst healthcare professionals. METHODS A literature search of databases was conducted in Medline then adapted as necessary to Scopus, Web of Science, Google Scholar, PubMed and CINAHL with Full Text. Eleven studies met the inclusion criteria for the review. RESULTS Nine studies employed various machine learning techniques to predict different mental health outcomes among healthcare workers. Models showed good predictive performance, with AUCs ranging from 0.82 to 0.904 for outcomes such as depression, anxiety and safety perceptions. Key risk factors identified included fatigue, stress, burnout, workload, sleep issues and lack of support. Two studies explored the potential of sensor-based technologies and GenAI analysis of physiological data. None of the included studies focused on the use of GenAI systems specifically for providing mental health support to healthcare workers. CONCLUSION Preliminary research demonstrates that AI/ML models can effectively predict mental health issues. However, more work is needed to evaluate the real-world integration and impact of these tools, including GenAI systems, in identifying clinician distress and supporting well-being over time. Further research should aim to explore how GenAI may be developed and applied to provide mental health support for healthcare workers.
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Affiliation(s)
- C Levin
- Faculty of School of Life and Health Sciences, Nursing Department, The Jerusalem College of Technology-Lev Academic Center, Jerusalem, Israel; The Department of Vascular Surgery, The Chaim Sheba Medical Center, Tel Hashomer, Ramat Gan, Tel Aviv, Israel
| | - E Naimi
- Department of Nursing, School of Health Professions, Faculty of Medicine, Tel Aviv University
| | - M Saban
- Department of Nursing, School of Health Professions, Faculty of Medicine, Tel Aviv University.
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21
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Zheng Q, Shen Q, Shu Z, Chang K, Zhong K, Yan Y, Ke J, Huang J, Su R, Xia J, Zhou X. Deep representation learning from electronic medical records identifies distinct symptom based subtypes and progression patterns for COVID-19 prognosis. Int J Med Inform 2024; 191:105555. [PMID: 39089210 DOI: 10.1016/j.ijmedinf.2024.105555] [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: 03/14/2024] [Revised: 06/17/2024] [Accepted: 07/14/2024] [Indexed: 08/03/2024]
Abstract
OBJECTIVE Symptoms are significant kind of phenotypes for managing and controlling of the burst of acute infectious diseases, such as COVID-19. Although patterns of symptom clusters and time series have been considered the high potential prediction factors for the prognosis of patients, the elaborated subtypes and their progression patterns based on symptom phenotypes related to the prognosis of COVID-19 patients still need be detected. This study aims to investigate patient subtypes and their progression patterns with distinct features of outcome and prognosis. METHODS This study included a total of 14,139 longitudinal electronic medical records (EMRs) obtained from four hospitals in Hubei Province, China, involving 2,683 individuals in the early stage of COVID-19 pandemic. A deep representation learning model was developed to help acquire the symptom profiles of patients. K-means clustering algorithm is used to divide them into distinct subtypes. Subsequently, symptom progression patterns were identified by considering the subtypes associated with patients upon admission and discharge. Furthermore, we used Fisher's test to identify significant clinical entities for each subtype. RESULTS Three distinct patient subtypes exhibiting specific symptoms and prognosis have been identified. Particularly, Subtype 0 includes 44.2% of the whole and is characterized by poor appetite, fatigue and sleep disorders; Subtype 1 includes 25.6% cases and is characterized by confusion, cough with bloody sputum, encopresis and urinary incontinence; Subtype 2 includes 30.2% cases and is characterized by dry cough and rhinorrhea. These three subtypes demonstrate significant disparities in prognosis, with the mortality rates of 4.72%, 8.59%, and 0.25% respectively. Furthermore, symptom cluster progression patterns showed that patients with Subtype 0 who manifest dark yellow urine, chest pain, etc. in the admission stage exhibit an elevated risk of transforming into the more severe subtypes with poor outcome, whereas those presenting with nausea and vomiting tend to incline towards entering the milder subtype. CONCLUSION This study has proposed a clinical meaningful approach by utilizing the deep representation learning and real-world EMR data containing symptom phenotypes to identify the COVID-19 subtypes and their progression patterns. The results would be potentially useful to help improve the precise stratification and management of acute infectious diseases.
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Affiliation(s)
- Qiguang Zheng
- School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Qifan Shen
- School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Zixin Shu
- School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Kai Chang
- School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Kunyu Zhong
- School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Yuhang Yan
- School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Jia Ke
- Hubei Provincial Hospital of Traditional Chinese Medicine, China
| | - Jingjing Huang
- Hubei Provincial Hospital of Traditional Chinese Medicine, China
| | - Rui Su
- Beijing Hospital of Traditional Chinese Medicine, China
| | - Jianan Xia
- School of Computer and Information Technology, Beijing Jiaotong University, China.
| | - Xuezhong Zhou
- School of Computer and Information Technology, Beijing Jiaotong University, China.
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Diakou I, Iliopoulos E, Papakonstantinou E, Dragoumani K, Yapijakis C, Iliopoulos C, Spandidos DA, Chrousos GP, Eliopoulos E, Vlachakis D. Multi‑label classification of biomedical data. MEDICINE INTERNATIONAL 2024; 4:68. [PMID: 39301328 PMCID: PMC11411592 DOI: 10.3892/mi.2024.192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 08/30/2024] [Indexed: 09/22/2024]
Abstract
Biomedical datasets constitute a rich source of information, containing multivariate data collected during medical practice. In spite of inherent challenges, such as missing or imbalanced data, these types of datasets are increasingly utilized as a basis for the construction of predictive machine-learning models. The prediction of disease outcomes and complications could inform the process of decision-making in the hospital setting and ensure the best possible patient management according to the patient's features. Multi-label classification algorithms, which are trained to assign a set of labels to input samples, can efficiently tackle outcome prediction tasks. Myocardial infarction (MI) represents a widespread health risk, accounting for a significant portion of heart disease-related mortality. Moreover, the danger of potential complications occurring in patients with MI during their period of hospitalization underlines the need for systems to efficiently assess the risks of patients with MI. In order to demonstrate the critical role of applying machine-learning methods in medical challenges, in the present study, a set of multi-label classifiers was evaluated on a public dataset of MI-related complications to predict the outcomes of hospitalized patients with MI, based on a set of input patient features. Such methods can be scaled through the use of larger datasets of patient records, along with fine-tuning for specific patient sub-groups or patient populations in specific regions, to increase the performance of these approaches. Overall, a prediction system based on classifiers trained on patient records may assist healthcare professionals in providing personalized care and efficient monitoring of high-risk patient subgroups.
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Affiliation(s)
- Io Diakou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Eddie Iliopoulos
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Eleni Papakonstantinou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
- University Research Institute of Maternal and Child Health and Precision Medicine, National and Kapodistrian University of Athens, 'Aghia Sophia' Children's Hospital, 11527 Athens, Greece
| | - Konstantina Dragoumani
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Christos Yapijakis
- University Research Institute of Maternal and Child Health and Precision Medicine, National and Kapodistrian University of Athens, 'Aghia Sophia' Children's Hospital, 11527 Athens, Greece
| | - Costas Iliopoulos
- School of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, London WC2R 2LS, UK
| | - Demetrios A Spandidos
- Laboratory of Clinical Virology, School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - George P Chrousos
- University Research Institute of Maternal and Child Health and Precision Medicine, National and Kapodistrian University of Athens, 'Aghia Sophia' Children's Hospital, 11527 Athens, Greece
| | - Elias Eliopoulos
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Dimitrios Vlachakis
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
- University Research Institute of Maternal and Child Health and Precision Medicine, National and Kapodistrian University of Athens, 'Aghia Sophia' Children's Hospital, 11527 Athens, Greece
- School of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, London WC2R 2LS, UK
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23
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Kelley MC, Perry SJ, Tucker BV. The Mason-Alberta Phonetic Segmenter: a forced alignment system based on deep neural networks and interpolation. PHONETICA 2024; 81:451-508. [PMID: 39248125 PMCID: PMC11449383 DOI: 10.1515/phon-2024-0015] [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: 04/18/2024] [Accepted: 08/08/2024] [Indexed: 09/10/2024]
Abstract
Given an orthographic transcription, forced alignment systems automatically determine boundaries between segments in speech, facilitating the use of large corpora. In the present paper, we introduce a neural network-based forced alignment system, the Mason-Alberta Phonetic Segmenter (MAPS). MAPS serves as a testbed for two possible improvements we pursue for forced alignment systems. The first is treating the acoustic model as a tagger, rather than a classifier, motivated by the common understanding that segments are not truly discrete and often overlap. The second is an interpolation technique to allow more precise boundaries than the typical 10 ms limit in modern systems. During testing, all system configurations we trained significantly outperformed the state-of-the-art Montreal Forced Aligner in the 10 ms boundary placement tolerance threshold. The greatest difference achieved was a 28.13 % relative performance increase. The Montreal Forced Aligner began to slightly outperform our models at around a 30 ms tolerance. We also reflect on the training process for acoustic modeling in forced alignment, highlighting how the output targets for these models do not match phoneticians' conception of similarity between phones and that reconciling this tension may require rethinking the task and output targets or how speech itself should be segmented.
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Affiliation(s)
- Matthew C Kelley
- Department of English, Linguistics Program, George Mason University 3298 , Fairfax, VA, USA
| | - Scott James Perry
- Department of Linguistics, University of Alberta, Edmonton, AB, Canada
| | - Benjamin V Tucker
- Department of Linguistics, University of Alberta, Edmonton, AB, Canada
- Department of Communication Sciences and Disorders, Northern Arizona University, Flagstaff, AZ, USA
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24
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Di Paolo LD, White B, Guénin-Carlut A, Constant A, Clark A. Active inference goes to school: the importance of active learning in the age of large language models. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230148. [PMID: 39155715 PMCID: PMC11391319 DOI: 10.1098/rstb.2023.0148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 12/16/2023] [Accepted: 01/23/2024] [Indexed: 08/20/2024] Open
Abstract
Human learning essentially involves embodied interactions with the material world. But our worlds now include increasing numbers of powerful and (apparently) disembodied generative artificial intelligence (AI). In what follows we ask how best to understand these new (somewhat 'alien', because of their disembodied nature) resources and how to incorporate them in our educational practices. We focus on methodologies that encourage exploration and embodied interactions with 'prepared' material environments, such as the carefully organized settings of Montessori education. Using the active inference framework, we approach our questions by thinking about human learning as epistemic foraging and prediction error minimization. We end by arguing that generative AI should figure naturally as new elements in prepared learning environments by facilitating sequences of precise prediction error enabling trajectories of self-correction. In these ways, we anticipate new synergies between (apparently) disembodied and (essentially) embodied forms of intelligence. This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.
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Affiliation(s)
- Laura Desirèe Di Paolo
- Department of Engineering and Informatics, The University of Sussex , Brighton, UK
- School of Psychology, Children & Technology Lab, The University of Sussex , Falmer (Brighton), UK
| | - Ben White
- Department of Philosophy, The University of Sussex , Sussex, UK
| | - Avel Guénin-Carlut
- Department of Engineering and Informatics, The University of Sussex , Brighton, UK
| | - Axel Constant
- Department of Engineering and Informatics, The University of Sussex , Brighton, UK
| | - Andy Clark
- Department of Engineering and Informatics, The University of Sussex , Brighton, UK
- Department of Philosophy, The University of Sussex , Sussex, UK
- Department of Philosophy, Macquarie University , Sydney, New South Wales, Australia
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25
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Chen Z, Chen M, Huang S, Wang Z, Zhang Y, Huang Y, Li W, Huang X. Texture-Based Classification of Fetal Growth Restriction From Intrauterine Neurosonographic Image. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024. [PMID: 39365033 DOI: 10.1002/jum.16594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 09/12/2024] [Accepted: 09/15/2024] [Indexed: 10/05/2024]
Abstract
OBJECTIVE Fetal growth restriction (FGR) is a condition where fetuses fail to reach their genetic potential for growth, posing a significant health challenge for newborns. The aim of this research was to explore the efficacy of texture-based analysis of neurosonographic images in identifying FGR in fetuses, which may provide a promising tool for early assessment of FGR. METHODS A retrospective analysis collected 100 intrauterine neurosonographic images from 50 FGR and 50 gestational age-appropriate fetuses. Using MaZda software, approximately 300 texture features were extracted from occipital white matter (OWM) and cerebellum of intrauterine neurosonographic images, respectively. Then 10 optimal features were separately selected by 3 algorithms, including the Fisher coefficient method, the method of minimizing classification error probability and average correlation coefficients, and the mutual information coefficient method. Further, the 10 statistically most significant features were selected from these sets to form the mixed feature set. After nonlinear discriminant analysis was performed to reduce feature dimensionality, the artificial neural network (ANN) classifier was conducted, respectively. RESULTS For OWM and cerebellum, a total of 11 and 14 statistically significant features were selected. When the mixed feature sets of OWM and cerebellum were applied to ANN classifier, classification accuracy were 90.00% (κ = 0.800; P < .001) and 93.00% (κ = 0.860; P < .001), and the receiver operating characteristic curve for identifying FGR showed an area under the curve of 0.82 and 0.87. CONCLUSIONS Texture analysis of fetal intrauterine neurosonographic images is a feasible and noninvasive strategy for evaluating FGR fetuses.
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Affiliation(s)
- Zehao Chen
- School of Computer Science and Technology, Dongguan University of Technology, Dongguan, China
| | - Mengjie Chen
- Department of Medical Ultrasonics, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Shiying Huang
- Department of Medical Ultrasonics, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Zhongming Wang
- School of Computer Science and Technology, Dongguan University of Technology, Dongguan, China
| | - Yiheng Zhang
- School of Computer Science and Technology, Dongguan University of Technology, Dongguan, China
| | - Yuhan Huang
- School of Computer Science and Technology, Dongguan University of Technology, Dongguan, China
| | - Weiling Li
- School of Computer Science and Technology, Dongguan University of Technology, Dongguan, China
| | - Xiaowei Huang
- School of Computer Science and Technology, Dongguan University of Technology, Dongguan, China
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Etim E, Tashi Choedron K, Ajai O. Municipal solid waste management in Lagos State: Expansion diffusion of awareness. WASTE MANAGEMENT (NEW YORK, N.Y.) 2024; 190:261-272. [PMID: 39362020 DOI: 10.1016/j.wasman.2024.09.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 09/20/2024] [Accepted: 09/28/2024] [Indexed: 10/05/2024]
Abstract
This study examined the role of waste management authorities in promoting public awareness of municipal solid waste management (MSWM) through the lens of the expansion diffusion theory (EDT). EDT emphasizes the spread of new ideas and practices within a society through various communication channels and distinct individuals within each system. We employed a mixed-method approach using 116 survey responses from Lagos residents and five semi-structured in-depth interviews. Our findings reveal the need for a more structured approach to create public awareness of MSWM, considering the distinct groups of residents in Lagos and their responses to innovation and knowledge diffusion. We propose four pillars on which waste management authorities in developing countries can sustain their MSWM awareness campaigns, as well as an awareness campaign strategy flowchart. Our findings add to the expanding body of research on public awareness and participation in MSWM, emphasizing the critical role that waste management authorities can play in fostering sustainable waste management awareness and practices.
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Affiliation(s)
- Emma Etim
- School of Geography, University of Nottingham, UK.
| | - Karma Tashi Choedron
- School of Politics, History and International Relations, Faculty of Arts and Social Sciences, University of Nottingham, Malaysia.
| | - Olawale Ajai
- Department of Strategy, Lagos Business School, Nigeria
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27
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Malle BF, Scheutz M, Cusimano C, Voiklis J, Komatsu T, Thapa S, Aladia S. People's judgments of humans and robots in a classic moral dilemma. Cognition 2024; 254:105958. [PMID: 39362054 DOI: 10.1016/j.cognition.2024.105958] [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: 06/14/2024] [Revised: 09/02/2024] [Accepted: 09/07/2024] [Indexed: 10/05/2024]
Abstract
How do ordinary people evaluate robots that make morally significant decisions? Previous work has found both equal and different evaluations, and different ones in either direction. In 13 studies (N = 7670), we asked people to evaluate humans and robots that make decisions in norm conflicts (variants of the classic trolley dilemma). We examined several conditions that may influence whether moral evaluations of human and robot agents are the same or different: the type of moral judgment (norms vs. blame); the structure of the dilemma (side effect vs. means-end); salience of particular information (victim, outcome); culture (Japan vs. US); and encouraged empathy. Norms for humans and robots are broadly similar, but blame judgments show a robust asymmetry under one condition: Humans are blamed less than robots specifically for inaction decisions-here, refraining from sacrificing one person for the good of many. This asymmetry may emerge because people appreciate that the human faces an impossible decision and deserves mitigated blame for inaction; when evaluating a robot, such appreciation appears to be lacking. However, our evidence for this explanation is mixed. We discuss alternative explanations and offer methodological guidance for future work into people's moral judgment of robots and humans.
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Radha Krishnan RP, Hung EH, Ashford M, Edillo CE, Gardner C, Hatrick HB, Kim B, Lai AWY, Li X, Zhao YX, Raubenheimer JE. Evaluating the capability of ChatGPT in predicting drug-drug interactions: Real-world evidence using hospitalized patient data. Br J Clin Pharmacol 2024. [PMID: 39359001 DOI: 10.1111/bcp.16275] [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: 06/07/2024] [Revised: 09/11/2024] [Accepted: 09/18/2024] [Indexed: 10/04/2024] Open
Abstract
Drug-drug interactions (DDIs) present a significant health burden, compounded by clinician time constraints and poor patient health literacy. We assessed the ability of ChatGPT (generative artificial intelligence-based large language model) to predict DDIs in a real-world setting. Demographics, diagnoses and prescribed medicines for 120 hospitalized patients were input through three standardized prompts to ChatGPT version 3.5 and compared against pharmacist DDI evaluation to estimate diagnostic accuracy. Area under receiver operating characteristic and inter-rater reliability (Cohen's and Fleiss' kappa coefficients) were calculated. ChatGPT's responses differed based on prompt wording style, with higher sensitivity for prompts mentioning 'drug interaction'. Confusion matrices displayed low true positive and high true negative rates, and there was minimal agreement between ChatGPT and pharmacists (Cohen's kappa values 0.077-0.143). Low sensitivity values suggest a lack of success in identifying DDIs by ChatGPT, and further development is required before it can reliably assess potential DDIs in real-world scenarios.
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Affiliation(s)
| | - Euniss Hinyo Hung
- Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
- Faculty of Science, University of Sydney, Sydney, New South Wales, Australia
| | - Megan Ashford
- Faculty of Science, University of Sydney, Sydney, New South Wales, Australia
| | - Clark Ethan Edillo
- Faculty of Science, University of Sydney, Sydney, New South Wales, Australia
| | - Charlise Gardner
- Faculty of Science, University of Sydney, Sydney, New South Wales, Australia
| | | | - Byungjun Kim
- Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
- Faculty of Science, University of Sydney, Sydney, New South Wales, Australia
| | - Angel Wing Yan Lai
- Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Xinran Li
- Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
- Faculty of Science, University of Sydney, Sydney, New South Wales, Australia
| | - Yvonne Xinyi Zhao
- Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
- Faculty of Science, University of Sydney, Sydney, New South Wales, Australia
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Goparaju N. Picture This: Text-to-Image Models Transforming Pediatric Emergency Medicine. Ann Emerg Med 2024:S0196-0644(24)00413-X. [PMID: 39365207 DOI: 10.1016/j.annemergmed.2024.07.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 07/10/2024] [Accepted: 07/23/2024] [Indexed: 10/05/2024]
Affiliation(s)
- Niharika Goparaju
- Department of Pediatric Emergency Medicine, the University of Texas Austin, Dell Medical School, Dell Children's Medical Center, Austin, TX.
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30
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Ballard JL, Wang Z, Li W, Shen L, Long Q. Deep learning-based approaches for multi-omics data integration and analysis. BioData Min 2024; 17:38. [PMID: 39358793 PMCID: PMC11446004 DOI: 10.1186/s13040-024-00391-z] [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: 04/24/2024] [Accepted: 09/06/2024] [Indexed: 10/04/2024] Open
Abstract
BACKGROUND The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a more complete understanding of each subject. In the biomedical domain, multi-omics data includes molecular (genomics, transcriptomics, proteomics, epigenomics, metabolomics, etc.) and imaging (radiomics, pathomics) modalities which, when combined, have the potential to improve performance on prediction, classification, clustering and other tasks. Deep learning encompasses a wide variety of methods, each of which have certain strengths and weaknesses for multi-omics integration. METHOD In this review, we categorize recent deep learning-based approaches by their basic architectures and discuss their unique capabilities in relation to one another. We also discuss some emerging themes advancing the field of multi-omics integration. RESULTS Deep learning-based multi-omics integration methods were categorized broadly into non-generative (feedforward neural networks, graph convolutional neural networks, and autoencoders) and generative (variational methods, generative adversarial models, and a generative pretrained model). Generative methods have the advantage of being able to impose constraints on the shared representations to enforce certain properties or incorporate prior knowledge. They can also be used to generate or impute missing modalities. Recent advances achieved by these methods include the ability to handle incomplete data as well as going beyond the traditional molecular omics data types to integrate other modalities such as imaging data. CONCLUSION We expect to see further growth in methods that can handle missingness, as this is a common challenge in working with complex and heterogeneous data. Additionally, methods that integrate more data types are expected to improve performance on downstream tasks by capturing a comprehensive view of each sample.
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Affiliation(s)
- Jenna L Ballard
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Zexuan Wang
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, 209 S. 33rd Street, Philadelphia, PA, 19104, USA
| | - Wenrui Li
- Department of Statistics, University of Connecticut, 215 Glenbrook Road, Storrs, CT, 06269, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, 19104, USA.
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, 19104, USA.
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Uribe SE, Maldupa I. Estimating the use of ChatGPT in dental research publications. J Dent 2024; 149:105275. [PMID: 39089668 DOI: 10.1016/j.jdent.2024.105275] [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: 06/17/2024] [Revised: 07/16/2024] [Accepted: 07/28/2024] [Indexed: 08/04/2024] Open
Abstract
INTRODUCTION Generative artificial intelligence (GenAI) Large-language models such as ChatGPT have become increasingly popular in various fields. However, the impact of ChatGPT on dental research writing has yet to be quantified. This study aimed to assess ChatGPT's usage in dental research writing and discuss potential advantages and challenges. METHODS Using a bibliometric design, we performed a keyword analysis of specific 'signaling words' indicative of ChatGPT use in the titles/abstracts of 299,695 dental research abstracts indexed PubMed 2018-2024. Statistical comparisons using normalized ratios per 10,000 dental publications compared changes in word frequency before and after the ChatGPT release on November 30, 2022. RESULTS Before ChatGPT's release, the frequency of abstracts with signaling words was 47.1 per 10,000 papers. After the release, this increased to 224.2 per 10,000 papers, an increase of 177.2 per 10,000 papers (p = 0.014, 95 % CI 53.5-300.7). The word 'delve' showed the most significant usage increase (increased ratio=17.0). CONCLUSIONS This study is among the first to systematically assess the use of GenAI, specifically ChatGPT, in dental research. We found evidence of the use and growth of ChatGPT in dental research publications. This trend indicates the widespread adoption of GenAI-assisted writing in scientific communication, consistent with other scientific fields. While GenAI can potentially increase productivity and inclusivity, it raises concerns such as bias, inaccuracy, and distortion of academic incentives. Therefore, our findings support the need for clear AI guidelines and standards for academic publishing to ensure responsible use and maintain scientific integrity.
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Affiliation(s)
- Sergio E Uribe
- Department of Conservative Dentistry and Oral Health, Riga Stradins University, Riga, Latvia; Baltic Biomaterials Centre of Excellence, Headquarters at Riga Technical University & RSU Institute of Stomatology, Riga, Latvia; Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, Germany.
| | - Ilze Maldupa
- Department of Conservative Dentistry and Oral Health, Riga Stradins University, Riga, Latvia.
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Rhim J, Gallois H, Ravitsky V, Bélisle-Pipon JC. Beyond Consent: The MAMLS in the Room. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2024; 24:85-88. [PMID: 39283388 DOI: 10.1080/15265161.2024.2388737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/20/2024]
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33
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Ahmed A, Imran AS, Kastrati Z, Daudpota SM, Ullah M, Noor W. Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset. Comput Biol Med 2024; 181:109044. [PMID: 39180859 DOI: 10.1016/j.compbiomed.2024.109044] [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: 11/01/2023] [Revised: 05/25/2024] [Accepted: 08/17/2024] [Indexed: 08/27/2024]
Abstract
Wrist pathologies, particularly fractures common among children and adolescents, present a critical diagnostic challenge. While X-ray imaging remains a prevalent diagnostic tool, the increasing misinterpretation rates highlight the need for more accurate analysis, especially considering the lack of specialized training among many surgeons and physicians. Recent advancements in deep convolutional neural networks offer promise in automating pathology detection in trauma X-rays. However, distinguishing subtle variations between pediatric wrist pathologies in X-rays remains challenging. Traditional manual annotation, though effective, is laborious, costly, and requires specialized expertise. In this paper, we address the challenge of pediatric wrist pathology recognition with a fine-grained approach, aimed at automatically identifying discriminative regions in X-rays without manual intervention. We refine our fine-grained architecture through ablation analysis and the integration of LION. Leveraging Grad-CAM, an explainable AI technique, we highlight these regions. Despite using limited data, reflective of real-world medical study constraints, our method consistently outperforms state-of-the-art image recognition models on both augmented and original (challenging) test sets. Our proposed refined architecture achieves an increase in accuracy of 1.06% and 1.25% compared to the baseline method, resulting in accuracies of 86% and 84%, respectively. Moreover, our approach demonstrates the highest fracture sensitivity of 97%, highlighting its potential to enhance wrist pathology recognition.
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Affiliation(s)
- Ammar Ahmed
- Intelligent Systems and Analytics (ISA) Research Group, Department of Computer Science (IDI), Norwegian University of Science & Technology (NTNU), Gjøvik, 2815, Norway.
| | - Ali Shariq Imran
- Intelligent Systems and Analytics (ISA) Research Group, Department of Computer Science (IDI), Norwegian University of Science & Technology (NTNU), Gjøvik, 2815, Norway.
| | - Zenun Kastrati
- Department of Informatics, Linnaeus University, Växjö, 351 95, Sweden.
| | | | - Mohib Ullah
- Intelligent Systems and Analytics (ISA) Research Group, Department of Computer Science (IDI), Norwegian University of Science & Technology (NTNU), Gjøvik, 2815, Norway.
| | - Waheed Noor
- Department of Computer Science & Information Technology, University of Balochistan, Quetta, 87300, Pakistan.
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Becker C, Conduit R, Chouinard PA, Laycock R. Can deepfakes be used to study emotion perception? A comparison of dynamic face stimuli. Behav Res Methods 2024; 56:7674-7690. [PMID: 38834812 PMCID: PMC11362322 DOI: 10.3758/s13428-024-02443-y] [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] [Accepted: 05/11/2024] [Indexed: 06/06/2024]
Abstract
Video recordings accurately capture facial expression movements; however, they are difficult for face perception researchers to standardise and manipulate. For this reason, dynamic morphs of photographs are often used, despite their lack of naturalistic facial motion. This study aimed to investigate how humans perceive emotions from faces using real videos and two different approaches to artificially generating dynamic expressions - dynamic morphs, and AI-synthesised deepfakes. Our participants perceived dynamic morphed expressions as less intense when compared with videos (all emotions) and deepfakes (fearful, happy, sad). Videos and deepfakes were perceived similarly. Additionally, they perceived morphed happiness and sadness, but not morphed anger or fear, as less genuine than other formats. Our findings support previous research indicating that social responses to morphed emotions are not representative of those to video recordings. The findings also suggest that deepfakes may offer a more suitable standardized stimulus type compared to morphs. Additionally, qualitative data were collected from participants and analysed using ChatGPT, a large language model. ChatGPT successfully identified themes in the data consistent with those identified by an independent human researcher. According to this analysis, our participants perceived dynamic morphs as less natural compared with videos and deepfakes. That participants perceived deepfakes and videos similarly suggests that deepfakes effectively replicate natural facial movements, making them a promising alternative for face perception research. The study contributes to the growing body of research exploring the usefulness of generative artificial intelligence for advancing the study of human perception.
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Białas M, Mirończuk MM, Mańdziuk J. Leveraging spiking neural networks for topic modeling. Neural Netw 2024; 178:106494. [PMID: 38972130 DOI: 10.1016/j.neunet.2024.106494] [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: 01/17/2024] [Revised: 05/06/2024] [Accepted: 06/25/2024] [Indexed: 07/09/2024]
Abstract
This article investigates the application of spiking neural networks (SNNs) to the problem of topic modeling (TM): the identification of significant groups of words that represent human-understandable topics in large sets of documents. Our research is based on the hypothesis that an SNN that implements the Hebbian learning paradigm is capable of becoming specialized in the detection of statistically significant word patterns in the presence of adequately tailored sequential input. To support this hypothesis, we propose a novel spiking topic model (STM) that transforms text into a sequence of spikes and uses that sequence to train single-layer SNNs. In STM, each SNN neuron represents one topic, and each of the neuron's weights corresponds to one word. STM synaptic connections are modified according to spike-timing-dependent plasticity; after training, the neurons' strongest weights are interpreted as the words that represent topics. We compare the performance of STM with four other TM methods Latent Dirichlet Allocation (LDA), Biterm Topic Model (BTM), Embedding Topic Model (ETM) and BERTopic on three datasets: 20Newsgroups, BBC news, and AG news. The results demonstrate that STM can discover high-quality topics and successfully compete with comparative classical methods. This sheds new light on the possibility of the adaptation of SNN models in unsupervised natural language processing.
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Affiliation(s)
- Marcin Białas
- National Information Processing Institute, al. Niepodległości 188b, 00-608, Warsaw, Poland.
| | | | - Jacek Mańdziuk
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland.
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He Z, Bauch CT. Effect of homophily on coupled behavior-disease dynamics near a tipping point. Math Biosci 2024; 376:109264. [PMID: 39097225 DOI: 10.1016/j.mbs.2024.109264] [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: 04/01/2024] [Revised: 06/18/2024] [Accepted: 07/26/2024] [Indexed: 08/05/2024]
Abstract
Understanding the interplay between social activities and disease dynamics is crucial for effective public health interventions. Recent studies using coupled behavior-disease models assumed homogeneous populations. However, heterogeneity in population, such as different social groups, cannot be ignored. In this study, we divided the population into social media users and non-users, and investigated the impact of homophily (the tendency for individuals to associate with others similar to themselves) and online events on disease dynamics. Our results reveal that homophily hinders the adoption of vaccinating strategies, hastening the approach to a tipping point after which the population converges to an endemic equilibrium with no vaccine uptake. Furthermore, we find that online events can significantly influence disease dynamics, with early discussions on social media platforms serving as an early warning signal of potential disease outbreaks. Our model provides insights into the mechanisms underlying these phenomena and underscores the importance of considering homophily in disease modeling and public health strategies.
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Affiliation(s)
- Zitao He
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
| | - Chris T Bauch
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
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Jia X, Carter BW, Duffton A, Harris E, Hobbs R, Li H. Advancing the Collaboration Between Imaging and Radiation Oncology. Semin Radiat Oncol 2024; 34:402-417. [PMID: 39271275 PMCID: PMC11407744 DOI: 10.1016/j.semradonc.2024.07.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
The fusion of cutting-edge imaging technologies with radiation therapy (RT) has catalyzed transformative breakthroughs in cancer treatment in recent decades. It is critical for us to review our achievements and preview into the next phase for future synergy between imaging and RT. This paper serves as a review and preview for fostering collaboration between these two domains in the forthcoming decade. Firstly, it delineates ten prospective directions ranging from technological innovations to leveraging imaging data in RT planning, execution, and preclinical research. Secondly, it presents major directions for infrastructure and team development in facilitating interdisciplinary synergy and clinical translation. We envision a future where seamless integration of imaging technologies into RT will not only meet the demands of RT but also unlock novel functionalities, enhancing accuracy, efficiency, safety, and ultimately, the standard of care for patients worldwide.
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Affiliation(s)
- Xun Jia
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD..
| | - Brett W Carter
- Department of Thoracic Imaging, Division of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Aileen Duffton
- Beatson West of Scotland Cancer Centre, Glasgow, UK.; Institute of Cancer Science, University of Glasgow, UK
| | - Emma Harris
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London, UK
| | - Robert Hobbs
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Heng Li
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
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Grover S, Court L, Amoo-Mitchual S, Longo J, Rodin D, Scott AA, Lievens Y, Yap ML, Abdel-Wahab M, Lee P, Harsdorf E, Khader J, Jia X, Dosanjh M, Elzawawy A, Ige T, Pomper M, Pistenmaa D, Hardenbergh P, Petereit DG, Sargent M, Cina K, Li B, Anacak Y, Mayo C, Prattipati S, Lasebikan N, Rendle K, O'Brien D, Wendling E, Coleman CN. Global Workforce and Access: Demand, Education, Quality. Semin Radiat Oncol 2024; 34:477-493. [PMID: 39271284 DOI: 10.1016/j.semradonc.2024.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
There has long existed a substantial disparity in access to radiotherapy globally. This issue has only been exacerbated as the growing disparity of cancer incidence between high-income countries (HIC) and low and middle-income countries (LMICs) widens, with a pronounced increase in cancer cases in LMICs. Even within HICs, iniquities within local communities may lead to a lack of access to care. Due to these trends, it is imperative to find solutions to narrow global disparities. This requires the engagement of a diverse cohort of stakeholders, including working professionals, non-governmental organizations, nonprofits, professional societies, academic and training institutions, and industry. This review brings together a diverse group of experts to highlight critical areas that could help reduce the current global disparities in radiation oncology. Advancements in technology and treatment, such as artificial intelligence, brachytherapy, hypofractionation, and digital networks, in combination with implementation science and novel funding mechanisms, offer means for increasing access to care and education globally. Common themes across sections reveal how utilizing these new innovations and strengthening collaborative efforts among stakeholders can help improve access to care globally while setting the framework for the next generation of innovations.
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Affiliation(s)
- Surbhi Grover
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Botswana-University of Pennsylvania Partnership, Gaborone, Botswana.
| | - Laurence Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center
| | - Sheldon Amoo-Mitchual
- Botswana-University of Pennsylvania Partnership, Gaborone, Botswana; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - John Longo
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Danielle Rodin
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada; Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada; Global Cancer Program, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | | | - Yolande Lievens
- Department of Radiation Oncology, Ghent University Hospital, Belgium; Ghent University, Ghent, Belgium
| | - Mei Ling Yap
- Liverpool and Macarthur Cancer Therapy Centres, Western Sydney University, Campbelltown, New South Wales, Australia; The George Institute for Global Health, UNSW Sydney, Barangaroo, NSW, Australia; Collaboration for Cancer Outcomes, Research and Evaluation (CCORE), Ingham Institute, UNSW Sydney, Liverpool, NSW, Australia
| | - May Abdel-Wahab
- Division of Human Health, International Atomic Energy Agency, Vienna, Austria
| | - Peter Lee
- Division of Human Health, International Atomic Energy Agency, Vienna, Austria
| | - Ekaterina Harsdorf
- Division of Human Health, International Atomic Energy Agency, Vienna, Austria
| | - Jamal Khader
- Radiation Oncology Department, King Hussein Cancer Center, Amman, Jordan
| | - Xun Jia
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Manjit Dosanjh
- ICEC, CERN, Geneva, Switzerland; University of Oxford, Oxford, UK
| | - Ahmed Elzawawy
- Department of Clinical Oncology, Suez Canal University, Ismailia, Egypt; Alsoliman Clinical and Radiation Oncology Center, Port Said, Egypt
| | | | - Miles Pomper
- James Martin Center for Nonproliferation Studies, Washington, DC; ICEC, International Cancer Expert Corps, Washington, DC
| | | | | | - Daniel G Petereit
- Monument Health Cancer Care Institute Rapid City, South Dakota; Avera Research Institute, Sioux Falls, SD
| | | | | | - Benjamin Li
- University of Washington, Seattle, WA; Fred Hutch Cancer Center, Seattle, WA
| | - Yavuz Anacak
- Department of Radiation Oncology, Ege University, Faculty of Medicine, Izmir, Turkey
| | - Chuck Mayo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | | | - Nwamaka Lasebikan
- Department of Radiation and Clinical Oncology, University of Nigeria Teaching Hospital, Enugu, Nigeria
| | - Katharine Rendle
- Department of Family Medicine & Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Donna O'Brien
- ICEC, International Cancer Expert Corps, Washington, DC
| | | | - C Norman Coleman
- ICEC, International Cancer Expert Corps, Washington, DC; Radiation Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD
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Masters K, Herrmann-Werner A, Festl-Wietek T, Taylor D. Preparing for Artificial General Intelligence (AGI) in Health Professions Education: AMEE Guide No. 172. MEDICAL TEACHER 2024; 46:1258-1271. [PMID: 39115700 DOI: 10.1080/0142159x.2024.2387802] [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: 07/27/2024] [Accepted: 07/30/2024] [Indexed: 09/28/2024]
Abstract
Generative Artificial Intelligence (GenAI) caught Health Professions Education (HPE) institutions off-guard, and they are currently adjusting to a changed educational environment. On the horizon, however, is Artificial General Intelligence (AGI) which promises to be an even greater leap and challenge. This Guide begins by explaining the context and nature of AGI, including its characteristics of multi-modality, generality, adaptability, autonomy, and learning ability. It then explores the implications of AGI on students (including personalised learning and electronic tutors) and HPE institutions, and considers some of the context provided by AGI in healthcare. It then raises the problems to address, including the impact on employment, social risks, student adaptability, costs, quality, and others. After considering a possible timeline, the Guide then ends by indicating some first steps that HPE institutions and educators can take to prepare for AGI.
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Affiliation(s)
- Ken Masters
- Medical Education and Informatics Department, College of Medicine and Health Sciences, Sultan Qaboos University, Muscat, Sultanate of Oman
| | - Anne Herrmann-Werner
- Tübingen Institute for Medical Education, University of Tübingen, Tübingen, Germany
| | - Teresa Festl-Wietek
- Tübingen Institute for Medical Education, University of Tübingen, Tübingen, Germany
| | - David Taylor
- College of Medicine, Center for Leadership and Innovation in Health Professions Education, Gulf Medical University, Ajman, United Arab Emirates
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Rai A, Nath Sharma B, Rabindrajit Luwang S, Nurujjaman M, Majhi S. Identifying extreme events in the stock market: A topological data analysis. CHAOS (WOODBURY, N.Y.) 2024; 34:103106. [PMID: 39352199 DOI: 10.1063/5.0220424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Accepted: 09/09/2024] [Indexed: 10/03/2024]
Abstract
This paper employs Topological Data Analysis (TDA) to detect extreme events (EEs) in the stock market at a continental level. Previous approaches, which analyzed stock indices separately, could not detect EEs for multiple time series in one go. TDA provides a robust framework for such analysis and identifies the EEs during the crashes for different indices. The TDA analysis shows that L1, L2 norms and Wasserstein distance (WD) of the world leading indices rise abruptly during the crashes, surpassing a threshold of μ+4∗σ, where μ and σ are the mean and the standard deviation of norm or WD, respectively. Our study identified the stock index crashes of the 2008 financial crisis and the COVID-19 pandemic across continents as EEs. Given that different sectors in an index behave differently, a sector-wise analysis was conducted during the COVID-19 pandemic for the Indian stock market. The sector-wise results show that after the occurrence of EE, we have observed strong crashes surpassing μ+2∗σ for an extended period for the banking, automobile, IT, realty, energy, and metal sectors. While for the pharmaceutical and FMCG sectors, no significant spikes were noted. Hence, TDA also proves successful in identifying the duration of shocks after the occurrence of EEs. This also indicates that the banking sector continued to face stress and remained volatile even after the crash. This study gives us the applicability of TDA as a powerful analytical tool to study EEs in various fields.
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Affiliation(s)
- Anish Rai
- Department of Physics, National Institute of Technology Sikkim, Ravangla, Sikkim 737139, India
| | - Buddha Nath Sharma
- Department of Physics, National Institute of Technology Sikkim, Ravangla, Sikkim 737139, India
| | | | - Md Nurujjaman
- Department of Physics, National Institute of Technology Sikkim, Ravangla, Sikkim 737139, India
| | - Sushovan Majhi
- Data Science Program, George Washington University, Washington, DC 20052, USA
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Kim JI, Manuele A, Maguire F, Zaheer R, McAllister TA, Beiko RG. Identification of key drivers of antimicrobial resistance in Enterococcus using machine learning. Can J Microbiol 2024; 70:446-460. [PMID: 39079170 DOI: 10.1139/cjm-2024-0049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2024]
Abstract
With antimicrobial resistance (AMR) rapidly evolving in pathogens, quick and accurate identification of genetic determinants of phenotypic resistance is essential for improving surveillance, stewardship, and clinical mitigation. Machine learning (ML) models show promise for AMR prediction in diagnostics but require a deep understanding of internal processes to use effectively. Our study utilised AMR gene, pangenomic, and predicted plasmid features from 647 Enterococcus faecium and Enterococcus faecalis genomes across the One Health continuum, along with corresponding resistance phenotypes, to develop interpretive ML classifiers. Vancomycin resistance could be predicted with 99% accuracy with AMR gene features, 98% with pangenome features, and 96% with plasmid clusters. Top pangenome features overlapped with the resistance genes of the vanA operon, which are often laterally transmitted via plasmids. Doxycycline resistance prediction achieved approximately 92% accuracy with pangenome features, with the top feature being elements of Tn916 conjugative transposon, a tet(M) carrier. Erythromycin resistance prediction models achieved about 90% accuracy, but top features were negatively correlated with resistance due to the confounding effect of population structure. This work demonstrates the importance of reviewing ML models' features to discern biological relevance even when achieving high-performance metrics. Our workflow offers the potential to propose hypotheses for experimental testing, enhancing the understanding of AMR mechanisms, which are crucial for combating the AMR crisis.
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Affiliation(s)
- Jee In Kim
- Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
- Institute for Comparative Genomics, Dalhousie University, Halifax, NS, Canada
- Agriculture and Agri-Food Canada, Lethbridge, AB, Canada
| | - Alexander Manuele
- Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
- Institute for Comparative Genomics, Dalhousie University, Halifax, NS, Canada
| | - Finlay Maguire
- Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
- Institute for Comparative Genomics, Dalhousie University, Halifax, NS, Canada
- Department of Community Health and Epidemiology, Dalhousie University, Faculty of Medicine, Halifax, NS, Canada
| | - Rahat Zaheer
- Agriculture and Agri-Food Canada, Lethbridge, AB, Canada
| | | | - Robert G Beiko
- Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
- Institute for Comparative Genomics, Dalhousie University, Halifax, NS, Canada
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Bhattarai K, Oh IY, Sierra JM, Tang J, Payne PRO, Abrams Z, Lai AM. Leveraging GPT-4 for identifying cancer phenotypes in electronic health records: a performance comparison between GPT-4, GPT-3.5-turbo, Flan-T5, Llama-3-8B, and spaCy's rule-based and machine learning-based methods. JAMIA Open 2024; 7:ooae060. [PMID: 38962662 PMCID: PMC11221943 DOI: 10.1093/jamiaopen/ooae060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 06/12/2024] [Accepted: 06/18/2024] [Indexed: 07/05/2024] Open
Abstract
Objective Accurately identifying clinical phenotypes from Electronic Health Records (EHRs) provides additional insights into patients' health, especially when such information is unavailable in structured data. This study evaluates the application of OpenAI's Generative Pre-trained Transformer (GPT)-4 model to identify clinical phenotypes from EHR text in non-small cell lung cancer (NSCLC) patients. The goal was to identify disease stages, treatments and progression utilizing GPT-4, and compare its performance against GPT-3.5-turbo, Flan-T5-xl, Flan-T5-xxl, Llama-3-8B, and 2 rule-based and machine learning-based methods, namely, scispaCy and medspaCy. Materials and Methods Phenotypes such as initial cancer stage, initial treatment, evidence of cancer recurrence, and affected organs during recurrence were identified from 13 646 clinical notes for 63 NSCLC patients from Washington University in St. Louis, Missouri. The performance of the GPT-4 model is evaluated against GPT-3.5-turbo, Flan-T5-xxl, Flan-T5-xl, Llama-3-8B, medspaCy, and scispaCy by comparing precision, recall, and micro-F1 scores. Results GPT-4 achieved higher F1 score, precision, and recall compared to Flan-T5-xl, Flan-T5-xxl, Llama-3-8B, medspaCy, and scispaCy's models. GPT-3.5-turbo performed similarly to that of GPT-4. GPT, Flan-T5, and Llama models were not constrained by explicit rule requirements for contextual pattern recognition. spaCy models relied on predefined patterns, leading to their suboptimal performance. Discussion and Conclusion GPT-4 improves clinical phenotype identification due to its robust pre-training and remarkable pattern recognition capability on the embedded tokens. It demonstrates data-driven effectiveness even with limited context in the input. While rule-based models remain useful for some tasks, GPT models offer improved contextual understanding of the text, and robust clinical phenotype extraction.
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Affiliation(s)
- Kriti Bhattarai
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, United States
- Department of Computer Science, Washington University in St Louis, St. Louis, MO 63110, United States
| | - Inez Y Oh
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Jonathan Moran Sierra
- Medical Scientist Training Program, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Jonathan Tang
- Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Philip R O Payne
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, United States
- Department of Computer Science, Washington University in St Louis, St. Louis, MO 63110, United States
| | - Zach Abrams
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Albert M Lai
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, United States
- Department of Computer Science, Washington University in St Louis, St. Louis, MO 63110, United States
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Bourdillon AT. Computer Vision-Radiomics & Pathognomics. Otolaryngol Clin North Am 2024; 57:719-751. [PMID: 38910065 DOI: 10.1016/j.otc.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.
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Affiliation(s)
- Alexandra T Bourdillon
- Department of Otolaryngology-Head & Neck Surgery, University of California-San Francisco, San Francisco, CA 94115, USA.
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Fleming SM, Shea N. Quality space computations for consciousness. Trends Cogn Sci 2024; 28:896-906. [PMID: 39025769 DOI: 10.1016/j.tics.2024.06.007] [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: 01/31/2024] [Revised: 06/18/2024] [Accepted: 06/19/2024] [Indexed: 07/20/2024]
Abstract
The quality space hypothesis about conscious experience proposes that conscious sensory states are experienced in relation to other possible sensory states. For instance, the colour red is experienced as being more like orange, and less like green or blue. Recent empirical findings suggest that subjective similarity space can be explained in terms of similarities in neural activation patterns. Here, we consider how localist, workspace, and higher-order theories of consciousness can accommodate claims about the qualitative character of experience and functionally support a quality space. We review existing empirical evidence for each of these positions, and highlight novel experimental tools, such as altering local activation spaces via brain stimulation or behavioural training, that can distinguish these accounts.
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Affiliation(s)
- Stephen M Fleming
- Wellcome Centre for Human Neuroimaging, University College London, London, UK; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK; Department of Experimental Psychology, University College London, London, UK; Canadian Institute for Advanced Research (CIFAR), Brain, Mind, and Consciousness Program, Toronto, ON, Canada.
| | - Nicholas Shea
- Institute of Philosophy, School of Advanced Study, University of London, London, UK; Faculty of Philosophy, University of Oxford, Oxford, UK.
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Polizzi A, Quinzi V, Lo Giudice A, Marzo G, Leonardi R, Isola G. Accuracy of Artificial Intelligence Models in the Prediction of Periodontitis: A Systematic Review. JDR Clin Trans Res 2024; 9:312-324. [PMID: 38589339 DOI: 10.1177/23800844241232318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024] Open
Abstract
INTRODUCTION Periodontitis is the main cause of tooth loss and is related to many systemic diseases. Artificial intelligence (AI) in periodontics has the potential to improve the accuracy of risk assessment and provide personalized treatment planning for patients with periodontitis. This systematic review aims to examine the actual evidence on the accuracy of various AI models in predicting periodontitis. METHODS Using a mix of MeSH keywords and free text words pooled by Boolean operators ('AND', 'OR'), a search strategy without a time frame setting was conducted on the following databases: Web of Science, ProQuest, PubMed, Scopus, and IEEE Explore. The QUADAS-2 risk of bias assessment was then performed. RESULTS From a total of 961 identified records screened, 8 articles were included for qualitative analysis: 4 studies showed an overall low risk of bias, 2 studies an unclear risk, and the remaining 2 studies a high risk. The most employed algorithms for periodontitis prediction were artificial neural networks, followed by support vector machines, decision trees, logistic regression, and random forest. The models showed good predictive performance for periodontitis according to different evaluation metrics, but the presented methods were heterogeneous. CONCLUSIONS AI algorithms may improve in the future the accuracy and reliability of periodontitis prediction. However, to date, most of the studies had a retrospective design and did not consider the most modern deep learning networks. Although the available evidence is limited by a lack of standardized data collection and protocols, the potential benefits of using AI in periodontics are significant and warrant further research and development in this area. KNOWLEDGE TRANSFER STATEMENT The use of AI in periodontics can lead to more accurate diagnosis and treatment planning, as well as improved patient education and engagement. Despite the current challenges and limitations of the available evidence, particularly the lack of standardized data collection and analysis protocols, the potential benefits of using AI in periodontics are significant and warrant further research and development in this area.
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Affiliation(s)
- A Polizzi
- Department of General Surgery and Surgical-Medical Specialties, School of Dentistry, University of Catania, Catania, Italy
| | - V Quinzi
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - A Lo Giudice
- Department of General Surgery and Surgical-Medical Specialties, School of Dentistry, University of Catania, Catania, Italy
| | - G Marzo
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - R Leonardi
- Department of General Surgery and Surgical-Medical Specialties, School of Dentistry, University of Catania, Catania, Italy
| | - G Isola
- Department of General Surgery and Surgical-Medical Specialties, School of Dentistry, University of Catania, Catania, Italy
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Sajdeya R, Narouze S. Harnessing artificial intelligence for predicting and managing postoperative pain: a narrative literature review. Curr Opin Anaesthesiol 2024; 37:604-615. [PMID: 39011674 DOI: 10.1097/aco.0000000000001408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
PURPOSE OF REVIEW This review examines recent research on artificial intelligence focusing on machine learning (ML) models for predicting postoperative pain outcomes. We also identify technical, ethical, and practical hurdles that demand continued investigation and research. RECENT FINDINGS Current ML models leverage diverse datasets, algorithmic techniques, and validation methods to identify predictive biomarkers, risk factors, and phenotypic signatures associated with increased acute and chronic postoperative pain and persistent opioid use. ML models demonstrate satisfactory performance to predict pain outcomes and their prognostic trajectories, identify modifiable risk factors and at-risk patients who benefit from targeted pain management strategies, and show promise in pain prevention applications. However, further evidence is needed to evaluate the reliability, generalizability, effectiveness, and safety of ML-driven approaches before their integration into perioperative pain management practices. SUMMARY Artificial intelligence (AI) has the potential to enhance perioperative pain management by providing more accurate predictive models and personalized interventions. By leveraging ML algorithms, clinicians can better identify at-risk patients and tailor treatment strategies accordingly. However, successful implementation needs to address challenges in data quality, algorithmic complexity, and ethical and practical considerations. Future research should focus on validating AI-driven interventions in clinical practice and fostering interdisciplinary collaboration to advance perioperative care.
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Affiliation(s)
- Ruba Sajdeya
- Department of Anesthesiology, Duke University School of Medicine, Durham, North Carolina
| | - Samer Narouze
- Division of Pain Medicine, University Hospitals Medical Center, Cleveland, Ohio, USA
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Rikhari H, Baidya Kayal E, Ganguly S, Sasi A, Sharma S, Antony A, Rangarajan K, Bakhshi S, Kandasamy D, Mehndiratta A. Improving lung nodule segmentation in thoracic CT scans through the ensemble of 3D U-Net models. Int J Comput Assist Radiol Surg 2024; 19:2089-2099. [PMID: 39044036 DOI: 10.1007/s11548-024-03222-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 06/24/2024] [Indexed: 07/25/2024]
Abstract
PURPOSE The current study explores the application of 3D U-Net architectures combined with Inception and ResNet modules for precise lung nodule detection through deep learning-based segmentation technique. This investigation is motivated by the objective of developing a Computer-Aided Diagnosis (CAD) system for effective diagnosis and prognostication of lung nodules in clinical settings. METHODS The proposed method trained four different 3D U-Net models on the retrospective dataset obtained from AIIMS Delhi. To augment the training dataset, affine transformations and intensity transforms were utilized. Preprocessing steps included CT scan voxel resampling, intensity normalization, and lung parenchyma segmentation. Model optimization utilized a hybrid loss function that combined Dice Loss and Focal Loss. The model performance of all four 3D U-Nets was evaluated patient-wise using dice coefficient and Jaccard coefficient, then averaged to obtain the average volumetric dice coefficient (DSCavg) and average Jaccard coefficient (IoUavg) on a test dataset comprising 53 CT scans. Additionally, an ensemble approach (Model-V) was utilized featuring 3D U-Net (Model-I), ResNet (Model-II), and Inception (Model-III) 3D U-Net architectures, combined with two distinct patch sizes for further investigation. RESULTS The ensemble of models obtained the highest DSCavg of 0.84 ± 0.05 and IoUavg of 0.74 ± 0.06 on the test dataset, compared against individual models. It mitigated false positives, overestimations, and underestimations observed in individual U-Net models. Moreover, the ensemble of models reduced average false positives per scan in the test dataset (1.57 nodules/scan) compared to individual models (2.69-3.39 nodules/scan). CONCLUSIONS The suggested ensemble approach presents a strong and effective strategy for automatically detecting and delineating lung nodules, potentially aiding CAD systems in clinical settings. This approach could assist radiologists in laborious and meticulous lung nodule detection tasks in CT scans, improving lung cancer diagnosis and treatment planning.
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Affiliation(s)
- Himanshu Rikhari
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India
| | - Esha Baidya Kayal
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India
| | - Shuvadeep Ganguly
- Medical Oncology, Dr. B.R.A. IRCH, All India Institute of Medical Sciences New Delhi, New Delhi, India
| | - Archana Sasi
- Medical Oncology, Dr. B.R.A. IRCH, All India Institute of Medical Sciences New Delhi, New Delhi, India
| | - Swetambri Sharma
- Medical Oncology, Dr. B.R.A. IRCH, All India Institute of Medical Sciences New Delhi, New Delhi, India
| | - Ajith Antony
- All India Institute of Medical Sciences New Delhi, New Delhi, India
| | - Krithika Rangarajan
- Dr. B.R.A. IRCH, All India Institute of Medical Sciences New Delhi, New Delhi, India
| | - Sameer Bakhshi
- Medical Oncology, Dr. B.R.A. IRCH, All India Institute of Medical Sciences New Delhi, New Delhi, India
| | | | - Amit Mehndiratta
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
- Department of Biomedical Engineering, All India Institute of Medical Sciences New Delhi, New Delhi, India.
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Shawli L, Alsobhi M, Faisal Chevidikunnan M, Rosewilliam S, Basuodan R, Khan F. Physical therapists' perceptions and attitudes towards artificial intelligence in healthcare and rehabilitation: A qualitative study. Musculoskelet Sci Pract 2024; 73:103152. [PMID: 39067366 DOI: 10.1016/j.msksp.2024.103152] [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: 06/14/2024] [Revised: 07/17/2024] [Accepted: 07/23/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is being introduced to rehabilitation practices, and it can optimize the patient's outcome through their ability to design personalized care strategies and interventions. OBJECTIVES To understand the attitudes and perceptions of physical therapy professionals on the use of AI in rehabilitation in regard to treatment planning, diagnosis, outcome prediction, and advantages and disadvantages. DESIGN AND METHODS This paper followed an exploratory, qualitative research design. Semi-structured, one-to-one interviews were conducted with participants of different experience levels and specialties in physical therapy. Results were evaluated using thematic analysis. RESULTS Four themes were identified: (i) perceptions of AI and its applications in healthcare services, (ii) impact on the workforce (iii) considerations around implementing AI within rehabilitation and (iv) AI, and the fast-approaching future. Participants shared views on the potential impact of AI on rehabilitation practices, such as aiding the decision-making process, saving time and effort of both the therapist and patients. Participants have stressed on potential pitfalls that still need to be considered, such as patient data privacy, potential loss of patient-healthcare practitioner relationship, ethical concerns regarding overreliance on these applications and how that might hinder effective patient care. CONCLUSION The findings add to the literature about physical therapists' understanding regarding the use of AI in patient care. Several concerns were raised to the adoption of AI, including concerns about patient privacy, and ethical concerns. Based on the study findings, researchers emphasize the importance of establishing guidelines when incorporating AI in rehabilitation to improve the therapist's knowledge and skills.
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Affiliation(s)
- Lama Shawli
- Department of Occupational Therapy, College of Applied Medical Sciences, King Saud Bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia
| | - Mashael Alsobhi
- Department of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.
| | - Mohamed Faisal Chevidikunnan
- Department of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Sheeba Rosewilliam
- School of Sports, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Reem Basuodan
- Department of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Fayaz Khan
- Department of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
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49
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Kojima K, Tadaka S, Okamura Y, Kinoshita K. Two-stage strategy using denoising autoencoders for robust reference-free genotype imputation with missing input genotypes. J Hum Genet 2024; 69:511-518. [PMID: 38918526 PMCID: PMC11422160 DOI: 10.1038/s10038-024-01261-6] [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] [Received: 11/07/2023] [Revised: 04/16/2024] [Accepted: 05/13/2024] [Indexed: 06/27/2024]
Abstract
Widely used genotype imputation methods are based on the Li and Stephens model, which assumes that new haplotypes can be represented by modifying existing haplotypes in a reference panel through mutations and recombinations. These methods use genotypes from SNP arrays as inputs to estimate haplotypes that align with the input genotypes by analyzing recombination patterns within a reference panel, and then infer unobserved variants. While these methods require reference panels in an identifiable form, their public use is limited due to privacy and consent concerns. One strategy to overcome these limitations is to use de-identified haplotype information, such as summary statistics or model parameters. Advances in deep learning (DL) offer the potential to develop imputation methods that use haplotype information in a reference-free manner by handling it as model parameters, while maintaining comparable imputation accuracy to methods based on the Li and Stephens model. Here, we provide a brief introduction to DL-based reference-free genotype imputation methods, including RNN-IMP, developed by our research group. We then evaluate the performance of RNN-IMP against widely-used Li and Stephens model-based imputation methods in terms of accuracy (R2), using the 1000 Genomes Project Phase 3 dataset and corresponding simulated Omni2.5 SNP genotype data. Although RNN-IMP is sensitive to missing values in input genotypes, we propose a two-stage imputation strategy: missing genotypes are first imputed using denoising autoencoders; RNN-IMP then processes these imputed genotypes. This approach restores the imputation accuracy that is degraded by missing values, enhancing the practical use of RNN-IMP.
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Affiliation(s)
- Kaname Kojima
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.
| | - Shu Tadaka
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Yasunobu Okamura
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-0873, Japan
| | - Kengo Kinoshita
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-0873, Japan.
- Graduate School of Information Sciences, Tohoku University, 6-3-09 Aza-Aoba, Aramaki, Aoba-ku, Sendai, Miyagi, 980-8579, Japan.
- Institute of Development, Aging and Cancer, Tohoku University, 4-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.
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50
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Naito T, Okada Y. Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology. J Hum Genet 2024; 69:481-486. [PMID: 38225263 PMCID: PMC11422162 DOI: 10.1038/s10038-023-01213-6] [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] [Received: 09/13/2023] [Revised: 11/23/2023] [Accepted: 12/04/2023] [Indexed: 01/17/2024]
Abstract
The imputation of unmeasured genotypes is essential in human genetic research, particularly in enhancing the power of genome-wide association studies and conducting subsequent fine-mapping. Recently, several deep learning-based genotype imputation methods for genome-wide variants with the capability of learning complex linkage disequilibrium patterns have been developed. Additionally, deep learning-based imputation has been applied to a distinct genomic region known as the major histocompatibility complex, referred to as HLA imputation. Despite their various advantages, the current deep learning-based genotype imputation methods do have certain limitations and have not yet become standard. These limitations include the modest accuracy improvement over statistical and conventional machine learning-based methods. However, their benefits include other aspects, such as their "reference-free" nature, which ensures complete privacy protection, and their higher computational efficiency. Furthermore, the continuing evolution of deep learning technologies is expected to contribute to further improvements in prediction accuracy and usability in the future.
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Affiliation(s)
- Tatsuhiko Naito
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita-shi, Osaka, 565-0871, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan.
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita-shi, Osaka, 565-0871, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, 2-2, Yamadaoka, Suita-shi, Osaka, 565-0871, Japan
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, 2-2, Yamadaoka, Suita-shi, Osaka, 565-0871, Japan
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