1
|
Croucher C, Powell W, Stevens B, Miller-Dicks M, Powell V, Wiltshire TJ, Spronck P. LoCoMoTe - A Framework for Classification of Natural Locomotion in VR by Task, Technique and Modality. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024; 30:5765-5781. [PMID: 37695974 DOI: 10.1109/tvcg.2023.3313439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
Virtual reality (VR) research has provided overviews of locomotion techniques, how they work, their strengths and overall user experience. Considerable research has investigated new methodologies, particularly machine learning to develop redirection algorithms. To best support the development of redirection algorithms through machine learning, we must understand how best to replicate human navigation and behaviour in VR, which can be supported by the accumulation of results produced through live-user experiments. However, it can be difficult to identify, select and compare relevant research without a pre-existing framework in an ever-growing research field. Therefore, this work aimed to facilitate the ongoing structuring and comparison of the VR-based natural walking literature by providing a standardised framework for researchers to utilise. We applied thematic analysis to study methodology descriptions from 140 VR-based papers that contained live-user experiments. From this analysis, we developed the LoCoMoTe framework with three themes: navigational decisions, technique implementation, and modalities. The LoCoMoTe framework provides a standardised approach to structuring and comparing experimental conditions. The framework should be continually updated to categorise and systematise knowledge and aid in identifying research gaps and discussions.
Collapse
|
2
|
Colato E, Stutters J, Narayanan S, Arnold DL, Chataway J, Gandini Wheeler-Kingshott CAM, Barkhof F, Ciccarelli O, Eshaghi A, Chard DT. Longitudinal network-based brain grey matter MRI measures are clinically relevant and sensitive to treatment effects in multiple sclerosis. Brain Commun 2024; 6:fcae234. [PMID: 39077376 PMCID: PMC11285187 DOI: 10.1093/braincomms/fcae234] [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: 07/17/2023] [Revised: 05/24/2024] [Accepted: 07/17/2024] [Indexed: 07/31/2024] Open
Abstract
In multiple sclerosis clinical trials, MRI outcome measures are typically extracted at a whole-brain level, but pathology is not homogeneous across the brain and so whole-brain measures may overlook regional treatment effects. Data-driven methods, such as independent component analysis, have shown promise in identifying regional disease effects but can only be computed at a group level and cannot be applied prospectively. The aim of this work was to develop a technique to extract longitudinal independent component analysis network-based measures of co-varying grey matter volumes, derived from T1-weighted volumetric MRI, in individual study participants, and assess their association with disability progression and treatment effects in clinical trials. We used longitudinal MRI and clinical data from 5089 participants (22 045 visits) with multiple sclerosis from eight clinical trials. We included people with relapsing-remitting, primary and secondary progressive multiple sclerosis. We used data from five negative clinical trials (2764 participants, 13 222 visits) to extract the independent component analysis-based measures. We then trained and cross-validated a least absolute shrinkage and selection operator regression model (which can be applied prospectively to previously unseen data) to predict the independent component analysis measures from the same regional MRI volume measures and applied it to data from three positive clinical trials (2325 participants, 8823 visits). We used nested mixed-effect models to determine how networks differ across multiple sclerosis phenotypes are associated with disability progression and to test sensitivity to treatment effects. We found 17 consistent patterns of co-varying regional volumes. In the training cohort, volume loss was faster in four networks in people with secondary progressive compared with relapsing-remitting multiple sclerosis and three networks with primary progressive multiple sclerosis. Volume changes were faster in secondary compared with primary progressive multiple sclerosis in four networks. In the combined positive trials cohort, eight independent component analysis networks and whole-brain grey matter volume measures showed treatment effects, and the magnitude of treatment-placebo differences in the network-based measures was consistently greater than with whole-brain grey matter volume measures. Longitudinal network-based analysis of grey matter volume changes is feasible using clinical trial data, showing differences cross-sectionally and longitudinally between multiple sclerosis phenotypes, associated with disability progression, and treatment effects. Future work is required to understand the pathological mechanisms underlying these regional changes.
Collapse
Affiliation(s)
- Elisa Colato
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK
| | - Jonathan Stutters
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK
| | - Sridar Narayanan
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Douglas L Arnold
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Jeremy Chataway
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK
- National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, W1T 7DN, UK
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK
- Brain Connectivity Centre, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Mondino Foundation, Pavia, 27100, Italy
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, 27100, Italy
| | - Frederik Barkhof
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK
- National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, W1T 7DN, UK
- Department of Radiology and Nuclear Medicine, Vrije Universiteit (VU) Medical Centre, Amsterdam, 1081 HZ, The Netherlands
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, WC1V 6LJ, UK
| | - Olga Ciccarelli
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK
- National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, W1T 7DN, UK
| | - Arman Eshaghi
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, WC1V 6LJ, UK
| | - Declan T Chard
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK
- National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, W1T 7DN, UK
| |
Collapse
|
3
|
Abbas MKG, Rassam A, Karamshahi F, Abunora R, Abouseada M. The Role of AI in Drug Discovery. Chembiochem 2024; 25:e202300816. [PMID: 38735845 DOI: 10.1002/cbic.202300816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/14/2024]
Abstract
The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.
Collapse
Affiliation(s)
- M K G Abbas
- Center for Advanced Materials, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Abrar Rassam
- Secondary Education, Educational Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Fatima Karamshahi
- Department of Chemistry and Earth Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Rehab Abunora
- Faculty of Medicine, General Medicine and Surgery, Helwan University, Cairo, Egypt
| | - Maha Abouseada
- Department of Chemistry and Earth Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
| |
Collapse
|
4
|
Maita KC, Avila FR, Torres-Guzman RA, Garcia JP, De Sario Velasquez GD, Borna S, Brown SA, Haider CR, Ho OS, Forte AJ. The usefulness of artificial intelligence in breast reconstruction: a systematic review. Breast Cancer 2024; 31:562-571. [PMID: 38619786 DOI: 10.1007/s12282-024-01582-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 03/30/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND Artificial Intelligence (AI) offers an approach to predictive modeling. The model learns to determine specific patterns of undesirable outcomes in a dataset. Therefore, a decision-making algorithm can be built based on these patterns to prevent negative results. This systematic review aimed to evaluate the usefulness of AI in breast reconstruction. METHODS A systematic review was conducted in August 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. MEDLINE, EMBASE, SCOPUS, and Google Scholar online databases were queried to capture all publications studying the use of artificial intelligence in breast reconstruction. RESULTS A total of 23 studies were full text-screened after removing duplicates, and twelve articles fulfilled our inclusion criteria. The Machine Learning algorithms applied for neuropathic pain, lymphedema diagnosis, microvascular abdominal flap failure, donor site complications associated to muscle sparing Transverse Rectus Abdominis flap, surgical complications, financial toxicity, and patient-reported outcomes after breast surgery demonstrated that AI is a helpful tool to accurately predict patient results. In addition, one study used Computer Vision technology to assist in Deep Inferior Epigastric Perforator Artery detection for flap design, considerably reducing the preoperative time compared to manual identification. CONCLUSIONS In breast reconstruction, AI can help the surgeon by optimizing the perioperative patients' counseling to predict negative outcomes, allowing execution of timely interventions and reducing the postoperative burden, which leads to obtaining the most successful results and improving patient satisfaction.
Collapse
Affiliation(s)
- Karla C Maita
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | - Francisco R Avila
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | | | - John P Garcia
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | | | - Sahar Borna
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | - Sally A Brown
- Department of Administration, Mayo Clinic, Jacksonville, FL, USA
| | - Clifton R Haider
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Olivia S Ho
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | - Antonio Jorge Forte
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA.
| |
Collapse
|
5
|
Park JH, Chun M, Bae SH, Woo J, Chon E, Kim HJ. Factors influencing psychological distress among breast cancer survivors using machine learning techniques. Sci Rep 2024; 14:15052. [PMID: 38956137 PMCID: PMC11219858 DOI: 10.1038/s41598-024-65132-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: 04/24/2024] [Accepted: 06/17/2024] [Indexed: 07/04/2024] Open
Abstract
Breast cancer is the most commonly diagnosed cancer among women worldwide. Breast cancer patients experience significant distress relating to their diagnosis and treatment. Managing this distress is critical for improving the lifespan and quality of life of breast cancer survivors. This study aimed to assess the level of distress in breast cancer survivors and analyze the variables that significantly affect distress using machine learning techniques. A survey was conducted with 641 adult breast cancer patients using the National Comprehensive Cancer Network Distress Thermometer tool. Participants identified various factors that caused distress. Five machine learning models were used to predict the classification of patients into mild and severe distress groups. The survey results indicated that 57.7% of the participants experienced severe distress. The top-three best-performing models indicated that depression, dealing with a partner, housing, work/school, and fatigue are the primary indicators. Among the emotional problems, depression, fear, worry, loss of interest in regular activities, and nervousness were determined as significant predictive factors. Therefore, machine learning models can be effectively applied to determine various factors influencing distress in breast cancer patients who have completed primary treatment, thereby identifying breast cancer patients who are vulnerable to distress in clinical settings.
Collapse
Affiliation(s)
- Jin-Hee Park
- College of Nursing, Research Institute of Nursing Science, Ajou University, Suwon, Republic of Korea
| | - Misun Chun
- Department of Radiation Oncology, School of Medicine, Ajou University, Suwon, Republic of Korea
| | - Sun Hyoung Bae
- College of Nursing, Research Institute of Nursing Science, Ajou University, Suwon, Republic of Korea
| | - Jeonghee Woo
- Management Team, Cancer Center, Gyeonggi Regional Cancer Center, Suwon, Republic of Korea
| | - Eunae Chon
- Management Team, Cancer Center, Gyeonggi Regional Cancer Center, Suwon, Republic of Korea
| | - Hee Jun Kim
- College of Nursing, Ajou University, 164, World Cup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea.
| |
Collapse
|
6
|
Fan L, Shen Y, Lou D, Gu N. Progress in the Computer-Aided Analysis in Multiple Aspects of Nanocatalysis Research. Adv Healthc Mater 2024:e2401576. [PMID: 38936401 DOI: 10.1002/adhm.202401576] [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: 04/29/2024] [Revised: 06/08/2024] [Indexed: 06/29/2024]
Abstract
Making the utmost of the differences and advantages of multiple disciplines, interdisciplinary integration breaks the science boundaries and accelerates the progress in mutual quests. As an organic connection of material science, enzymology, and biomedicine, nanozyme-related research is further supported by computer technology, which injects in new vitality, and contributes to in-depth understanding, unprecedented insights, and broadened application possibilities. Utilizing computer-aided first-principles method, high-speed and high-throughput mathematic, physic, and chemic models are introduced to perform atomic-level kinetic analysis for nanocatalytic reaction process, and theoretically illustrate the underlying nanozymetic mechanism and structure-function relationship. On this basis, nanozymes with desirable properties can be designed and demand-oriented synthesized without repeated trial-and-error experiments. Besides that, computational analysis and device also play an indispensable role in nanozyme-based detecting methods to realize automatic readouts with improved accuracy and reproducibility. Here, this work focuses on the crossing of nanocatalysis research and computational technology, to inspire the research in computer-aided analysis in nanozyme field to a greater extent.
Collapse
Affiliation(s)
- Lin Fan
- Medical School of Nanjing University, Nanjing, 210093, P. R. China
- School of Integrated Circuit Science and Engineering (Industry-Education Integration School), Nanjing University of Posts and Telecommunications, Nanjing, 210023, P. R. China
| | - Yilei Shen
- School of Integrated Circuit Science and Engineering (Industry-Education Integration School), Nanjing University of Posts and Telecommunications, Nanjing, 210023, P. R. China
| | - Doudou Lou
- Nanjing Institute for Food and Drug Control, Nanjing, 211198, P. R. China
| | - Ning Gu
- Medical School of Nanjing University, Nanjing, 210093, P. R. China
| |
Collapse
|
7
|
Bassani D, Parrott NJ, Manevski N, Zhang JD. Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules. Expert Opin Drug Discov 2024; 19:683-698. [PMID: 38727016 DOI: 10.1080/17460441.2024.2348157] [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: 10/23/2023] [Accepted: 04/23/2024] [Indexed: 05/22/2024]
Abstract
INTRODUCTION Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary. AREAS COVERED This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review. EXPERT OPINION ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.
Collapse
Affiliation(s)
- Davide Bassani
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Neil John Parrott
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Nenad Manevski
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Jitao David Zhang
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| |
Collapse
|
8
|
Xue L, Singla RK, He S, Arrasate S, González-Díaz H, Miao L, Shen B. Warfarin-A natural anticoagulant: A review of research trends for precision medication. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 128:155479. [PMID: 38493714 DOI: 10.1016/j.phymed.2024.155479] [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: 07/27/2023] [Revised: 01/29/2024] [Accepted: 02/22/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND Warfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of these factors on individual variability. PURPOSE The aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics and machine learning models of personalized warfarin dosage. METHODS A bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology, mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were added to retrieve the literature. RESULTS Bibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms. There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models, and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains biological or pharmacological mechanisms in structure. The process of pharmacometric model development is very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more mathematical and have less biological interpretation. However, it is faster, more efficient, and less time-consuming. Most published models of machine learning algorithms were established based on cross-sectional data sourced from the database. CONCLUSION Future research on personalized warfarin medication should focus on combining the advantages of machine learning and pharmacometrics algorithms to establish a more robust warfarin dosage algorithm. Randomized controlled trials should be performed to evaluate the established algorithm of warfarin dosage. Moreover, a more user-friendly and accessible warfarin precision medicine platform should be developed.
Collapse
Affiliation(s)
- Ling Xue
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China; Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, China; Department of Pharmacology, Faculty of Medicine, University of The Basque Country (UPV/EHU), Bilbao, Basque Country, Spain
| | - Rajeev K Singla
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China; School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab-144411, India
| | - Shan He
- IKERDATA S.l., ZITEK, University of The Basque Country (UPVEHU), Rectorate Building, 48940, Bilbao, Basque Country, Spain; Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Basque Country, Spain
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Basque Country, Spain
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Basque Country, Spain; BIOFISIKA: Basque Center for Biophysics CSIC, University of The Basque Country (UPV/EHU), Barrio Sarriena s/n, Leioa, Bizkaia 48940, Basque Country, Spain; IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Basque Country, Spain
| | - Liyan Miao
- Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, China; Institute for Interdisciplinary Drug Research and Translational Sciences, Soochow University, Suzhou, China; College of Pharmaceutical Sciences, Soochow University, Suzhou, China.
| | - Bairong Shen
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
| |
Collapse
|
9
|
Qiao L, Li H, Wang Z, Sun H, Feng G, Yin D. Machine learning based on SEER database to predict distant metastasis of thyroid cancer. Endocrine 2024; 84:1040-1050. [PMID: 38155324 DOI: 10.1007/s12020-023-03657-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 12/09/2023] [Indexed: 12/30/2023]
Abstract
OBJECTIVE Distant metastasis of thyroid cancer often indicates poor prognosis, and it is important to identify patients who have developed distant metastasis or are at high risk as early as possible. This paper aimed to predict distant metastasis of thyroid cancer through the construction of machine learning models to provide a reference for clinical diagnosis and treatment. MATERIALS & METHODS Data on demographic and clinicopathological characteristics of thyroid cancer patients between 2010 and 2015 were extracted from the National Institutes of Health (NIH) Surveillance, Epidemiology, and End Results (SEER) database. Our research used univariate and multivariate logistic models to screen independent risk factors, respectively. Decision Trees (DT), ElasticNet (ENET), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multilayer Perceptron (MLP), Radial Basis Function Support Vector Machine (RBFSVM) and seven machine learning models were compared and evaluated by the following metrics: the area under receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), sensitivity(also called recall), specificity, precision, accuracy and F1 score. Interpretable machine learning was used to identify possible correlation between variables and distant metastasis. RESULTS Independent risk factors for distant metastasis, including age, gender, race, marital status, histological type, capsular invasion, and number of lymph nodes metastases were screened by multifactorial regression analysis. Among the seven machine learning algorithms, RF was the best algorithm, with an AUC of 0.948, sensitivity of 0.919, accuracy of 0.845, and F1 score of 0.886 in the training set, and an AUC of 0.960, sensitivity of 0.929, accuracy of 0.906, and F1 score of 0.908 in the test set. CONCLUSIONS The machine learning model constructed in this study helps in the early diagnosis of distant thyroid metastases and helps physicians to make better decisions and medical interventions.
Collapse
Affiliation(s)
- Lixue Qiao
- Thyroid Surgery Department, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hao Li
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ziyang Wang
- Engineering Research Center of Multidisciplinary Diagnosis and Treatment of Thyroid Cancer of Henan Province, Zhengzhou, China
| | - Hanlin Sun
- Key Medicine Laboratory of Thyroid Cancer of Henan Province, Zhengzhou, China
| | - Guicheng Feng
- Thyroid Surgery Department, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Detao Yin
- Thyroid Surgery Department, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| |
Collapse
|
10
|
Maqsood A, Chen C, Jacobsson TJ. The Future of Material Scientists in an Age of Artificial Intelligence. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2401401. [PMID: 38477440 PMCID: PMC11109614 DOI: 10.1002/advs.202401401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 02/13/2024] [Indexed: 03/14/2024]
Abstract
Material science has historically evolved in tandem with advancements in technologies for characterization, synthesis, and computation. Another type of technology to add to this mix is machine learning (ML) and artificial intelligence (AI). Now increasingly sophisticated AI-models are seen that can solve progressively harder problems across a variety of fields. From a material science perspective, it is indisputable that machine learning and artificial intelligence offer a potent toolkit with the potential to substantially accelerate research efforts in areas such as the development and discovery of new functional materials. Less clear is how to best harness this development, what new skill sets will be required, and how it may affect established research practices. In this paper, those question are explored with respect to increasingly more sophisticated ML/AI-approaches. To structure the discussion, a conceptual framework of an AI-ladder is introduced. This AI-ladder ranges from basic data-fitting techniques to more advanced functionalities such as semi-autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, and the orchestration of specialized AI modules as stepping-stones toward general artificial intelligence. This ladder metaphor provides a hierarchical framework for contemplating the opportunities, challenges, and evolving skill sets required to stay competitive in the age of artificial intelligence.
Collapse
Affiliation(s)
- Ayman Maqsood
- Institute of Photoelectronic Thin Film Devices and TechnologyKey Laboratory of Photoelectronic Thin Film Devices and Technology of TianjinCollege of Electronic Information and Optical EngineeringNankai UniversityTianjin300350China
| | - Chen Chen
- Institute of Photoelectronic Thin Film Devices and TechnologyKey Laboratory of Photoelectronic Thin Film Devices and Technology of TianjinCollege of Electronic Information and Optical EngineeringNankai UniversityTianjin300350China
| | - T. Jesper Jacobsson
- Institute of Photoelectronic Thin Film Devices and TechnologyKey Laboratory of Photoelectronic Thin Film Devices and Technology of TianjinCollege of Electronic Information and Optical EngineeringNankai UniversityTianjin300350China
- Department of PhysicsChemistry and Biology (IFM)Linköping UniversityLinköping581 83Sweden
| |
Collapse
|
11
|
Sotirov S, Dimitrov I. Tumor-Derived Antigenic Peptides as Potential Cancer Vaccines. Int J Mol Sci 2024; 25:4934. [PMID: 38732150 PMCID: PMC11084719 DOI: 10.3390/ijms25094934] [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: 02/11/2024] [Revised: 04/25/2024] [Accepted: 04/28/2024] [Indexed: 05/13/2024] Open
Abstract
Peptide antigens derived from tumors have been observed to elicit protective immune responses, categorized as either tumor-associated antigens (TAAs) or tumor-specific antigens (TSAs). Subunit cancer vaccines incorporating these antigens have shown promise in inducing protective immune responses, leading to cancer prevention or eradication. Over recent years, peptide-based cancer vaccines have gained popularity as a treatment modality and are often combined with other forms of cancer therapy. Several clinical trials have explored the safety and efficacy of peptide-based cancer vaccines, with promising outcomes. Advancements in techniques such as whole-exome sequencing, next-generation sequencing, and in silico methods have facilitated the identification of antigens, making it increasingly feasible. Furthermore, the development of novel delivery methods and a deeper understanding of tumor immune evasion mechanisms have heightened the interest in these vaccines among researchers. This article provides an overview of novel insights regarding advancements in the field of peptide-based vaccines as a promising therapeutic avenue for cancer treatment. It summarizes existing computational methods for tumor neoantigen prediction, ongoing clinical trials involving peptide-based cancer vaccines, and recent studies on human vaccination experiments.
Collapse
Affiliation(s)
| | - Ivan Dimitrov
- Drug Design and Bioinformatics Lab, Faculty of Pharmacy, Medical University of Sofia, 2, Dunav Str., 1000 Sofia, Bulgaria;
| |
Collapse
|
12
|
Terranova N, Renard D, Shahin MH, Menon S, Cao Y, Hop CECA, Hayes S, Madrasi K, Stodtmann S, Tensfeldt T, Vaddady P, Ellinwood N, Lu J. Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices. Clin Pharmacol Ther 2024; 115:658-672. [PMID: 37716910 DOI: 10.1002/cpt.3053] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 09/11/2023] [Indexed: 09/18/2023]
Abstract
Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model-informed drug discovery and development (MID3). To foster a wider adoption and acceptance of these advanced algorithms, the Innovation and Quality (IQ) Consortium initiated the AI/ML working group in 2021 with the aim of promoting their acceptance among the broader scientific community as well as by regulatory agencies. By drawing insights from workshops organized by the working group and attended by key stakeholders across the biopharma industry, academia, and regulatory agencies, this white paper provides a perspective from the IQ Consortium. The range of applications covered in this white paper encompass the following thematic topics: (i) AI/ML-enabled Analytics for Pharmacometrics and Quantitative Systems Pharmacology (QSP) Workflows; (ii) Explainable Artificial Intelligence and its Applications in Disease Progression Modeling; (iii) Natural Language Processing (NLP) in Quantitative Pharmacology Modeling; and (iv) AI/ML Utilization in Drug Discovery. Additionally, the paper offers a set of best practices to ensure an effective and responsible use of AI, including considering the context of use, explainability and generalizability of models, and having human-in-the-loop. We believe that embracing the transformative power of AI in quantitative modeling while adopting a set of good practices can unlock new opportunities for innovation, increase efficiency, and ultimately bring benefits to patients.
Collapse
Affiliation(s)
- Nadia Terranova
- Quantitative Pharmacology, Merck KGaA, Lausanne, Switzerland
| | - Didier Renard
- Full Development Pharmacometrics, Novartis Pharma AG, Basel, Switzerland
| | | | - Sujatha Menon
- Clinical Pharmacology, Pfizer Inc., Groton, Connecticut, USA
| | - Youfang Cao
- Clinical Pharmacology and Translational Medicine, Eisai Inc., Nutley, New Jersey, USA
| | | | - Sean Hayes
- Quantitative Pharmacology & Pharmacometrics, Merck & Co. Inc., Rahway, New Jersey, USA
| | - Kumpal Madrasi
- Modeling & Simulation, Sanofi, Bridgewater, New Jersey, USA
| | - Sven Stodtmann
- Pharmacometrics, AbbVie Deutschland GmbH & Co. KG, Ludwigshafen, Germany
| | | | - Pavan Vaddady
- Quantitative Clinical Pharmacology, Daiichi Sankyo, Inc., Basking Ridge, New Jersey, USA
| | | | - James Lu
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
| |
Collapse
|
13
|
Hernández HO, Montoya F, Hernández-Herrera P, Díaz-Guerrero DS, Olveres J, Bloomfield-Gadêlha H, Darszon A, Escalante-Ramírez B, Corkidi G. Feature-based 3D+t descriptors of hyperactivated human sperm beat patterns. Heliyon 2024; 10:e26645. [PMID: 38444471 PMCID: PMC10912238 DOI: 10.1016/j.heliyon.2024.e26645] [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: 04/24/2023] [Revised: 11/23/2023] [Accepted: 02/16/2024] [Indexed: 03/07/2024] Open
Abstract
The flagellar movement of the mammalian sperm plays a crucial role in fertilization. In the female reproductive tract, human spermatozoa undergo a process called capacitation which promotes changes in their motility. Only capacitated spermatozoa may be hyperactivated and only those that transition to hyperactivated motility are capable of fertilizing the egg. Hyperactivated motility is characterized by asymmetric flagellar bends of greater amplitude and lower frequency. Historically, clinical fertilization studies have used two-dimensional analysis to classify sperm motility, despite the inherently three-dimensional (3D) nature of sperm motion. Recent research has described several 3D beating features of sperm flagella. However, the 3D motility pattern of hyperactivated spermatozoa has not yet been characterized. One of the main challenges in classifying these patterns in 3D is the lack of a ground-truth reference, as it can be difficult to visually assess differences in flagellar beat patterns. Additionally, it is worth noting that only a relatively small proportion, approximately 10-20% of sperm incubated under capacitating conditions exhibit hyperactivated motility. In this work, we used a multifocal image acquisition system that can acquire, segment, and track sperm flagella in 3D+t. We developed a feature-based vector that describes the spatio-temporal flagellar sperm motility patterns by an envelope of ellipses. The classification results obtained using our 3D feature-based descriptors can serve as potential label for future work involving deep neural networks. By using the classification results as labels, it will be possible to train a deep neural network to automatically classify spermatozoa based on their 3D flagellar beating patterns. We demonstrated the effectiveness of the descriptors by applying them to a dataset of human sperm cells and showing that they can accurately differentiate between non-hyperactivated and hyperactivated 3D motility patterns of the sperm cells. This work contributes to the understanding of 3D flagellar hyperactive motility patterns and provides a framework for research in the fields of human and animal fertility.
Collapse
Affiliation(s)
- Haydee O. Hernández
- Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México, UNAM, Ciudad de México, Mexico
- Laboratorio de Imágenes y Visión por Computadora, Instituto de Biotecnología, UNAM, Cuernavaca, Mexico
| | - Fernando Montoya
- Laboratorio de Imágenes y Visión por Computadora, Instituto de Biotecnología, UNAM, Cuernavaca, Mexico
| | - Paul Hernández-Herrera
- Laboratorio de Imágenes y Visión por Computadora, Instituto de Biotecnología, UNAM, Cuernavaca, Mexico
- Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico
| | - Dan S. Díaz-Guerrero
- Laboratorio de Imágenes y Visión por Computadora, Instituto de Biotecnología, UNAM, Cuernavaca, Mexico
| | - Jimena Olveres
- Departamento de Procesamiento de Señales, Facultad de Ingeniería, UNAM, Ciudad de México, Mexico
| | - Hermes Bloomfield-Gadêlha
- Department of Engineering Mathematics and Technology, Bristol Robotics Laboratory, University of Bristol, Bristol, United Kingdom
| | - Alberto Darszon
- Departamento de Genética del Desarrollo y Fisiología Molecular, Instituto de Biotecnología, UNAM, Ciudad de México, Mexico
| | - Boris Escalante-Ramírez
- Departamento de Procesamiento de Señales, Facultad de Ingeniería, UNAM, Ciudad de México, Mexico
| | - Gabriel Corkidi
- Laboratorio de Imágenes y Visión por Computadora, Instituto de Biotecnología, UNAM, Cuernavaca, Mexico
| |
Collapse
|
14
|
Stivi T, Padawer D, Dirini N, Nachshon A, Batzofin BM, Ledot S. Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome. J Clin Med 2024; 13:1505. [PMID: 38592696 PMCID: PMC10934889 DOI: 10.3390/jcm13051505] [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: 02/01/2024] [Revised: 02/29/2024] [Accepted: 03/03/2024] [Indexed: 04/10/2024] Open
Abstract
The management of mechanical ventilation (MV) remains a challenge in intensive care units (ICUs). The digitalization of healthcare and the implementation of artificial intelligence (AI) and machine learning (ML) has significantly influenced medical decision-making capabilities, potentially enhancing patient outcomes. Acute respiratory distress syndrome, an overwhelming inflammatory lung disease, is common in ICUs. Most patients require MV. Prolonged MV is associated with an increased length of stay, morbidity, and mortality. Shortening the MV duration has both clinical and economic benefits and emphasizes the need for better MV weaning management. AI and ML models can assist the physician in weaning patients from MV by providing predictive tools based on big data. Many ML models have been developed in recent years, dealing with this unmet need. Such models provide an important prediction regarding the success of the individual patient's MV weaning. Some AI models have shown a notable impact on clinical outcomes. However, there are challenges in integrating AI models into clinical practice due to the unfamiliar nature of AI for many physicians and the complexity of some AI models. Our review explores the evolution of weaning methods up to and including AI and ML as weaning aids.
Collapse
Affiliation(s)
- Tamar Stivi
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Dan Padawer
- Department of Pulmonary Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel;
- Faculty of Medicine, Hebrew University of Jerusalem, Campus Ein Kerem, Jerusalem 9112102, Israel
| | - Noor Dirini
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Akiva Nachshon
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Baruch M. Batzofin
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Stephane Ledot
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
- Faculty of Medicine, Hebrew University of Jerusalem, Campus Ein Kerem, Jerusalem 9112102, Israel
| |
Collapse
|
15
|
Burman CF, Hermansson E, Bock D, Franzén S, Svensson D. Digital twins and Bayesian dynamic borrowing: Two recent approaches for incorporating historical control data. Pharm Stat 2024. [PMID: 38439136 DOI: 10.1002/pst.2376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 01/29/2024] [Accepted: 02/20/2024] [Indexed: 03/06/2024]
Abstract
Recent years have seen an increasing interest in incorporating external control data for designing and evaluating randomized clinical trials (RCT). This may decrease costs and shorten inclusion times by reducing sample sizes. For small populations, with limited recruitment, this can be especially important. Bayesian dynamic borrowing (BDB) has been a popular choice as it claims to protect against potential prior data conflict. Digital twins (DT) has recently been proposed as another method to utilize historical data. DT, also known as PROCOVA™, is based on constructing a prognostic score from historical control data, typically using machine learning. This score is included in a pre-specified ANCOVA as the primary analysis of the RCT. The promise of this idea is power increase while guaranteeing strong type 1 error control. In this paper, we apply analytic derivations and simulations to analyze and discuss examples of these two approaches. We conclude that BDB and DT, although similar in scope, have fundamental differences which need be considered in the specific application. The inflation of the type 1 error is a serious issue for BDB, while more evidence is needed of a tangible value of DT for real RCTs.
Collapse
Affiliation(s)
- Carl-Fredrik Burman
- Early Biometrics & Statistical Innovation, Data Science & Artificial Intelligence, R&D, AstraZeneca, Gothenburg, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Erik Hermansson
- Early Biometrics & Statistical Innovation, Data Science & Artificial Intelligence, R&D, AstraZeneca, Gothenburg, Sweden
| | - David Bock
- Early Biometrics & Statistical Innovation, Data Science & Artificial Intelligence, R&D, AstraZeneca, Gothenburg, Sweden
| | - Stefan Franzén
- BMP Evidence Statistics, BioPharmaceuticals Medical, AstraZeneca, Gothenburg, Sweden
| | - David Svensson
- Early Biometrics & Statistical Innovation, Data Science & Artificial Intelligence, R&D, AstraZeneca, Gothenburg, Sweden
| |
Collapse
|
16
|
Zhang Y, Zhu T, Zheng Y, Xiong Y, Liu W, Zeng W, Tang W, Liu C. Machine learning-based medical imaging diagnosis in patients with temporomandibular disorders: a diagnostic test accuracy systematic review and meta-analysis. Clin Oral Investig 2024; 28:186. [PMID: 38430334 DOI: 10.1007/s00784-024-05586-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 02/25/2024] [Indexed: 03/03/2024]
Abstract
OBJECTIVES Temporomandibular disorders (TMDs) are the second most common musculoskeletal condition which are challenging tasks for most clinicians. Recent research used machine learning (ML) algorithms to diagnose TMDs intelligently. This study aimed to systematically evaluate the quality of these studies and assess the diagnostic accuracy of existing models. MATERIALS AND METHODS Twelve databases (Europe PMC, Embase, etc.) and two registers were searched for published and unpublished studies using ML algorithms on medical images. Two reviewers extracted the characteristics of studies and assessed the methodological quality using the QUADAS-2 tool independently. RESULTS A total of 28 studies (29 reports) were included: one was at unclear risk of bias and the others were at high risk. Thus the certainty of evidence was quite low. These studies used many types of algorithms including 8 machine learning models (logistic regression, support vector machine, random forest, etc.) and 15 deep learning models (Resnet152, Yolo v5, Inception V3, etc.). The diagnostic accuracy of a few models was relatively satisfactory. The pooled sensitivity and specificity were 0.745 (0.660-0.814) and 0.770 (0.700-0.828) in random forest, 0.765 (0.686-0.829) and 0.766 (0.688-0.830) in XGBoost, and 0.781 (0.704-0.843) and 0.781 (0.704-0.843) in LightGBM. CONCLUSIONS Most studies had high risks of bias in Patient Selection and Index Test. Some algorithms are relatively satisfactory and might be promising in intelligent diagnosis. Overall, more high-quality studies and more types of algorithms should be conducted in the future. CLINICAL RELEVANCE We evaluated the diagnostic accuracy of the existing models and provided clinicians with much advice about the selection of algorithms. This study stated the promising orientation of future research, and we believe it will promote the intelligent diagnosis of TMDs.
Collapse
Affiliation(s)
- Yunan Zhang
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China
| | - Tao Zhu
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China
| | - Yunhao Zheng
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China
| | - Yutao Xiong
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China
| | - Wei Liu
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China
| | - Wei Zeng
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China
| | - Wei Tang
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China.
| | - Chang Liu
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China.
| |
Collapse
|
17
|
Noda T, Mizuno S, Mogushi K, Hase T, Iida Y, Takeuchi K, Ishiwata Y, Nagata M. Development of a predictive model for nephrotoxicity during tacrolimus treatment using machine learning methods. Br J Clin Pharmacol 2024; 90:675-683. [PMID: 37921554 DOI: 10.1111/bcp.15953] [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: 06/23/2023] [Revised: 10/06/2023] [Accepted: 10/29/2023] [Indexed: 11/04/2023] Open
Abstract
AIM When administering tacrolimus, therapeutic drug monitoring is recommended because nephrotoxicity, an adverse event, occurs at supra-therapeutic whole-blood concentrations of tacrolimus. However, some patients exhibit nephrotoxicity even at the recommended concentrations, therefore establishing a therapeutic range of tacrolimus concentration for the individual patient is necessary to avoid nephrotoxicity. This study aimed to develop a model for individualized prediction of nephrotoxicity in patients administered tacrolimus. METHODS We collected data, such as laboratory test data at tacrolimus initiation, concomitant drugs and tacrolimus whole-blood concentration, from medical records of patients who received oral tacrolimus. Nephrotoxicity was defined as an increase in serum creatinine levels within 60 days of tacrolimus initiation. We built 13 prediction models based on different machine learning algorithms: logistic regression, support vector machine, gradient-boosting trees, random forest and neural networks. The best performing model was compared with the conventional model, which classifies patients according to the tacrolimus concentration alone. RESULTS Data from 163 and 41 patients were used to construct models and evaluate the best performing one, respectively. Most of the patients were diagnosed with inflammatory or autoimmune diseases. The best performing model was built using a support vector machine; it showed a high F2 score of 0.750 and outperformed the conventional model (0.500). CONCLUSIONS A machine learning model to predict nephrotoxicity in patients during tacrolimus treatment was developed using tacrolimus whole-blood concentration and other patient data. This model could potentially assist in identifying high-risk patients who require individualized target therapeutic concentrations of tacrolimus prior to treatment initiation to prevent nephrotoxicity.
Collapse
Affiliation(s)
- Tsubura Noda
- Department of Pharmacokinetics and Pharmacodynamics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Bunkyo-ku, Japan
| | - Shotaro Mizuno
- Department of Pharmacokinetics and Pharmacodynamics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Bunkyo-ku, Japan
| | - Kaoru Mogushi
- Innovative Human Resource Development Division, Institute of Education, Tokyo Medical and Dental University (TMDU), Tokyo, Bunkyo-ku, Japan
| | - Takeshi Hase
- Innovative Human Resource Development Division, Institute of Education, Tokyo Medical and Dental University (TMDU), Tokyo, Bunkyo-ku, Japan
| | - Yoritsugu Iida
- Innovative Human Resource Development Division, Institute of Education, Tokyo Medical and Dental University (TMDU), Tokyo, Bunkyo-ku, Japan
| | - Katsuyuki Takeuchi
- Innovative Human Resource Development Division, Institute of Education, Tokyo Medical and Dental University (TMDU), Tokyo, Bunkyo-ku, Japan
| | - Yasuyoshi Ishiwata
- Department of Pharmacy, Tokyo Medical and Dental University Hospital, Tokyo Medical and Dental University (TMDU), Tokyo, Bunkyo-ku, Japan
| | - Masashi Nagata
- Department of Pharmacokinetics and Pharmacodynamics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Bunkyo-ku, Japan
- Department of Pharmacy, Tokyo Medical and Dental University Hospital, Tokyo Medical and Dental University (TMDU), Tokyo, Bunkyo-ku, Japan
| |
Collapse
|
18
|
Zhuang J, Midgley AC, Wei Y, Liu Q, Kong D, Huang X. Machine-Learning-Assisted Nanozyme Design: Lessons from Materials and Engineered Enzymes. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2210848. [PMID: 36701424 DOI: 10.1002/adma.202210848] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/03/2023] [Indexed: 05/11/2023]
Abstract
Nanozymes are nanomaterials that exhibit enzyme-like biomimicry. In combination with intrinsic characteristics of nanomaterials, nanozymes have broad applicability in materials science, chemical engineering, bioengineering, biochemistry, and disease theranostics. Recently, the heterogeneity of published results has highlighted the complexity and diversity of nanozymes in terms of consistency of catalytic capacity. Machine learning (ML) shows promising potential for discovering new materials, yet it remains challenging for the design of new nanozymes based on ML approaches. Alternatively, ML is employed to promote optimization of intelligent design and application of catalytic materials and engineered enzymes. Incorporation of the successful ML algorithms used in the intelligent design of catalytic materials and engineered enzymes can concomitantly facilitate the guided development of next-generation nanozymes with desirable properties. Here, recent progress in ML, its utilization in the design of catalytic materials and enzymes, and how emergent ML applications serve as promising strategies to circumvent challenges associated with time-expensive and laborious testing in nanozyme research and development are summarized. The potential applications of successful examples of ML-aided catalytic materials and engineered enzymes in nanozyme design are also highlighted, with special focus on the unified aims in enhancing design and recapitulation of substrate selectivity and catalytic activity.
Collapse
Affiliation(s)
- Jie Zhuang
- School of Medicine, and State, Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300071, China
| | - Adam C Midgley
- Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers, Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
| | - Yonghua Wei
- Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers, Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
| | - Qiqi Liu
- Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers, Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
| | - Deling Kong
- Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers, Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
| | - Xinglu Huang
- Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers, Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
| |
Collapse
|
19
|
Ryan DK, Maclean RH, Balston A, Scourfield A, Shah AD, Ross J. Artificial intelligence and machine learning for clinical pharmacology. Br J Clin Pharmacol 2024; 90:629-639. [PMID: 37845024 DOI: 10.1111/bcp.15930] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/04/2023] [Accepted: 10/06/2023] [Indexed: 10/18/2023] Open
Abstract
Artificial intelligence (AI) will impact many aspects of clinical pharmacology, including drug discovery and development, clinical trials, personalized medicine, pharmacogenomics, pharmacovigilance and clinical toxicology. The rapid progress of AI in healthcare means clinical pharmacologists should have an understanding of AI and its implementation in clinical practice. As with any new therapy or health technology, it is imperative that AI tools are subject to robust and stringent evaluation to ensure that they enhance clinical practice in a safe and equitable manner. This review serves as an introduction to AI for the clinical pharmacologist, highlighting current applications, aspects of model development and issues surrounding evaluation and deployment. The aim of this article is to empower clinical pharmacologists to embrace and lead on the safe and effective use of AI within healthcare.
Collapse
Affiliation(s)
- David K Ryan
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Rory H Maclean
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Alfred Balston
- Department of Clinical Pharmacology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Andrew Scourfield
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Anoop D Shah
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
- National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, UK
| | - Jack Ross
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
| |
Collapse
|
20
|
Hassan J, Saeed SM, Deka L, Uddin MJ, Das DB. Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges. Pharmaceutics 2024; 16:260. [PMID: 38399314 PMCID: PMC10892549 DOI: 10.3390/pharmaceutics16020260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.
Collapse
Affiliation(s)
- Jasmin Hassan
- Drug Delivery & Therapeutics Lab, Dhaka 1212, Bangladesh; (J.H.); (S.M.S.)
| | | | - Lipika Deka
- Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK;
| | - Md Jasim Uddin
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Diganta B. Das
- Department of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, UK
| |
Collapse
|
21
|
S G, Kannadhasan S, A J. Multi class robust brain tumor with hybrid classification using DTA algorithm. Heliyon 2024; 10:e23610. [PMID: 38187263 PMCID: PMC10770571 DOI: 10.1016/j.heliyon.2023.e23610] [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: 02/13/2023] [Revised: 10/03/2023] [Accepted: 12/07/2023] [Indexed: 01/09/2024] Open
Abstract
Analyzing brain tumours is important for prompt diagnosis and efficient patient care. The morphology of tumours, which includes their size, location, texture, and heteromorphic appearance in medical pictures, makes them difficult to analyse. A unique two-phase deep learning-based framework is suggested in this respect to recognise and classify brain cancers in magnetic resonance images (MRIs). A new DTA approach is suggested in the first phase to successfully identify tumour MRI images from healthy persons. DTA are specifically designed and perform well are used to create the deep boosted feature space, which is then fed into the group of machine learning (ML) classifiers. In the second stage, a brand-new hybrid features fusion-based brain tumour classification technique is put forward, one that makes use of both static and dynamic features as well as an ML classifier to classify various tumour kinds. The proposed algorithm, which can recognise the heteromorphic and variable behaviour of different tumours, is where the dynamic characteristics are taken.In this paper, many segmentation algorithms for MRI and PET are reviewed together with their performance evaluations in order to detect brain tumours. There are numerous segmentation methods available for the diagnosis of medical images. The features of the image, such as the capacity to distinguish between similarities and discontinuities, are often used to classify the segmentation techniques. Neuroradiologists have a difficult issue in trying to quickly identify the abnormal region, which is essential in the medical field. In order to overcome this problem, the efficiency of different segmentation procedures is assessed. The segmentation methods considered here are Ant Colony Optimization (ACO), Wavelet Transform (WT), Gradient Vector Flow (GVF), Gray level Co-occurrence matrix (GLCM), and Artificial Bee Colony (ABC). The various performance metrics are used to evaluate the suggested segmentation algorithms. The GVF strategy works better with MRI images, whereas the ABC and ACO approaches perform similarly with PET scans, according to the data acquired.
Collapse
Affiliation(s)
- Ganesh S
- Department of Computer Science and Engineering, Study World College of Engineering, Coimbatore, Tamilnadu, India
| | - S. Kannadhasan
- Department of Electronics and Communication Engineering, Study World College of Engineering, Coimbatore, Tamilnadu, India
| | - Jayachandran A
- Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamilnadu, India
| |
Collapse
|
22
|
Banerjee D, Adhikary S, Bhattacharya S, Chakraborty A, Dutta S, Chatterjee S, Ganguly A, Nanda S, Rajak P. Breaking boundaries: Artificial intelligence for pesticide detection and eco-friendly degradation. ENVIRONMENTAL RESEARCH 2024; 241:117601. [PMID: 37977271 DOI: 10.1016/j.envres.2023.117601] [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: 06/30/2023] [Revised: 09/21/2023] [Accepted: 11/03/2023] [Indexed: 11/19/2023]
Abstract
Pesticides are extensively used agrochemicals across the world to control pest populations. However, irrational application of pesticides leads to contamination of various components of the environment, like air, soil, water, and vegetation, all of which build up significant levels of pesticide residues. Further, these environmental contaminants fuel objectionable human toxicity and impose a greater risk to the ecosystem. Therefore, search of methodologies having potential to detect and degrade pesticides in different environmental media is currently receiving profound global attention. Beyond the conventional approaches, Artificial Intelligence (AI) coupled with machine learning and artificial neural networks are rapidly growing branches of science that enable quick data analysis and precise detection of pesticides in various environmental components. Interestingly, nanoparticle (NP)-mediated detection and degradation of pesticides could be linked to AI algorithms to achieve superior performance. NP-based sensors stand out for their operational simplicity as well as their high sensitivity and low detection limits when compared to conventional, time-consuming spectrophotometric assays. NPs coated with fluorophores or conjugated with antibody or enzyme-anchored sensors can be used through Surface-Enhanced Raman Spectrometry, fluorescence, or chemiluminescence methodologies for selective and more precise detection of pesticides. Moreover, NPs assist in the photocatalytic breakdown of various organic and inorganic pesticides. Here, AI models are ideal means to identify, classify, characterize, and even predict the data of pesticides obtained through NP sensors. The present study aims to discuss the environmental contamination and negative impacts of pesticides on the ecosystem. The article also elaborates the AI and NP-assisted approaches for detecting and degrading a wide range of pesticide residues in various environmental and agrecultural sources including fruits and vegetables. Finally, the prevailing limitations and future goals of AI-NP-assisted techniques have also been dissected.
Collapse
Affiliation(s)
- Diyasha Banerjee
- Department of Animal Science, Kazi Nazrul University, Asansol, West Bengal, India.
| | - Satadal Adhikary
- Post Graduate Department of Zoology, A. B. N. Seal College, Cooch Behar, West Bengal, India.
| | | | - Aritra Chakraborty
- Department of Animal Science, Kazi Nazrul University, Asansol, West Bengal, India.
| | - Sohini Dutta
- Department of Animal Science, Kazi Nazrul University, Asansol, West Bengal, India.
| | - Sovona Chatterjee
- Department of Animal Science, Kazi Nazrul University, Asansol, West Bengal, India.
| | - Abhratanu Ganguly
- Department of Animal Science, Kazi Nazrul University, Asansol, West Bengal, India.
| | - Sayantani Nanda
- Department of Animal Science, Kazi Nazrul University, Asansol, West Bengal, India.
| | - Prem Rajak
- Department of Animal Science, Kazi Nazrul University, Asansol, West Bengal, India.
| |
Collapse
|
23
|
Adida S, Legarreta AD, Hudson JS, McCarthy D, Andrews E, Shanahan R, Taori S, Lavadi RS, Buell TJ, Hamilton DK, Agarwal N, Gerszten PC. Machine Learning in Spine Surgery: A Narrative Review. Neurosurgery 2024; 94:53-64. [PMID: 37930259 DOI: 10.1227/neu.0000000000002660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/06/2023] [Indexed: 11/07/2023] Open
Abstract
Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.
Collapse
Affiliation(s)
- Samuel Adida
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Andrew D Legarreta
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Joseph S Hudson
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - David McCarthy
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Edward Andrews
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Regan Shanahan
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Suchet Taori
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Raj Swaroop Lavadi
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Thomas J Buell
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - D Kojo Hamilton
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Nitin Agarwal
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh , Pennsylvania , USA
| | - Peter C Gerszten
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| |
Collapse
|
24
|
Umbaugh DS, Jaeschke H. Biomarker discovery in acetaminophen hepatotoxicity: leveraging single-cell transcriptomics and mechanistic insight. Expert Rev Clin Pharmacol 2024; 17:143-155. [PMID: 38217408 PMCID: PMC10872301 DOI: 10.1080/17512433.2024.2306219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 01/12/2024] [Indexed: 01/15/2024]
Abstract
INTRODUCTION Acetaminophen (APAP) overdose is the leading cause of drug-induced liver injury and can cause a rapid progression to acute liver failure (ALF). Therefore, the identification of prognostic biomarkers to determine which patients will require a liver transplant is critical for APAP-induced ALF. AREAS COVERED We begin by relating the mechanistic investigations in mouse models of APAP hepatotoxicity to the human APAP overdose pathophysiology. We draw insights from the established sequence of molecular events in mice to understand the progression of events in the APAP overdose patient. Through this mechanistic understanding, several new biomarkers, such as CXCL14, have recently been evaluated. We also explore how single-cell RNA sequencing, spatial transcriptomics, and other omics approaches have been leveraged for identifying novel biomarkers and how these approaches will continue to push the field of biomarker discovery forward. EXPERT OPINION Recent investigations have elucidated several new biomarkers or combination of markers such as CXCL14, a regenerative miRNA signature, a cell death miRNA signature, hepcidin, LDH, CPS1, and FABP1. While these biomarkers are promising, they all require further validation. Larger cohort studies analyzing these new biomarkers in the same patient samples, while adding these candidate biomarkers to prognostic models will further support their clinical utility.
Collapse
Affiliation(s)
- David S Umbaugh
- Department of Pharmacology, Toxicology & Therapeutics, University of Kansas Medical Center, Kansas City, KS, USA
| | - Hartmut Jaeschke
- Department of Pharmacology, Toxicology & Therapeutics, University of Kansas Medical Center, Kansas City, KS, USA
| |
Collapse
|
25
|
Huang J, Gao Y, Chang Y, Peng J, Yu Y, Wang B. Machine Learning in Bioelectrocatalysis. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306583. [PMID: 37946709 PMCID: PMC10787072 DOI: 10.1002/advs.202306583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Indexed: 11/12/2023]
Abstract
At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high-value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction.
Collapse
Affiliation(s)
- Jiamin Huang
- Department of Environmental Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, China
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology, Beijing, 100190, China
| | - Yang Gao
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology, Beijing, 100190, China
| | - Yanhong Chang
- Department of Environmental Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Yadong Yu
- College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, 211816, China
| | - Bin Wang
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology, Beijing, 100190, China
| |
Collapse
|
26
|
Wetthasinghe ST, Garashchuk SV, Rassolov VA. Stability Trends in disubstituted Cobaltocenium Based on the Analysis of the Machine Learning Models. J Phys Chem A 2023; 127:10701-10708. [PMID: 38015632 DOI: 10.1021/acs.jpca.3c05668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Cobaltocenium derivatives have shown great potential as components of anion exchange membranes in fuel cells because they exhibit excellent thermal and alkaline stability under operating conditions while allowing for high anion mobility. The properties of the cobaltocenium-anion complexes can be chemically tuned through the substituent groups on the cyclopentadienyl (Cp) rings of the cation CoCp2+. However, the synthesis and characterization of the full range of possible derivatives are very challenging and time-consuming, and while the computational tools can greatly expedite this process, full screening of the electronic structure at a high level of theory is still computationally intensive. Therefore, in this work, we consider the machine learning (ML) modeling as a tool of predicting stability of disubstituted [CoCp2]OH complexes measured by their bond-dissociation energy (BDE). The relevant process here is the dissociation of the cobaltocenium-hydroxide complex into fragments [CoCpY']OH and CpY, where Y and Y' each represent one out of 42 substituent groups of experimental interest. In agreement with the previous ML study of 120 mono- and selected disubstituted species [Wetthasinghe et al. J. Chem. Phys. A (2022) 126], our analysis of the data set expanded to all possible disubstituted cobaltoceniums, points to the highest occupied and lowest unoccupied molecular orbitals, along with the Hirshfeld charge on the singly substituted benzene, to be the key features predicting the BDE of the unseen complexes. Based on the examination of the outliers, the acidity of substituents ((CO)NH2 in our case) is found to be of special significance for the cobaltocenium stability and for the model development. Moreover, we demonstrate that upon the data set refinement, the conventional ML models are capable of predicting the BDE close to 1 kcal/mol based on the properties of just the fragments, thereby greatly reducing the total number of species and of the computational time of each calculation. Such fragment-based "combinatorial" approach to the BDE modeling is noteworthy, since the geometry optimization of complexes in solution is conceptually challenging and computationally demanding, even when leveraging high-performance computing resources.
Collapse
Affiliation(s)
- Shehani T Wetthasinghe
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States
| | - Sophya V Garashchuk
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States
| | - Vitaly A Rassolov
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States
| |
Collapse
|
27
|
Sharma S, Kogan C, Varma MVS, Prasad B. Analysis of the interplay of physiological response to food intake and drug properties in food-drug interactions. Drug Metab Pharmacokinet 2023; 53:100518. [PMID: 37856928 DOI: 10.1016/j.dmpk.2023.100518] [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: 03/06/2023] [Revised: 05/02/2023] [Accepted: 06/02/2023] [Indexed: 10/21/2023]
Abstract
The effect of food on oral drug absorption is determined by the complex interplay among gut physiological factors and drug properties. The currently used dissolution testing and classification systems (biopharmaceutics classification system, BCS or biopharmaceutics drug disposition classification system, BDDCS) do not account for dynamic changes in gastrointestinal physiology caused by food intake. This study aimed to identify key drug properties that influence food effect (FE) using supervised machine learning approaches. The analysis showed that drugs with high logP, dose number, and extraction ratio have a higher probability of positive FE, while drugs with low permeability and high efflux saturation index have a greater likelihood of negative FE. Weakly acidic drugs also showed a greater probability of positive FE, particularly at pKa >4.3. The importance of drug properties in predicting FE was ranked as logP, dose number, extraction ratio, pKa, and permeability. The accuracy of FE prediction using the models was compared with BCS and extended clearance classification system (ECCS). Overall, the likelihood or magnitude of FE depends on physiological changes to food intake such as altered bile acid secretion rate, intestinal metabolism, transport kinetics, and gastric emptying time, which should be considered along with drug properties (e.g., solubility, logP, and ionization) in predicting FE of orally administered drugs.
Collapse
Affiliation(s)
- Sheena Sharma
- Department of Pharmaceutical Sciences, Washington State University, Spokane, WA, USA
| | - Clark Kogan
- Center for Interdisciplinary Statistical Education and Research (CISER), Washington State University, Pullman, WA, USA
| | - Manthena V S Varma
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Pfizer Global Research and Development, Pfizer Inc., Groton, CT, USA
| | - Bhagwat Prasad
- Department of Pharmaceutical Sciences, Washington State University, Spokane, WA, USA.
| |
Collapse
|
28
|
Antão J, de Mast J, Marques A, Franssen FME, Spruit MA, Deng Q. Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases. Expert Rev Respir Med 2023; 17:1207-1219. [PMID: 38270524 DOI: 10.1080/17476348.2024.2302940] [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: 07/13/2023] [Accepted: 01/04/2024] [Indexed: 01/26/2024]
Abstract
INTRODUCTION Asthma and chronic obstructive pulmonary disease (COPD) are leading causes of morbidity and mortality worldwide. Despite all available diagnostics and treatments, these conditions pose a significant individual, economic and social burden. Artificial intelligence (AI) promises to support clinical decision-making processes by optimizing diagnosis and treatment strategies of these heterogeneous and complex chronic respiratory diseases. Its capabilities extend to predicting exacerbation risk, disease progression and mortality, providing healthcare professionals with valuable insights for more effective care. Nevertheless, the knowledge gap between respiratory clinicians and data scientists remains a major constraint for wide application of AI and may hinder future progress. This narrative review aims to bridge this gap and encourage AI deployment by explaining its methodology and added value in asthma and COPD diagnosis and treatment. AREAS COVERED This review offers an overview of the fundamental concepts of AI and machine learning, outlines the key steps in building a model, provides examples of their applicability in asthma and COPD care, and discusses barriers to their implementation. EXPERT OPINION Machine learning can advance our understanding of asthma and COPD, enabling personalized therapy and better outcomes. Further research and validation are needed to ensure the development of clinically meaningful and generalizable models.
Collapse
Affiliation(s)
- Joana Antão
- Lab3R - Respiratory Research and Rehabilitation Laboratory, School of Health Sciences, University of Aveiro (ESSUA), Aveiro, Portugal
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Jeroen de Mast
- Economics and Business, University of Amsterdam, Amsterdam, The Netherlands
| | - Alda Marques
- Lab3R - Respiratory Research and Rehabilitation Laboratory, School of Health Sciences, University of Aveiro (ESSUA), Aveiro, Portugal
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Frits M E Franssen
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Martijn A Spruit
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Qichen Deng
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| |
Collapse
|
29
|
Li J, Yang G, Zhang L. Artificial Intelligence Empowered Nuclear Medicine and Molecular Imaging in Cardiology: A State-of-the-Art Review. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:586-596. [PMID: 38223683 PMCID: PMC10781930 DOI: 10.1007/s43657-023-00137-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 01/16/2024]
Abstract
Nuclear medicine and molecular imaging plays a significant role in the detection and management of cardiovascular disease (CVD). With recent advancements in computer power and the availability of digital archives, artificial intelligence (AI) is rapidly gaining traction in the field of medical imaging, including nuclear medicine and molecular imaging. However, the complex and time-consuming workflow and interpretation involved in nuclear medicine and molecular imaging, limit their extensive utilization in clinical practice. To address this challenge, AI has emerged as a fundamental tool for enhancing the role of nuclear medicine and molecular imaging. It has shown promising applications in various crucial aspects of nuclear cardiology, such as optimizing imaging protocols, facilitating data processing, aiding in CVD diagnosis, risk classification and prognosis. In this review paper, we will introduce the key concepts of AI and provide an overview of its current progress in the field of nuclear cardiology. In addition, we will discuss future perspectives for AI in this domain.
Collapse
Affiliation(s)
- Junhao Li
- Department of Nuclear Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
| | - Guifen Yang
- Department of Nuclear Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
| |
Collapse
|
30
|
Saleh O, Otim FN, Otim O. Application of supervised learning classification modeling for predicting benthic sediment toxicity in the southern California bight: A test of concept. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 901:165946. [PMID: 37541495 DOI: 10.1016/j.scitotenv.2023.165946] [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: 06/06/2023] [Revised: 07/28/2023] [Accepted: 07/29/2023] [Indexed: 08/06/2023]
Abstract
Benthic sediment toxicity is linked to harmful effects in marine organisms and humans, and an understanding of the link would require, in part, a comprehensive and exhaustive analysis of sediment toxicity data already in hand. One tool which could aid in the process is machine learning (ML), a supervised classification modeling technique that has transformed how actionable insight are acquired from large datasets. The current study is a test of concept in which an ML classifier is sought that can accurately extrapolate the characteristics of a 5437 California-wide coastal training dataset (assembled from 1635 samples) to predict sediment toxicity in southern California bight (SCB). Twelve classifiers were trained to recognize sediment toxicity using 70 % of the dataset and among them, a Gradient Boosting Classifier (GBC) model using latitude, longitude, and water depth was found to be the most accurate at predicting toxicity (83 %). Among the variables, latitude was found to be the most significant driver of prediction by GBC in this test ecosystem. The performance of the model was verified with the remaining 30 % of the dataset and found to be 83 % accurate. Presented with 884 unfamiliar data points assembled from 854 measurements at 346 stations across SCB, GBC was 87 % accurate post-training, thus demonstrating a role supervised learning can play in the southern California environmental analytics.
Collapse
Affiliation(s)
- Omar Saleh
- Department of Humanities and Sciences, University of California - Los Angeles, Los Angeles, CA 90024, USA
| | - Francesca Nyega Otim
- Department of Anthropology, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA
| | - Ochan Otim
- Department of Humanities and Sciences, University of California - Los Angeles, Los Angeles, CA 90024, USA; Environmental Monitoring Division, City of Los Angeles, Playa Del Rey, CA 90293, USA.
| |
Collapse
|
31
|
McLeish E, Slater N, Mastaglia FL, Needham M, Coudert JD. From data to diagnosis: how machine learning is revolutionizing biomarker discovery in idiopathic inflammatory myopathies. Brief Bioinform 2023; 25:bbad514. [PMID: 38243695 PMCID: PMC10796252 DOI: 10.1093/bib/bbad514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 11/29/2023] [Accepted: 12/17/2023] [Indexed: 01/21/2024] Open
Abstract
Idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of muscle disorders including adult and juvenile dermatomyositis, polymyositis, immune-mediated necrotising myopathy and sporadic inclusion body myositis, all of which present with variable symptoms and disease progression. The identification of effective biomarkers for IIMs has been challenging due to the heterogeneity between IIMs and within IIM subgroups, but recent advances in machine learning (ML) techniques have shown promises in identifying novel biomarkers. This paper reviews recent studies on potential biomarkers for IIM and evaluates their clinical utility. We also explore how data analytic tools and ML algorithms have been used to identify biomarkers, highlighting their potential to advance our understanding and diagnosis of IIM and improve patient outcomes. Overall, ML techniques have great potential to revolutionize biomarker discovery in IIMs and lead to more effective diagnosis and treatment.
Collapse
Affiliation(s)
- Emily McLeish
- Murdoch University, Centre for Molecular Medicine and Innovative Therapeutics, Murdoch, Western Australia (WA), Australia
| | - Nataliya Slater
- Murdoch University, Centre for Molecular Medicine and Innovative Therapeutics, Murdoch, Western Australia (WA), Australia
| | - Frank L Mastaglia
- Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
| | - Merrilee Needham
- Murdoch University, Centre for Molecular Medicine and Innovative Therapeutics, Murdoch, Western Australia (WA), Australia
- Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
- University of Notre Dame Australia, School of Medicine, Fremantle, WA, Australia
- Fiona Stanley Hospital, Department of Neurology, Murdoch, WA, Australia
| | - Jerome D Coudert
- Murdoch University, Centre for Molecular Medicine and Innovative Therapeutics, Murdoch, Western Australia, WA, Australia
- Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
- University of Notre Dame Australia, School of Medicine, Fremantle, WA, Australia
| |
Collapse
|
32
|
Larrea-Sebal A, Jebari-Benslaiman S, Galicia-Garcia U, Jose-Urteaga AS, Uribe KB, Benito-Vicente A, Martín C. Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies. Curr Atheroscler Rep 2023; 25:839-859. [PMID: 37847331 PMCID: PMC10618353 DOI: 10.1007/s11883-023-01154-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/15/2023] [Indexed: 10/18/2023]
Abstract
PURPOSE OF REVIEW Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH. RECENT FINDINGS In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.
Collapse
Affiliation(s)
- Asier Larrea-Sebal
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain
- Fundación Biofisika Bizkaia, 48940, Leioa, Spain
| | - Shifa Jebari-Benslaiman
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain
| | - Unai Galicia-Garcia
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain
| | - Ane San Jose-Urteaga
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
| | - Kepa B Uribe
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
| | - Asier Benito-Vicente
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain
| | - César Martín
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain.
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain.
| |
Collapse
|
33
|
Mu Y, Tizhoosh HR, Dehkharghanian T, Campbell CJV. Whole slide image representation in bone marrow cytology. Comput Biol Med 2023; 166:107530. [PMID: 37837726 DOI: 10.1016/j.compbiomed.2023.107530] [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: 07/28/2023] [Revised: 09/17/2023] [Accepted: 09/27/2023] [Indexed: 10/16/2023]
Abstract
One of the goals of AI-based computational pathology is to generate compact representations of whole slide images (WSIs) that capture the essential information needed for diagnosis. While such approaches have been applied to histopathology, few applications have been reported in cytology. Bone marrow aspirate cytology is the basis for key clinical decisions in hematology. However, visual inspection of aspirate specimens is a tedious and complex process subject to variation in interpretation, and hematopathology expertise is scarce. The ability to generate a compact representation of an aspirate specimen may form the basis for clinical decision-support tools in hematology. In this study, we leverage our previously published end-to-end AI-based system for counting and classifying cells from bone marrow aspirate WSIs, which enables the direct use of individual cells as inputs rather than WSI patches. We then construct bags of individual cell features from each WSI, and apply multiple instance learning to extract their vector representations. To evaluate the quality of our representations, we conducted WSI retrieval and classification tasks. Our results show that we achieved a mAP@10 of 0.58 ±0.02 in WSI-level image retrieval, surpassing the random-retrieval baseline of 0.39 ±0.1. Furthermore, we predicted five diagnostic labels for individual aspirate WSIs with a weighted-average F1 score of 0.57 ±0.03 using a k-nearest-neighbors (k-NN) model, outperforming guessing using empirical class prior probabilities (0.26 ±0.02). We present the first example of exploring trainable mechanisms to generate compact, slide-level representations in bone marrow cytology with deep learning. This method has the potential to summarize complex semantic information in WSIs toward improved diagnostics in hematology, and may eventually support AI-assisted computational pathology approaches.
Collapse
Affiliation(s)
- Youqing Mu
- University of Toronto, Toronto, Canada; McMaster University, Hamilton, Canada
| | - H R Tizhoosh
- Rhazes Lab, Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA
| | - Taher Dehkharghanian
- McMaster University, Hamilton, Canada; University Health Network, Toronto, Canada
| | - Clinton J V Campbell
- McMaster University, Hamilton, Canada; William Osler Health System, Brampton, Canada.
| |
Collapse
|
34
|
Grothues S, Becker AK, Hohlmann B, Radermacher K. Parameter-based patient-specific restoration of physiological knee morphology for optimized implant design and matching. BIOMED ENG-BIOMED TE 2023; 68:537-544. [PMID: 37185164 DOI: 10.1515/bmt-2023-0017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 04/03/2023] [Indexed: 05/17/2023]
Abstract
Total knee arthroplasty (TKA) patients may present with genetic deformities, such as trochlear dysplasia, or deformities related to osteoarthritis. This pathologic morphology should be corrected by TKA to compensate for related functional deficiencies. Hence, a reconstruction of an equivalent physiological knee morphology would be favorable for detailed preoperative planning and the patient-specific implant selection or design process. A parametric database of 673 knees, each described by 36 femoral parameter values, was used. Each knee was classified as pathological or physiological based on cut-off values from literature. A clinical and a mathematical classification approach were developed to distinguish between affected and unaffected parameters. Three different prediction methods were used for the restoration of physiological parameter values: regression, nearest neighbor search and artificial neural networks. Several variants of the respective prediction model were considered, such as different network architectures. Regarding all methods, the model variant chosen resulted in a prediction error below the parameters' standard deviation, while the regression yielded the lowest errors. Future analyses should consider other deformities, also of tibia and patella. Furthermore, the functional consequences of the parameter changes should be analyzed.
Collapse
Affiliation(s)
- Sonja Grothues
- Chair of Medical Engineering, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
| | - Ann-Kristin Becker
- Chair of Medical Engineering, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
| | - Benjamin Hohlmann
- Chair of Medical Engineering, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
| | - Klaus Radermacher
- Chair of Medical Engineering, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
| |
Collapse
|
35
|
Przyczyna D, Mech K, Kowalewska E, Marzec M, Mazur T, Zawal P, Szaciłowski K. The Memristive Properties and Spike Timing-Dependent Plasticity in Electrodeposited Copper Tungstates and Molybdates. MATERIALS (BASEL, SWITZERLAND) 2023; 16:6675. [PMID: 37895657 PMCID: PMC10608134 DOI: 10.3390/ma16206675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 09/28/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023]
Abstract
Memristors possess non-volatile memory, adjusting their electrical resistance to the current that flows through them and allowing switching between high and low conducting states. This technology could find applications in fields such as IT, consumer electronics, computing, sensors, and medicine. In this paper, we report successful electrodeposition of thin-film materials consisting of copper tungstate and copper molybdate (CuWO4 and Cu3Mo2O9), which showed notable memristive properties. Material characterisation was performed with techniques such as XRD, XPS, and SEM. The electrodeposited materials exhibited the ability to switch between low and high resistive states during varied cyclic scans and short-term impulses. The retention time of these switched states was also explored. Using these materials, the effects seen in biological systems, specifically spike timing-dependent plasticity, were simulated, being based on analogue operation of the memristors to achieve multiple conductivity states. Bio-inspired simulations performed directly on the material could possibly offer energy and time savings for classical computations. Memristors could be crucial for the advancement of high-efficiency, low-energy neuromorphic electronic devices and technologies in the future.
Collapse
Affiliation(s)
- Dawid Przyczyna
- Academic Centre for Materials and Nanotechnology, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland; (D.P.); (K.M.); (E.K.); (M.M.); (T.M.); (P.Z.)
- Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland
| | - Krzysztof Mech
- Academic Centre for Materials and Nanotechnology, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland; (D.P.); (K.M.); (E.K.); (M.M.); (T.M.); (P.Z.)
| | - Ewelina Kowalewska
- Academic Centre for Materials and Nanotechnology, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland; (D.P.); (K.M.); (E.K.); (M.M.); (T.M.); (P.Z.)
| | - Mateusz Marzec
- Academic Centre for Materials and Nanotechnology, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland; (D.P.); (K.M.); (E.K.); (M.M.); (T.M.); (P.Z.)
| | - Tomasz Mazur
- Academic Centre for Materials and Nanotechnology, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland; (D.P.); (K.M.); (E.K.); (M.M.); (T.M.); (P.Z.)
| | - Piotr Zawal
- Academic Centre for Materials and Nanotechnology, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland; (D.P.); (K.M.); (E.K.); (M.M.); (T.M.); (P.Z.)
- Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland
| | - Konrad Szaciłowski
- Academic Centre for Materials and Nanotechnology, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland; (D.P.); (K.M.); (E.K.); (M.M.); (T.M.); (P.Z.)
| |
Collapse
|
36
|
Juarez I, Kurouski D. Surface-enhanced Raman spectroscopy hair analysis after household contamination. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:4996-5001. [PMID: 37609869 DOI: 10.1039/d3ay01219k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Trace evidence found at crime scenes is rarely in an unsullied condition. Surface-enhanced Raman spectroscopy (SERS) is a modern analytical technique that can be used for the detection of artificial hair colourants (S. Higgins and D. Kurouski, Surface-Enhanced Raman Spectroscopy Enables Highly Accurate Identification of Different Brands, Types and Colors of Hair Dyes, Talanta, 2022, 251, 123762). However, contaminants pose a problem to collecting accurate spectra from the dyes. In this study, we sought to analyze how the different physical properties of contaminants can influence the collected spectra. We utilized 11 household substances of varying viscosity and opacity to contaminate hair dyed with permanent black or semi-permanent blue dyes. We discovered that contaminant opacity generally does not affect the spectral quality but that high contaminant viscosity does and that acidic substances could destroy the colourant's spectral identity altogether. Cleaning the contaminated hair with a water rinse allowed the underlying colourant to be identified in 21 out of 22 cases. Overall, this study provided a clearer understanding of the capabilities and limitations of SERS in forensic hair analysis.
Collapse
Affiliation(s)
- Isaac Juarez
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, Texas 77843, USA.
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, Texas 77843, USA.
- Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, USA
- Institute for Advancing Health Through Agriculture, Texas A&M University, College Station, Texas, 77843, USA
| |
Collapse
|
37
|
Aranda-Michel E, Sultan I. Commentary: Can we crack the black box of machine learning for aortic aneurysms? J Thorac Cardiovasc Surg 2023; 166:1021-1022. [PMID: 35000686 DOI: 10.1016/j.jtcvs.2021.12.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 10/19/2022]
Affiliation(s)
- Edgar Aranda-Michel
- Division of Cardiac Surgery, Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa
| | - Ibrahim Sultan
- Division of Cardiac Surgery, Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa; UPMC Center for Thoracic Aortic Disease, Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa.
| |
Collapse
|
38
|
Li B, Beaton D, Eisenberg N, Lee DS, Wijeysundera DN, Lindsay TF, de Mestral C, Mamdani M, Roche-Nagle G, Al-Omran M. Using machine learning to predict outcomes following carotid endarterectomy. J Vasc Surg 2023; 78:973-987.e6. [PMID: 37211142 DOI: 10.1016/j.jvs.2023.05.024] [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: 02/17/2023] [Revised: 05/08/2023] [Accepted: 05/13/2023] [Indexed: 05/23/2023]
Abstract
OBJECTIVE Prediction of outcomes following carotid endarterectomy (CEA) remains challenging, with a lack of standardized tools to guide perioperative management. We used machine learning (ML) to develop automated algorithms that predict outcomes following CEA. METHODS The Vascular Quality Initiative (VQI) database was used to identify patients who underwent CEA between 2003 and 2022. We identified 71 potential predictor variables (features) from the index hospitalization (43 preoperative [demographic/clinical], 21 intraoperative [procedural], and 7 postoperative [in-hospital complications]). The primary outcome was stroke or death at 1 year following CEA. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained six ML models using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intra- and postoperative data. Model robustness was evaluated using calibration plots and Brier scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, insurance status, symptom status, and urgency of surgery. RESULTS Overall, 166,369 patients underwent CEA during the study period. In total, 7749 patients (4.7%) had the primary outcome of stroke or death at 1 year. Patients with an outcome were older with more comorbidities, had poorer functional status, and demonstrated higher risk anatomic features. They were also more likely to undergo intraoperative surgical re-exploration and have in-hospital complications. Our best performing prediction model at the preoperative stage was XGBoost, achieving an AUROC of 0.90 (95% confidence interval [CI], 0.89-0.91). In comparison, logistic regression had an AUROC of 0.65 (95% CI, 0.63-0.67), and existing tools in the literature demonstrate AUROCs ranging from 0.58 to 0.74. Our XGBoost models maintained excellent performance at the intra- and postoperative stages, with AUROCs of 0.90 (95% CI, 0.89-0.91) and 0.94 (95% CI, 0.93-0.95), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.15 (preoperative), 0.14 (intraoperative), and 0.11 (postoperative). Of the top 10 predictors, eight were preoperative features, including comorbidities, functional status, and previous procedures. Model performance remained robust on all subgroup analyses. CONCLUSIONS We developed ML models that accurately predict outcomes following CEA. Our algorithms perform better than logistic regression and existing tools, and therefore, have potential for important utility in guiding perioperative risk mitigation strategies to prevent adverse outcomes.
Collapse
Affiliation(s)
- Ben Li
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada
| | - Derek Beaton
- Data Science and Advanced Analytics Department, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - Naomi Eisenberg
- Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Douglas S Lee
- Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada
| | - Duminda N Wijeysundera
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Department of Anesthesia, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Thomas F Lindsay
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Charles de Mestral
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Muhammad Mamdani
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada; Data Science and Advanced Analytics Department, Unity Health Toronto, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Graham Roche-Nagle
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Mohammed Al-Omran
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Department of Surgery, King Faisal Specialist Hospital and Research Center, Riyadh, Kingdom of Saudi Arabia.
| |
Collapse
|
39
|
Bassani D, Brigo A, Andrews-Morger A. Federated Learning in Computational Toxicology: An Industrial Perspective on the Effiris Hackathon. Chem Res Toxicol 2023; 36:1503-1517. [PMID: 37584277 PMCID: PMC10523574 DOI: 10.1021/acs.chemrestox.3c00137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Indexed: 08/17/2023]
Abstract
In silico approaches have acquired a towering role in pharmaceutical research and development, allowing laboratories all around the world to design, create, and optimize novel molecular entities with unprecedented efficiency. From a toxicological perspective, computational methods have guided the choices of medicinal chemists toward compounds displaying improved safety profiles. Even if the recent advances in the field are significant, many challenges remain active in the on-target and off-target prediction fields. Machine learning methods have shown their ability to identify molecules with safety concerns. However, they strongly depend on the abundance and diversity of data used for their training. Sharing such information among pharmaceutical companies remains extremely limited due to confidentiality reasons, but in this scenario, a recent concept named "federated learning" can help overcome such concerns. Within this framework, it is possible for companies to contribute to the training of common machine learning algorithms, using, but not sharing, their proprietary data. Very recently, Lhasa Limited organized a hackathon involving several industrial partners in order to assess the performance of their federated learning platform, called "Effiris". In this paper, we share our experience as Roche in participating in such an event, evaluating the performance of the federated algorithms and comparing them with those coming from our in-house-only machine learning models. Our aim is to highlight the advantages of federated learning and its intrinsic limitations and also suggest some points for potential improvements in the method.
Collapse
Affiliation(s)
- Davide Bassani
- Pharmaceutical Research &
Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| | - Alessandro Brigo
- Pharmaceutical Research &
Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| | - Andrea Andrews-Morger
- Pharmaceutical Research &
Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| |
Collapse
|
40
|
Zhang SY, Zhu XQ, Chen LH, He Y, Jing AM. Progress in research of risk prediction models for upper gastrointestinal bleeding. Shijie Huaren Xiaohua Zazhi 2023; 31:695-704. [DOI: 10.11569/wcjd.v31.i17.695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/28/2023] [Accepted: 09/01/2023] [Indexed: 09/08/2023] Open
Abstract
Upper gastrointestinal bleeding is a common gastrointes-tinal condition with a high incidence rate and great harm to the body. This paper reviews the rebleeding, endos-copic intervention, and death risk prediction models for upper gastrointestinal bleeding, including the study population, research methods, related risk factors, and model performance, in order to provide reference for clinicians to conduct risk assessment as soon as possible, formulate effective prevention and management plans, and improve patient survival outcomes.
Collapse
Affiliation(s)
- Shi-Yi Zhang
- Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Xiu-Qin Zhu
- Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Li-Hong Chen
- Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Yuan He
- Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - A-Min Jing
- Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| |
Collapse
|
41
|
Jiang S, Wang T, Zhang KH. Data-driven decision-making for precision diagnosis of digestive diseases. Biomed Eng Online 2023; 22:87. [PMID: 37658345 PMCID: PMC10472739 DOI: 10.1186/s12938-023-01148-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 08/15/2023] [Indexed: 09/03/2023] Open
Abstract
Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.
Collapse
Affiliation(s)
- Song Jiang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Ting Wang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Kun-He Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| |
Collapse
|
42
|
Lam A, Squires E, Tan S, Swen NJ, Barilla A, Kovoor J, Gupta A, Bacchi S, Khurana S. Artificial intelligence for predicting acute appendicitis: a systematic review. ANZ J Surg 2023; 93:2070-2078. [PMID: 37458222 DOI: 10.1111/ans.18610] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/06/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Paediatric appendicitis may be challenging to diagnose, and outcomes difficult to predict. While diagnostic and prognostic scores exist, artificial intelligence (AI) may be able to assist with these tasks. METHOD A systematic review was conducted aiming to evaluate the currently available evidence regarding the use of AI in the diagnosis and prognostication of paediatric appendicitis. In accordance with the PRISMA guidelines, the databases PubMed, EMBASE, and Cochrane Library were searched. This review was prospectively registered on PROSPERO. RESULTS Ten studies met inclusion criteria. All studies described the derivation and validation of AI models, and none described evaluation of the implementation of these models. Commonly used input parameters included varying combinations of demographic, clinical, laboratory, and imaging characteristics. While multiple studies used histopathological examination as the ground truth for a diagnosis of appendicitis, less robust techniques, such as the use of ICD10 codes, were also employed. Commonly used algorithms have included random forest models and artificial neural networks. High levels of model performance have been described for diagnosis of appendicitis and, to a lesser extent, subtypes of appendicitis (such as complicated versus uncomplicated appendicitis). Most studies did not provide all measures of model performance required to assess clinical usability. CONCLUSIONS The available evidence suggests the creation of prediction models for diagnosis and classification of appendicitis using AI techniques, is being increasingly explored. However, further implementation studies are required to demonstrate benefit in system or patient-centred outcomes with model deployment and to progress these models to the stage of clinical usability.
Collapse
Affiliation(s)
- Antoinette Lam
- University of Adelaide, Adelaide, South Australia, Australia
| | - Emily Squires
- Flinders University, Adelaide, South Australia, Australia
| | - Sheryn Tan
- University of Adelaide, Adelaide, South Australia, Australia
| | - Ng Jeng Swen
- University of Adelaide, Adelaide, South Australia, Australia
| | | | - Joshua Kovoor
- University of Adelaide, Adelaide, South Australia, Australia
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
- Gold Coast University Hospital, Gold Coast, Queensland, Australia
| | - Aashray Gupta
- University of Adelaide, Adelaide, South Australia, Australia
- Women's and Children's Hospital, Adelaide, South Australia, Australia
| | - Stephen Bacchi
- University of Adelaide, Adelaide, South Australia, Australia
- Flinders University, Adelaide, South Australia, Australia
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Sanjeev Khurana
- University of Adelaide, Adelaide, South Australia, Australia
- Women's and Children's Hospital, Adelaide, South Australia, Australia
| |
Collapse
|
43
|
Amoroso N, Quarto S, La Rocca M, Tangaro S, Monaco A, Bellotti R. An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease. Front Aging Neurosci 2023; 15:1238065. [PMID: 37719873 PMCID: PMC10501457 DOI: 10.3389/fnagi.2023.1238065] [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: 06/10/2023] [Accepted: 08/08/2023] [Indexed: 09/19/2023] Open
Abstract
The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human experts, especially from non-computational domains, approach artificial intelligence; this is particularly true for clinical applications where the transparency of the results is often compromised by the algorithmic complexity. Here, we investigate how Alzheimer's disease (AD) affects brain connectivity within a cohort of 432 subjects whose T1 brain Magnetic Resonance Imaging data (MRI) were acquired within the Alzheimer's Disease Neuroimaging Initiative (ADNI). In particular, the cohort included 92 patients with AD, 126 normal controls (NC) and 214 subjects with mild cognitive impairment (MCI). We show how graph theory-based models can accurately distinguish these clinical conditions and how Shapley values, borrowed from game theory, can be adopted to make these models intelligible and easy to interpret. Explainability analyses outline the role played by regions like putamen, middle and superior temporal gyrus; from a class-related perspective, it is possible to outline specific regions, such as hippocampus and amygdala for AD and posterior cingulate and precuneus for MCI. The approach is general and could be adopted to outline how brain connectivity affects specific brain regions.
Collapse
Affiliation(s)
- Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Silvano Quarto
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Marianna La Rocca
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| |
Collapse
|
44
|
Cellina M, Cacioppa LM, Cè M, Chiarpenello V, Costa M, Vincenzo Z, Pais D, Bausano MV, Rossini N, Bruno A, Floridi C. Artificial Intelligence in Lung Cancer Screening: The Future Is Now. Cancers (Basel) 2023; 15:4344. [PMID: 37686619 PMCID: PMC10486721 DOI: 10.3390/cancers15174344] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/27/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023] Open
Abstract
Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients' survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients' outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called "virtual biopsy". This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.
Collapse
Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, 20121 Milano, Italy;
| | - Laura Maria Cacioppa
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (L.M.C.); (N.R.); (A.B.)
- Division of Interventional Radiology, Department of Radiological Sciences, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Vittoria Chiarpenello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Marco Costa
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Zakaria Vincenzo
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Daniele Pais
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Maria Vittoria Bausano
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Nicolò Rossini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (L.M.C.); (N.R.); (A.B.)
| | - Alessandra Bruno
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (L.M.C.); (N.R.); (A.B.)
| | - Chiara Floridi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (L.M.C.); (N.R.); (A.B.)
- Division of Interventional Radiology, Department of Radiological Sciences, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
- Division of Radiology, Department of Radiological Sciences, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
| |
Collapse
|
45
|
Fang W, Wu J, Cheng M, Zhu X, Du M, Chen C, Liao W, Zhi K, Pan W. Diagnosis of invasive fungal infections: challenges and recent developments. J Biomed Sci 2023; 30:42. [PMID: 37337179 DOI: 10.1186/s12929-023-00926-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/13/2023] [Indexed: 06/21/2023] Open
Abstract
BACKGROUND The global burden of invasive fungal infections (IFIs) has shown an upsurge in recent years due to the higher load of immunocompromised patients suffering from various diseases. The role of early and accurate diagnosis in the aggressive containment of the fungal infection at the initial stages becomes crucial thus, preventing the development of a life-threatening situation. With the changing demands of clinical mycology, the field of fungal diagnostics has evolved and come a long way from traditional methods of microscopy and culturing to more advanced non-culture-based tools. With the advent of more powerful approaches such as novel PCR assays, T2 Candida, microfluidic chip technology, next generation sequencing, new generation biosensors, nanotechnology-based tools, artificial intelligence-based models, the face of fungal diagnostics is constantly changing for the better. All these advances have been reviewed here giving the latest update to our readers in the most orderly flow. MAIN TEXT A detailed literature survey was conducted by the team followed by data collection, pertinent data extraction, in-depth analysis, and composing the various sub-sections and the final review. The review is unique in its kind as it discusses the advances in molecular methods; advances in serology-based methods; advances in biosensor technology; and advances in machine learning-based models, all under one roof. To the best of our knowledge, there has been no review covering all of these fields (especially biosensor technology and machine learning using artificial intelligence) with relevance to invasive fungal infections. CONCLUSION The review will undoubtedly assist in updating the scientific community's understanding of the most recent advancements that are on the horizon and that may be implemented as adjuncts to the traditional diagnostic algorithms.
Collapse
Affiliation(s)
- Wenjie Fang
- Department of Dermatology, Shanghai Key Laboratory of Molecular Medical Mycology, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Junqi Wu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
- Shanghai Engineering Research Center of Lung Transplantation, Shanghai, 200433, China
| | - Mingrong Cheng
- Department of Anorectal Surgery, The Third Affiliated Hospital of Guizhou Medical University, Guizhou, 558000, China
| | - Xinlin Zhu
- Department of Dermatology, Shanghai Key Laboratory of Molecular Medical Mycology, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Mingwei Du
- Department of Dermatology, Shanghai Key Laboratory of Molecular Medical Mycology, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
- Shanghai Engineering Research Center of Lung Transplantation, Shanghai, 200433, China
| | - Wanqing Liao
- Department of Dermatology, Shanghai Key Laboratory of Molecular Medical Mycology, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Kangkang Zhi
- Department of Vascular and Endovascular Surgery, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China.
| | - Weihua Pan
- Department of Dermatology, Shanghai Key Laboratory of Molecular Medical Mycology, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China.
| |
Collapse
|
46
|
Yildirim O, Ozkaya B. Prediction of biogas production of industrial scale anaerobic digestion plant by machine learning algorithms. CHEMOSPHERE 2023:138976. [PMID: 37230302 DOI: 10.1016/j.chemosphere.2023.138976] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/25/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023]
Abstract
In the anaerobic digestion (AD) process there are some difficulties in maintaining process stability due to the complexity of the system. The variability of the raw material coming to the facility, temperature fluctuations and pH changes as a result of microbial processes cause process instability and require continuous monitoring and control. Increasing continuous monitoring, and internet of things applications within the scope of Industry 4.0 in AD facilities can provide process stability control and early intervention. In this study, five different machine learning (ML) algorithms (RF, ANN, KNN, SVR, and XGBoost) were used to describe and predict the correlation between operational parameters and biogas production quantities collected from a real-scale anaerobic digestion plant. The KNN algorithm had the lowest accuracy in predicting total biogas production over time, while the RF model had the highest prediction accuracy of all prediction models. The RF method produced the best prediction, with an R2 of 0.9242, and it was followed by XGBoost, ANN, SVR, and KNN (with R2 values of 0.8960, 0.8703, 0.8655, 0.8326, respectively). Real-time process control will be provided and process stability will be maintained by preventing low-efficiency biogas production with the integration of ML applications into AD facilities.
Collapse
Affiliation(s)
- Oznur Yildirim
- Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa Campus, 34220, Istanbul, Turkey.
| | - Bestami Ozkaya
- Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa Campus, 34220, Istanbul, Turkey
| |
Collapse
|
47
|
Park D, Son SI, Kim MS, Kim TY, Choi JH, Lee SE, Hong D, Kim MC. Machine learning predictive model for aspiration screening in hospitalized patients with acute stroke. Sci Rep 2023; 13:7835. [PMID: 37188793 DOI: 10.1038/s41598-023-34999-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/11/2023] [Indexed: 05/17/2023] Open
Abstract
Dysphagia is a fatal condition after acute stroke. We established machine learning (ML) models for screening aspiration in patients with acute stroke. This retrospective study enrolled patients with acute stroke admitted to a cerebrovascular specialty hospital between January 2016 and June 2022. A videofluoroscopic swallowing study (VFSS) confirmed aspiration. We evaluated the Gugging Swallowing Screen (GUSS), an early assessment tool for dysphagia, in all patients and compared its predictive value with ML models. Following ML algorithms were applied: regularized logistic regressions (ridge, lasso, and elastic net), random forest, extreme gradient boosting, support vector machines, k-nearest neighbors, and naïve Bayes. We finally analyzed data from 3408 patients, and 448 of them had aspiration on VFSS. The GUSS showed an area under the receiver operating characteristics curve (AUROC) of 0.79 (0.77-0.81). The ridge regression model was the best model among all ML models, with an AUROC of 0.81 (0.76-0.86), an F1 measure of 0.45. Regularized logistic regression models exhibited higher sensitivity (0.66-0.72) than the GUSS (0.64). Feature importance analyses revealed that the modified Rankin scale was the most important feature of ML performance. The proposed ML prediction models are valid and practical for screening aspiration in patients with acute stroke.
Collapse
Affiliation(s)
- Dougho Park
- Department of Medical Science and Engineering, School of Convergence Science and Technology, Pohang University of Science and Technology, Pohang, Republic of Korea.
- Department of Rehabilitation Medicine, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea.
| | - Seok Il Son
- Occupational Therapy Department of Rehabilitation Center, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| | - Min Sol Kim
- Occupational Therapy Department of Rehabilitation Center, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| | - Tae Yeon Kim
- Speech-Language Therapy Department of Rehabilitation Center, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| | - Jun Hwa Choi
- Department of Quality Improvement, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| | - Sang-Eok Lee
- Department of Rehabilitation Medicine, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| | - Daeyoung Hong
- Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| | - Mun-Chul Kim
- Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| |
Collapse
|
48
|
Miceli G, Basso MG, Rizzo G, Pintus C, Cocciola E, Pennacchio AR, Tuttolomondo A. Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review. Biomedicines 2023; 11:biomedicines11041138. [PMID: 37189756 DOI: 10.3390/biomedicines11041138] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/29/2023] [Accepted: 04/06/2023] [Indexed: 05/17/2023] Open
Abstract
The correct recognition of the etiology of ischemic stroke (IS) allows tempestive interventions in therapy with the aim of treating the cause and preventing a new cerebral ischemic event. Nevertheless, the identification of the cause is often challenging and is based on clinical features and data obtained by imaging techniques and other diagnostic exams. TOAST classification system describes the different etiologies of ischemic stroke and includes five subtypes: LAAS (large-artery atherosclerosis), CEI (cardio embolism), SVD (small vessel disease), ODE (stroke of other determined etiology), and UDE (stroke of undetermined etiology). AI models, providing computational methodologies for quantitative and objective evaluations, seem to increase the sensitivity of main IS causes, such as tomographic diagnosis of carotid stenosis, electrocardiographic recognition of atrial fibrillation, and identification of small vessel disease in magnetic resonance images. The aim of this review is to provide overall knowledge about the most effective AI models used in the differential diagnosis of ischemic stroke etiology according to the TOAST classification. According to our results, AI has proven to be a useful tool for identifying predictive factors capable of subtyping acute stroke patients in large heterogeneous populations and, in particular, clarifying the etiology of UDE IS especially detecting cardioembolic sources.
Collapse
Affiliation(s)
- Giuseppe Miceli
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Maria Grazia Basso
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Giuliana Rizzo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Chiara Pintus
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Elena Cocciola
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Andrea Roberta Pennacchio
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| | - Antonino Tuttolomondo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), Università Degli Studi di Palermo, Piazza Delle Cliniche 2, 90127 Palermo, Italy
- Internal Medicine and Stroke Care Ward, University Hospital, Policlinico "P. Giaccone", 90141 Palermo, Italy
| |
Collapse
|
49
|
Weissman DN, Radonovich LJ. Importance of and Approach to Taking a History of Exposures to Occupational Respiratory Hazards. Semin Respir Crit Care Med 2023; 44:396-404. [PMID: 37015287 DOI: 10.1055/s-0043-1766120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
Occupational respiratory diseases are caused by exposure to respiratory hazards at work. It is important to document those exposures and whether they are causing or exacerbating disease because these determinations can have important impacts on diagnosis, treatment, job restrictions, and eligibility for benefits. Without investigation, it is easy to miss clinically relevant exposures, especially in those with chronic diseases that can have work and nonwork causes. The first and most important step in identifying exposures to respiratory hazards at work is to take an appropriate history. For efficiency, this is a two-step process. An initial quick screening history is done by asking only a few questions. Follow-up questions are asked if there are positive responses to the screening questions or if an occupational etiology is suspected based on the clinical presentation. Electronic health records have promise for facilitating this process. Follow-up to the screening history may include additional questions, evaluating additional sources of information about workplace exposures, and medical testing. Radiographic findings or tests conducted on noninvasive samples or lung tissue can be used as biomarkers. Online resources can be used to learn more about exposures associated with occupations and industries and to see if investigations evaluating exposures were performed in the patient's own workplace. It is important to adhere to the patient's wishes about contacting the employer. With patient consent, the employer can be an important source of information about exposures and, if a problem exists, has an important role in taking corrective action. Consultation for challenging cases is available from a variety of professional and governmental entities. If a clinician identifies a significant public health issue, such as an occupational disease outbreak, it is important to notify relevant public health authorities so that steps can be taken to prevent additional exposures and appropriately care for those already exposed.
Collapse
Affiliation(s)
- David N Weissman
- Respiratory Health Division, National Institute for Occupational Safety and Health, Morgantown, West Virginia
| | - Lewis J Radonovich
- Respiratory Health Division, National Institute for Occupational Safety and Health, Morgantown, West Virginia
| |
Collapse
|
50
|
Rathore AS, Nikita S, Thakur G, Mishra S. Artificial intelligence and machine learning applications in biopharmaceutical manufacturing. Trends Biotechnol 2023; 41:497-510. [PMID: 36117026 DOI: 10.1016/j.tibtech.2022.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 07/08/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022]
Abstract
Artificial intelligence and machine learning (AI-ML) offer vast potential in optimal design, monitoring, and control of biopharmaceutical manufacturing. The driving forces for adoption of AI-ML techniques include the growing global demand for biotherapeutics and the shift toward Industry 4.0, spurring the rise of integrated process platforms and continuous processes that require intelligent, automated supervision. This review summarizes AI-ML applications in biopharmaceutical manufacturing, with a focus on the most used AI-ML algorithms, including multivariate data analysis, artificial neural networks, and reinforcement learning. Perspectives on the future growth of AI-ML applications in the area and the challenges of implementing these techniques at manufacturing scale are also presented.
Collapse
Affiliation(s)
- Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India.
| | - Saxena Nikita
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Garima Thakur
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Somesh Mishra
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| |
Collapse
|