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Jeong SM, Kim S, Lee EC, Kim HJ. Exploring Spectrogram-Based Audio Classification for Parkinson's Disease: A Study on Speech Classification and Qualitative Reliability Verification. SENSORS (BASEL, SWITZERLAND) 2024; 24:4625. [PMID: 39066023 PMCID: PMC11280556 DOI: 10.3390/s24144625] [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: 06/12/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024]
Abstract
Patients suffering from Parkinson's disease suffer from voice impairment. In this study, we introduce models to classify normal and Parkinson's patients using their speech. We used an AST (audio spectrogram transformer), a transformer-based speech classification model that has recently outperformed CNN-based models in many fields, and a CNN-based PSLA (pretraining, sampling, labeling, and aggregation), a high-performance model in the existing speech classification field, for the study. This study compares and analyzes the models from both quantitative and qualitative perspectives. First, qualitatively, PSLA outperformed AST by more than 4% in accuracy, and the AUC was also higher, with 94.16% for AST and 97.43% for PSLA. Furthermore, we qualitatively evaluated the ability of the models to capture the acoustic features of Parkinson's through various CAM (class activation map)-based XAI (eXplainable AI) models such as GradCAM and EigenCAM. Based on PSLA, we found that the model focuses well on the muffled frequency band of Parkinson's speech, and the heatmap analysis of false positives and false negatives shows that the speech features are also visually represented when the model actually makes incorrect predictions. The contribution of this paper is that we not only found a suitable model for diagnosing Parkinson's through speech using two different types of models but also validated the predictions of the model in practice.
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Affiliation(s)
- Seung-Min Jeong
- Department of AI & Informatics, Graduate School, Sangmyung University, Hongjimun 2-gil 20, Jongno-gu, Seoul 03016, Republic of Korea; (S.-M.J.); (S.K.)
| | - Seunghyun Kim
- Department of AI & Informatics, Graduate School, Sangmyung University, Hongjimun 2-gil 20, Jongno-gu, Seoul 03016, Republic of Korea; (S.-M.J.); (S.K.)
| | - Eui Chul Lee
- Department of Human-Centered Artificial Intelligence, Sangmyung University, Hongjimun 2-gil 20, Jongno-gu, Seoul 03016, Republic of Korea
| | - Han Joon Kim
- Department of Neurology, Seoul National University College of Medicine, Seoul National University Hospital, Daehak-ro 101, Jongno-gu, Seoul 03080, Republic of Korea
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Olawade DB, Teke J, Fapohunda O, Weerasinghe K, Usman SO, Ige AO, Clement David-Olawade A. Leveraging artificial intelligence in vaccine development: A narrative review. J Microbiol Methods 2024; 224:106998. [PMID: 39019262 DOI: 10.1016/j.mimet.2024.106998] [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: 06/10/2024] [Revised: 07/12/2024] [Accepted: 07/12/2024] [Indexed: 07/19/2024]
Abstract
Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficient. The advent of artificial intelligence (AI) has ushered in a new era in vaccine design, offering unprecedented opportunities to expedite the process. This narrative review explores the role of AI in vaccine development, focusing on antigen selection, epitope prediction, adjuvant identification, and optimization strategies. AI algorithms, including machine learning and deep learning, leverage genomic data, protein structures, and immune system interactions to predict antigenic epitopes, assess immunogenicity, and prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate the rational design of immunogens and the identification of novel adjuvant candidates with optimal safety and efficacy profiles. Challenges such as data heterogeneity, model interpretability, and regulatory considerations must be addressed to realize the full potential of AI in vaccine development. Integrating emerging technologies, such as single-cell omics and synthetic biology, promises to enhance vaccine design precision and scalability. This review underscores the transformative impact of AI on vaccine development and highlights the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases.
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Affiliation(s)
- David B Olawade
- Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, United Kingdom; Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom.
| | - Jennifer Teke
- Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom
| | | | - Kusal Weerasinghe
- Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom
| | - Sunday O Usman
- Department of Systems and Industrial Engineering, University of Arizona, USA
| | - Abimbola O Ige
- Department of Chemistry, Faculty of Science, University of Ibadan, Ibadan, Nigeria
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3
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Chu R, Wei J, Lu W, Dong C, Chen Y. MFS-DBF: A trustworthy multichannel feature sieve and decision boundary formulation system for Obstructive Sleep Apnea detection. Comput Biol Med 2024; 179:108842. [PMID: 38996552 DOI: 10.1016/j.compbiomed.2024.108842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 04/15/2024] [Accepted: 06/04/2024] [Indexed: 07/14/2024]
Abstract
The fine identification of sleep apnea events is instrumental in Obstructive Sleep Apnea (OSA) diagnosis. The development of sleep apnea event detection algorithms based on polysomnography is becoming a research hotspot in medical signal processing. In this paper, we propose an Inverse-Projection based Visualization System (IPVS) for sleep apnea event detection algorithms. The IPVS consists of a feature dimensionality reduction module and a feature reconstruction module. First, features of blood oxygen saturation and nasal airflow are extracted and used as input data for event analysis. Then, visual analysis is conducted on the feature distribution for apnea events. Next, dimensionality reduction and reconstruction methods are combined to achieve the dynamic visualization of sleep apnea event feature sets and the visual analysis of classifier decision boundaries. Moreover, the decision-making consistency is explored for various sleep apnea event detection classifiers, which provides researchers and users with an intuitive understanding of the detection algorithm. We applied the IPVS to an OSA detection algorithm with an accuracy of 84% and a diagnostic accuracy of 92% on a publicly available dataset. The experimental results show that the consistency between our visualization results and prior medical knowledge provides strong evidence for the practicality of the proposed system. For clinical practice, the IPVS can guide users to focus on samples with higher uncertainty presented by the OSA detection algorithm, reducing the workload and improving the efficiency of clinical diagnosis, which in turn increases the value of trust.
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Affiliation(s)
- Ronghe Chu
- College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China.
| | - Jianguo Wei
- College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China.
| | - Wenhuan Lu
- College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China.
| | - Chaoyu Dong
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China.
| | - Yibing Chen
- Department of Pulmonary and Critical Care Medicine, First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China.
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4
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Lange M, Lowe A, Kayser I, Schaller A. Approaches for the use of Artificial Intelligence in workplace health promotion and prevention: A systematic scoping review. JMIR AI 2024. [PMID: 38989904 DOI: 10.2196/53506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is an umbrella term for various algorithms and rapidly emerging technologies with huge potential for workplace health promotion and prevention (WHPP). WHPP interventions aim to improve people's health and well-being through behavioral and organizational measures or by minimizing the burden of workplace-related diseases and associated risk factors. While AI has been the focus of research in other health-related fields, such as Public Health or biomedicine, the transition of AI into WHPP research has yet to be systematically investigated. OBJECTIVE The systematic scoping review aims to comprehensively assess an overview of the current use of AI in WHPP. The results will be then used to point to future research directions. The following research questions were derived: (1) what are the study characteristics of studies on AI algorithms and technologies in the context of WHPP, (2) what specific WHPP fields (prevention, behavioral, and organizational approaches) were addressed by the AI algorithms and technologies, and (3) what kind of interventions lead to which outcomes? METHODS A systematic scoping literature review (PRISMA-ScR) was conducted in the three academic databases PubMed, IEEE, and ACM in July 2023, searching for articles published between January 2000 and December 2023. Studies needed to be 1) peer-reviewed, 2) written in English, and 3) focused on any AI-based algorithm or technology that (4) were conducted in the context of WHPP or (5) an associated field. Information on study design, AI algorithms and technologies, WHPP fields, and the PICO framework were extracted blindly with Rayyan and summarized. RESULTS A total of ten studies were included. Risk prevention and modeling were the most identified WHPP fields (n=6), followed by behavioral health promotion (n=4) and organizational health promotion (n=1). Four studies focused on mental health. Most AI algorithms were machine learning-based, and three studies used combined deep learning algorithms. AI algorithms and technologies were primarily implemented in smartphone applications (eg, in the form of a Chatbot) or used the smartphone as a data source (eg, GPS). Behavioral approaches ranged from 8 to 12 weeks and were compared to control groups. Three studies evaluated the robustness and accuracy of an AI model or framework. CONCLUSIONS Although AI has caught increasing attention in health-related research, the review reveals that AI in WHPP is marginally investigated. Our results indicate that AI is promising for individualization and risk prediction in WHPP, but current research does not cover the scope of WHPP. Beyond that, future research will profit from an extended range of research in all fields of WHPP, longitudinal data, and reporting guidelines. CLINICALTRIAL Registered on 5th July 2023 at Open Science Framework [1].
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Affiliation(s)
- Martin Lange
- Department of Fitness & Health, IST University of Applied Science, Erkrather Straße 220a-c, Duesseldorf, DE
| | - Alexandra Lowe
- Department of Fitness & Health, IST University of Applied Science, Erkrather Straße 220a-c, Duesseldorf, DE
| | - Ina Kayser
- Department of Communication & Business, IST University of Applied Science, Duesseldorf, DE
| | - Andrea Schaller
- Institute of Sport Science, Department of Human Sciences, University of the Bundeswehr Munich, Munich, DE
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5
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Zheng Y, Ren R, Zuo T, Chen X, Li H, Xie C, Weng M, He C, Xu M, Wang L, Li N, Li X. Prediction of early-phase cytomegalovirus pneumonia in post-stem cell transplantation using a deep learning model. Technol Health Care 2024:THC240597. [PMID: 39058469 DOI: 10.3233/thc-240597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2024]
Abstract
BACKGROUND Diagnostic challenges exist for CMV pneumonia in post-hematopoietic stem cell transplantation (post-HSCT) patients, despite early-phase radiographic changes. OBJECTIVE The study aims to employ a deep learning model distinguishing CMV pneumonia from COVID-19 pneumonia, community-acquired pneumonia, and normal lungs post-HSCT. METHODS Initially, 6 neural network models were pre-trained with COVID-19 pneumonia, community-acquired pneumonia, and normal lung CT images from Kaggle's COVID multiclass dataset (Dataset A), then Dataset A was combined with the CMV pneumonia images from our center, forming Dataset B. We use a few-shot transfer learning strategy to fine-tune the pre-trained models and evaluate model performance in Dataset B. RESULTS 34 cases of CMV pneumonia were found between January 2018 and December 2022 post-HSCT. Dataset A contained 1681 images of each subgroup from Kaggle. Combined with Dataset A, Dataset B was initially formed by 98 images of CMV pneumonia and normal lung. The optimal model (Xception) achieved an accuracy of 0.9034. Precision, recall, and F1-score all reached 0.9091, with an AUC of 0.9668 in the test set of Dataset B. CONCLUSIONS This framework demonstrates the deep learning model's ability to distinguish rare pneumonia types utilizing a small volume of CT images, facilitating early detection of CMV pneumonia post-HSCT.
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Affiliation(s)
- Yanhua Zheng
- Fujian Provincial Key Laboratory on Hematology, Fujian Institute of Hematology, Fujian Medical University Union Hospital, Fuzhou, China
- Department of Hematology, The First Hospital of China Medical University, Shenyang, China
| | - Ruilin Ren
- Department of Hematology, Fujian Medical University Union Hospital, Fuzhou, China
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Teng Zuo
- Urology Department, Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Xuan Chen
- Department of Hematology, Fujian Medical University Union Hospital, Fuzhou, China
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Hanxuan Li
- Department of Hematology, Fujian Medical University Union Hospital, Fuzhou, China
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Cheng Xie
- Department of Hematology, Fujian Medical University Union Hospital, Fuzhou, China
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Meiling Weng
- Department of Hematology, Fujian Medical University Union Hospital, Fuzhou, China
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Chunxiao He
- Fujian Provincial Key Laboratory on Hematology, Fujian Institute of Hematology, Fujian Medical University Union Hospital, Fuzhou, China
- Department of Hematology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Min Xu
- Fujian Provincial Key Laboratory on Hematology, Fujian Institute of Hematology, Fujian Medical University Union Hospital, Fuzhou, China
- Department of Hematology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Lili Wang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Nainong Li
- Fujian Provincial Key Laboratory on Hematology, Fujian Institute of Hematology, Fujian Medical University Union Hospital, Fuzhou, China
- Department of Hematology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Xiaofan Li
- Fujian Provincial Key Laboratory on Hematology, Fujian Institute of Hematology, Fujian Medical University Union Hospital, Fuzhou, China
- Department of Hematology, Fujian Medical University Union Hospital, Fuzhou, China
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6
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Zhan C, Dai Z, Yin S, Carroll KC, Soltanian MR. Conceptualizing future groundwater models through a ternary framework of multisource data, human expertise, and machine intelligence. WATER RESEARCH 2024; 257:121679. [PMID: 38696982 DOI: 10.1016/j.watres.2024.121679] [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: 02/23/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/04/2024]
Abstract
Groundwater models are essential for understanding aquifer systems behavior and effective water resources spatio-temporal distributions, yet they are often hindered by challenges related to model assumptions, parametrization, uncertainty, and computational efficiency. Machine intelligence, especially deep learning, promises a paradigm shift in overcoming these challenges. A critical examination of existing machine-driven methods reveals the inherent limitations, particularly in terms of the interpretability and the ability to generalize findings. To overcome these challenges, we develop a ternary framework that synergizes the valuable insights from multisource data, human expertise, and machine intelligence. This framework capitalizes on the distinct strengths of each element: the value and relevance of multisource data, the innovative capacity of human expertise, and the analytical efficiency of machine intelligence. Our goal is to conceptualize sustainable water management practices and enhance our understanding and predictive capabilities of groundwater systems. Unlike approaches that rely solely on abundant data, our framework emphasizes the quality and strategic use of available data, combined with human intellect and advanced computing, to overcome current limitations and pave the way for more realistic groundwater simulations.
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Affiliation(s)
- Chuanjun Zhan
- School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China
| | - Zhenxue Dai
- School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China; College of Construction Engineering, Jilin University, Changchun 130026, China; Institute of Intelligent Simulation and Early Warning for Subsurface Environment, Jilin University, Changchun 130026, China.
| | - Shangxian Yin
- North China Institute of Science & Technology, Langfang 065201, China.
| | - Kenneth C Carroll
- Department of Plant & Environmental Science, New Mexico State University, Las Cruces, NM, USA
| | - Mohamad Reza Soltanian
- Departments of Geosciences and Environmental Engineering, University of Cincinnati, Cincinnati, OH, USA
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Xu M, Islam M, Bai L, Ren H. Privacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2291-2302. [PMID: 38381643 DOI: 10.1109/tmi.2024.3364969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Deep Neural Networks (DNNs) based semantic segmentation of the robotic instruments and tissues can enhance the precision of surgical activities in robot-assisted surgery. However, in biological learning, DNNs cannot learn incremental tasks over time and exhibit catastrophic forgetting, which refers to the sharp decline in performance on previously learned tasks after learning a new one. Specifically, when data scarcity is the issue, the model shows a rapid drop in performance on previously learned instruments after learning new data with new instruments. The problem becomes worse when it limits releasing the dataset of the old instruments for the old model due to privacy concerns and the unavailability of the data for the new or updated version of the instruments for the continual learning model. For this purpose, we develop a privacy-preserving synthetic continual semantic segmentation framework by blending and harmonizing (i) open-source old instruments foreground to the synthesized background without revealing real patient data in public and (ii) new instruments foreground to extensively augmented real background. To boost the balanced logit distillation from the old model to the continual learning model, we design overlapping class-aware temperature normalization (CAT) by controlling model learning utility. We also introduce multi-scale shifted-feature distillation (SD) to maintain long and short-range spatial relationships among the semantic objects where conventional short-range spatial features with limited information reduce the power of feature distillation. We demonstrate the effectiveness of our framework on the EndoVis 2017 and 2018 instrument segmentation dataset with a generalized continual learning setting. Code is available at https://github.com/XuMengyaAmy/Synthetic_CAT_SD.
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Nadeem MS, Kurugollu F, Atlam HF, Franqueira VN. Weapon Violence Dataset 2.0: A synthetic dataset for violence detection. Data Brief 2024; 54:110448. [PMID: 38725552 PMCID: PMC11079471 DOI: 10.1016/j.dib.2024.110448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/04/2024] [Accepted: 04/15/2024] [Indexed: 05/12/2024] Open
Abstract
In the current era, satisfying the appetite of data hungry models is becoming an increasingly challenging task. This challenge is particularly magnified in research areas characterised by sensitivity, where the quest for genuine data proves to be elusive. The study of violence serves as a poignant example, entailing ethical considerations and compounded by the scarcity of authentic, real-world data that is predominantly accessible only to law enforcement agencies. Existing datasets in this field often resort to using content from movies or open-source video platforms like YouTube, further emphasising the scarcity of authentic data. To address this, our dataset aims to pioneer a new approach by creating the first synthetic virtual dataset for violence detection, named the Weapon Violence Dataset (WVD). The dataset is generated by creating virtual violence scenarios inside the photo-realistic video game namely: Grand Theft Auto-V (GTA-V). This dataset includes carefully selected video clips of person-to-person fights captured from a frontal view, featuring various weapons-both hot and cold across different times of the day. Specifically, WVD contains three categories: Hot violence and Cold violence (representing the violence category) as well as No violence (constituting the control class). The dataset is designed and created in a way that will enable the research community to train deep models on such synthetic data with the ability to increase the data corpus if the needs arise. The dataset is publicly available on Kaggle and comprises normal RGB and optic flow videos.
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Affiliation(s)
- Muhammad Shahroz Nadeem
- School of Technology, Business and Arts, University of Suffolk, Ipswich IP4 1QJ, United Kingdom
| | - Fatih Kurugollu
- College of Computing and Informatics, Department of Computer Science, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Hany F. Atlam
- Cyber security Centre, Warwick Manufacturing Group(WMG), University of Warwick, Coventry CV4 7AL, United Kingdom
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Yang H, Zhu D, He S, Xu Z, Liu Z, Zhang W, Cai J. Enhancing psychiatric rehabilitation outcomes through a multimodal multitask learning model based on BERT and TabNet: An approach for personalized treatment and improved decision-making. Psychiatry Res 2024; 336:115896. [PMID: 38626625 DOI: 10.1016/j.psychres.2024.115896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 04/03/2024] [Accepted: 04/05/2024] [Indexed: 04/18/2024]
Abstract
Evaluating the rehabilitation status of individuals with serious mental illnesses (SMI) necessitates a comprehensive analysis of multimodal data, including unstructured text records and structured diagnostic data. However, progress in the effective assessment of rehabilitation status remains limited. Our study develops a deep learning model integrating Bidirectional Encoder Representations from Transformers (BERT) and TabNet through a late fusion strategy to enhance rehabilitation prediction, including referral risk, dangerous behaviors, self-awareness, and medication adherence, in patients with SMI. BERT processes unstructured textual data, such as doctor's notes, whereas TabNet manages structured diagnostic information. The model's interpretability function serves to assist healthcare professionals in understanding the model's predictive decisions, improving patient care. Our model exhibited excellent predictive performance for all four tasks, with an accuracy exceeding 0.78 and an area under the curve of 0.70. In addition, a series of tests proved the model's robustness, fairness, and interpretability. This study combines multimodal and multitask learning strategies into a model and applies it to rehabilitation assessment tasks, offering a promising new tool that can be seamlessly integrated with the clinical workflow to support the provision of optimized patient care.
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Affiliation(s)
- Hongyi Yang
- School of Design, Shanghai Jiao Tong University, Shanghai, China
| | - Dian Zhu
- School of Design, Shanghai Jiao Tong University, Shanghai, China
| | - Siyuan He
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhiqi Xu
- School of Design, Shanghai Jiao Tong University, Shanghai, China
| | - Zhao Liu
- School of Design, Shanghai Jiao Tong University, Shanghai, China.
| | - Weibo Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China; Mental Health Branch, China Hospital Development Institute, Shanghai Jiao Tong University, Shanghai, China.
| | - Jun Cai
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Mental Health Branch, China Hospital Development Institute, Shanghai Jiao Tong University, Shanghai, China.
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Fjeldstad J, Qi W, Mercuri N, Siddique N, Meriano J, Krivoi A, Nayot D. An artificial intelligence tool predicts blastocyst development from static images of fresh mature oocytes. Reprod Biomed Online 2024; 48:103842. [PMID: 38552566 DOI: 10.1016/j.rbmo.2024.103842] [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: 08/29/2023] [Revised: 01/04/2024] [Accepted: 01/10/2024] [Indexed: 06/11/2024]
Abstract
RESEARCH QUESTION Can a deep learning image analysis model be developed to assess oocyte quality by predicting blastocyst development from images of denuded mature oocytes? DESIGN A deep learning model was developed utilizing 37,133 static oocyte images with associated laboratory outcomes from eight fertility clinics (six countries). A subset of data (n = 7807) was allocated to test model performance. External model validation was conducted to assess generalizability and robustness on new data (n = 12,357) from two fertility clinics (two countries). Performance was assessed by calculating area under the curve (AUC), balanced accuracy, specificity and sensitivity. Subgroup analyses were performed on the test dataset for age group, male factor and geographical location of the clinic. Model probabilities of the external dataset were converted to a 0-10 scoring scale to facilitate analysis of correlation with blastocyst development and quality. RESULTS The deep learning model demonstrated AUC of 0.64, balanced accuracy of 0.60, specificity of 0.55 and sensitivity of 0.65 on the test dataset. Subgroup analyses displayed the highest performance for age group 38-39 years (AUC 0.68), a negligible impact of male factor, and good model generalizability across geographical locations. Model performance was confirmed on external data: AUC of 0.63, balanced accuracy of 0.58, specificity of 0.57 and sensitivity of 0.59. Analysis of the scoring scale revealed that higher scoring oocytes correlated with higher likelihood of blastocyst development and good-quality blastocyst formation. CONCLUSION The deep learning model showed a favourable performance for the evaluation of oocytes in terms of competence to develop into a blastocyst, and when the predictions were converted into scores, they correlated with blastocyst quality. This represents a significant first step in oocyte evaluation for scientific and clinical applications.
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Affiliation(s)
| | - Weikai Qi
- Future Fertility, Toronto, Ontario, Canada
| | | | | | | | | | - Dan Nayot
- Future Fertility, Toronto, Ontario, Canada
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11
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Yuan Z, Yang D, Zhao J, Liang Y. Enhancement of OCT en faceimages by unsupervised deep learning. Phys Med Biol 2024; 69:115042. [PMID: 38749469 DOI: 10.1088/1361-6560/ad4c52] [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: 12/21/2023] [Accepted: 05/15/2024] [Indexed: 05/31/2024]
Abstract
Objective. The quality of optical coherence tomography (OCT)en faceimages is crucial for clinical visualization of early disease. As a three dimensional and coherent imaging, defocus and speckle noise are inevitable, which seriously affect evaluation of microstructure of bio-samples in OCT images. The deep learning has demonstrated great potential in OCT refocusing and denoising, but it is limited by the difficulty of sufficient paired training data. This work aims to develop an unsupervised method to enhance the quality of OCTen faceimages.Approach. We proposed an unsupervised deep learning-based pipeline. The unregistered defocused conventional OCT images and focused speckle-free OCT images were collected by a home-made speckle modulating OCT system to construct the dataset. The image enhancement model was trained with the cycle training strategy. Finally, the speckle noise and defocus were both effectively improved.Main results. The experimental results on complex bio-samples indicated that the proposed method is effective and generalized in enhancing the quality of OCTen faceimages.Significance. The proposed unsupervised deep learning method helps to reduce the complexity of data construction, which is conducive to practical applications in OCT bio-sample imaging.
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Affiliation(s)
- Zhuoqun Yuan
- Institute of Modern Optics, Nankai University, Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology, Tianjin 300350, People's Republic of China
| | - Di Yang
- Institute of Modern Optics, Nankai University, Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology, Tianjin 300350, People's Republic of China
| | - Jingzhu Zhao
- Department of Thyroid and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, People's Republic of China
| | - Yanmei Liang
- Institute of Modern Optics, Nankai University, Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology, Tianjin 300350, People's Republic of China
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12
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Goetz L, Seedat N, Vandersluis R, van der Schaar M. Generalization-a key challenge for responsible AI in patient-facing clinical applications. NPJ Digit Med 2024; 7:126. [PMID: 38773304 PMCID: PMC11109198 DOI: 10.1038/s41746-024-01127-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 04/25/2024] [Indexed: 05/23/2024] Open
Affiliation(s)
- Lea Goetz
- Artificial Intelligence and Machine Learning, GSK, London, UK.
| | - Nabeel Seedat
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
| | | | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
- Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, UK
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Uddin MG, Rahman A, Rosa Taghikhah F, Olbert AI. Data-driven evolution of water quality models: An in-depth investigation of innovative outlier detection approaches-A case study of Irish Water Quality Index (IEWQI) model. WATER RESEARCH 2024; 255:121499. [PMID: 38552494 DOI: 10.1016/j.watres.2024.121499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 02/09/2024] [Accepted: 03/19/2024] [Indexed: 04/24/2024]
Abstract
Recently, there has been a significant advancement in the water quality index (WQI) models utilizing data-driven approaches, especially those integrating machine learning and artificial intelligence (ML/AI) technology. Although, several recent studies have revealed that the data-driven model has produced inconsistent results due to the data outliers, which significantly impact model reliability and accuracy. The present study was carried out to assess the impact of data outliers on a recently developed Irish Water Quality Index (IEWQI) model, which relies on data-driven techniques. To the author's best knowledge, there has been no systematic framework for evaluating the influence of data outliers on such models. For the purposes of assessing the outlier impact of the data outliers on the water quality (WQ) model, this was the first initiative in research to introduce a comprehensive approach that combines machine learning with advanced statistical techniques. The proposed framework was implemented in Cork Harbour, Ireland, to evaluate the IEWQI model's sensitivity to outliers in input indicators to assess the water quality. In order to detect the data outlier, the study utilized two widely used ML techniques, including Isolation Forest (IF) and Kernel Density Estimation (KDE) within the dataset, for predicting WQ with and without these outliers. For validating the ML results, the study used five commonly used statistical measures. The performance metric (R2) indicates that the model performance improved slightly (R2 increased from 0.92 to 0.95) in predicting WQ after removing the data outlier from the input. But the IEWQI scores revealed that there were no statistically significant differences among the actual values, predictions with outliers, and predictions without outliers, with a 95 % confidence interval at p < 0.05. The results of model uncertainty also revealed that the model contributed <1 % uncertainty to the final assessment results for using both datasets (with and without outliers). In addition, all statistical measures indicated that the ML techniques provided reliable results that can be utilized for detecting outliers and their impacts on the IEWQI model. The findings of the research reveal that although the data outliers had no significant impact on the IEWQI model architecture, they had moderate impacts on the rating schemes' of the model. This finding indicated that detecting the data outliers could improve the accuracy of the IEWQI model in rating WQ as well as be helpful in mitigating the model eclipsing problem. In addition, the results of the research provide evidence of how the data outliers influenced the data-driven model in predicting WQ and reliability, particularly since the study confirmed that the IEWQI model's could be effective for accurately rating WQ despite the presence of the data outliers in the input. It could occur due to the spatio-temporal variability inherent in WQ indicators. However, the research assesses the influence of data input outliers on the IEWQI model and underscores important areas for future investigation. These areas include expanding temporal analysis using multi-year data, examining spatial outlier patterns, and evaluating detection methods. Moreover, it is essential to explore the real-world impacts of revised rating categories, involve stakeholders in outlier management, and fine-tune model parameters. Analysing model performance across varying temporal and spatial resolutions and incorporating additional environmental data can significantly enhance the accuracy of WQ assessment. Consequently, this study offers valuable insights to strengthen the IEWQI model's robustness and provides avenues for enhancing its utility in broader WQ assessment applications. Moreover, the study successfully adopted the framework for evaluating how data input outliers affect the data-driven model, such as the IEWQI model. The current study has been carried out in Cork Harbour for only a single year of WQ data. The framework should be tested across various domains for evaluating the response of the IEWQI model's in terms of the spatio-temporal resolution of the domain. Nevertheless, the study recommended that future research should be conducted to adjust or revise the IEWQI model's rating schemes and investigate the practical effects of data outliers on updated rating categories. However, the study provides potential recommendations for enhancing the IEWQI model's adaptability and reveals its effectiveness in expanding its applicability in more general WQ assessment scenarios.
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Affiliation(s)
- Md Galal Uddin
- School of Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, National University of Ireland Galway, Ireland.
| | - Azizur Rahman
- School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga, Australia; The Gulbali Institute of Agriculture, Water and Environment, Charles Sturt University, Wagga, Australia
| | | | - Agnieszka I Olbert
- School of Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, National University of Ireland Galway, Ireland
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14
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Wang Y, Wang J, Gao F, Song J. Unveiling value patterns via deep reinforcement learning in heterogeneous data analytics. PATTERNS (NEW YORK, N.Y.) 2024; 5:100965. [PMID: 38800362 PMCID: PMC11117055 DOI: 10.1016/j.patter.2024.100965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 02/03/2024] [Accepted: 03/06/2024] [Indexed: 05/29/2024]
Abstract
Artificial intelligence has substantially improved the efficiency of data utilization across various sectors. However, the insufficient filtering of low-quality data poses challenges to uncertainty management, threatening system stability. In this study, we introduce a data-valuation approach employing deep reinforcement learning to elucidate the value patterns in data-driven tasks. By strategically optimizing with iterative sampling and feedback, our method is effective in diverse scenarios and consistently outperforms the classic methods in both accuracy and efficiency. In China's wind-power prediction, excluding 25% of the overall dataset deemed low-value led to a 10.5% improvement in accuracy. Utilizing just 42.8% of the dataset, the model discerned 80% of linear patterns, showcasing the data's intrinsic and transferable value. A nationwide analysis identified a data-value-sensitive geographic belt across 10 provinces, leading to robust policy recommendations informed by variances in power outputs and data values, as well as geographic climate factors.
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Affiliation(s)
- Yanzhi Wang
- Department of Industrial Engineering and Management, College of Engineering, Peking University, Beijing 100871, China
| | - Jianxiao Wang
- National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing 100871, China
- PKU-Changsha Institute for Computing and Digital Economy, Changsha 410000, China
| | - Feng Gao
- Department of Industrial Engineering and Management, College of Engineering, Peking University, Beijing 100871, China
| | - Jie Song
- Department of Industrial Engineering and Management, College of Engineering, Peking University, Beijing 100871, China
- National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing 100871, China
- PKU-Changsha Institute for Computing and Digital Economy, Changsha 410000, China
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15
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Salehjahromi M, Karpinets TV, Sujit SJ, Qayati M, Chen P, Aminu M, Saad MB, Bandyopadhyay R, Hong L, Sheshadri A, Lin J, Antonoff MB, Sepesi B, Ostrin EJ, Toumazis I, Huang P, Cheng C, Cascone T, Vokes NI, Behrens C, Siewerdsen JH, Hazle JD, Chang JY, Zhang J, Lu Y, Godoy MCB, Chung C, Jaffray D, Wistuba I, Lee JJ, Vaporciyan AA, Gibbons DL, Gladish G, Heymach JV, Wu CC, Zhang J, Wu J. Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept. Cell Rep Med 2024; 5:101463. [PMID: 38471502 PMCID: PMC10983039 DOI: 10.1016/j.xcrm.2024.101463] [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: 02/01/2023] [Revised: 09/07/2023] [Accepted: 02/15/2024] [Indexed: 03/14/2024]
Abstract
[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.
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Affiliation(s)
| | | | - Sheeba J Sujit
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Mohamed Qayati
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Pingjun Chen
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Muhammad Aminu
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Maliazurina B Saad
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | | | - Lingzhi Hong
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA; Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX USA
| | - Julie Lin
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX USA
| | - Mara B Antonoff
- Department of Thoracic and Cardiovascular Surgery, MD Anderson Cancer Center, Houston, TX, USA
| | - Boris Sepesi
- Department of Thoracic and Cardiovascular Surgery, MD Anderson Cancer Center, Houston, TX, USA
| | - Edwin J Ostrin
- Department of General Internal Medicine, MD Anderson Cancer Center, Houston, TX, USA
| | - Iakovos Toumazis
- Department of Health Services Research, MD Anderson Cancer Center, Houston, TX, USA
| | - Peng Huang
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, USA
| | - Chao Cheng
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Tina Cascone
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Natalie I Vokes
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Carmen Behrens
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Jeffrey H Siewerdsen
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA; Institute for Data Science in Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - John D Hazle
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Joe Y Chang
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Jianhua Zhang
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX, USA
| | - Yang Lu
- Department of Nuclear Medicine, MD Anderson Cancer Center, Houston, TX, USA
| | - Myrna C B Godoy
- Department of Thoracic Imaging, MD Anderson Cancer Center, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA; Institute for Data Science in Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - David Jaffray
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA; Institute for Data Science in Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Ignacio Wistuba
- Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, USA
| | - J Jack Lee
- Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, USA
| | - Ara A Vaporciyan
- Department of Thoracic and Cardiovascular Surgery, MD Anderson Cancer Center, Houston, TX, USA
| | - Don L Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Gregory Gladish
- Department of Thoracic Imaging, MD Anderson Cancer Center, Houston, TX, USA
| | - John V Heymach
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Carol C Wu
- Department of Thoracic Imaging, MD Anderson Cancer Center, Houston, TX, USA
| | - Jianjun Zhang
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX, USA; Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA; Lung Cancer Genomics Program, MD Anderson Cancer Center, Houston, TX, USA; Lung Cancer Interception Program, MD Anderson Cancer Center, Houston, TX, USA
| | - Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA; Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA; Institute for Data Science in Oncology, MD Anderson Cancer Center, Houston, TX, USA.
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16
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Alzubaidi L, Salhi A, A.Fadhel M, Bai J, Hollman F, Italia K, Pareyon R, Albahri AS, Ouyang C, Santamaría J, Cutbush K, Gupta A, Abbosh A, Gu Y. Trustworthy deep learning framework for the detection of abnormalities in X-ray shoulder images. PLoS One 2024; 19:e0299545. [PMID: 38466693 PMCID: PMC10927121 DOI: 10.1371/journal.pone.0299545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 02/12/2024] [Indexed: 03/13/2024] Open
Abstract
Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen's kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results.
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Affiliation(s)
- Laith Alzubaidi
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
- Akunah Medical Technology Pty Ltd Company, Brisbane, QLD, Australia
| | - Asma Salhi
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
- Akunah Medical Technology Pty Ltd Company, Brisbane, QLD, Australia
| | | | - Jinshuai Bai
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
| | - Freek Hollman
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
| | - Kristine Italia
- Akunah Medical Technology Pty Ltd Company, Brisbane, QLD, Australia
| | - Roberto Pareyon
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
| | - A. S. Albahri
- Technical College, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq
| | - Chun Ouyang
- School of Information Systems, Queensland University of Technology, Brisbane, QLD, Australia
| | - Jose Santamaría
- Department of Computer Science, University of Jaén, Jaén, Spain
| | - Kenneth Cutbush
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
- School of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Ashish Gupta
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
- Akunah Medical Technology Pty Ltd Company, Brisbane, QLD, Australia
- Greenslopes Private Hospital, Brisbane, QLD, Australia
| | - Amin Abbosh
- School of Information Technology and Electrical Engineering, Brisbane, QLD, Australia
| | - Yuantong Gu
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
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17
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Ezell JM, Ajayi BP, Parikh T, Miller K, Rains A, Scales D. Drug Use and Artificial Intelligence: Weighing Concerns and Possibilities for Prevention. Am J Prev Med 2024; 66:568-572. [PMID: 38056683 DOI: 10.1016/j.amepre.2023.11.024] [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: 09/29/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 12/08/2023]
Affiliation(s)
- Jerel M Ezell
- Community Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California; Berkeley Center for Cultural Humility, University of California Berkeley, Berkeley, California.
| | - Babatunde Patrick Ajayi
- Community Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California
| | - Tapan Parikh
- Information Science, The College of Arts & Sciences, Cornell University, New York, New York
| | - Kyle Miller
- Department of Medicine, Southern Illinois University, Carbondale, Illinois
| | - Alex Rains
- Pritzer School of Medicine, The University of Chicago, Chicago, Illinois
| | - David Scales
- Division of General Internal Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, New York
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18
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Shen T, Li S, Wang XS, Wang D, Wu S, Xia J, Zhang L. Deep reinforcement learning enables better bias control in benchmark for virtual screening. Comput Biol Med 2024; 171:108165. [PMID: 38402838 DOI: 10.1016/j.compbiomed.2024.108165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 02/07/2024] [Accepted: 02/14/2024] [Indexed: 02/27/2024]
Abstract
Virtual screening (VS) has been incorporated into the paradigm of modern drug discovery. This field is now undergoing a new wave of revolution driven by artificial intelligence and more specifically, machine learning (ML). In terms of those out-of-the-box datasets for model training or benchmarking, their data volume and applicability domain are limited. They are suffering from the biases constantly reported in the ML application. To address these issues, we present a novel benchmark named MUBDsyn. The utilization of synthetic decoys (i.e., presumed inactives) is the main feature of MUBDsyn, where deep reinforcement learning was leveraged for bias control during decoy generation. Then, we carried out extensive validations on this new benchmark. First, we confirmed that MUBDsyn was superior to the classical benchmarks in control of domain bias, artificial enrichment bias and analogue bias. Moreover, we found that the assessment of ML models based on MUBDsyn was less biased as revealed by the analysis of asymmetric validation embedding bias. In addition, MUBDsyn showed better setting of benchmarking challenge for deep learning models compared with NRLiSt-BDB. Overall, we have proven that MUBDsyn is the close-to-ideal benchmark for VS. The computational tool is publicly available for the easy extension of MUBDsyn.
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Affiliation(s)
- Tao Shen
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Shan Li
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| | - Xiang Simon Wang
- Artificial Intelligence and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, USA
| | - Dongmei Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
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19
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Liu T, Li K, Wang Y, Li H, Zhao H. Evaluating the Utilities of Foundation Models in Single-cell Data Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.08.555192. [PMID: 38464157 PMCID: PMC10925156 DOI: 10.1101/2023.09.08.555192] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Foundation Models (FMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of FMs in single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data. By comparing ten different single-cell FMs with task-specific methods, we found that single-cell FMs may not consistently excel in all tasks than task-specific methods. However, the emergent abilities and the successful applications of cross-species/cross-modality transfer learning of FMs are promising. In addition, we present a systematic evaluation of the effects of hyper-parameters, initial settings, and stability for training single-cell FMs based on a proposed scEval framework, and provide guidelines for pre-training and fine-tuning. Our work summarizes the current state of single-cell FMs and points to their constraints and avenues for future development.
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20
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Li G, Li C, Wang C, Wang Z. Suboptimal capability of individual machine learning algorithms in modeling small-scale imbalanced clinical data of local hospital. PLoS One 2024; 19:e0298328. [PMID: 38394317 PMCID: PMC10890755 DOI: 10.1371/journal.pone.0298328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 01/22/2024] [Indexed: 02/25/2024] Open
Abstract
In recent years, artificial intelligence (AI) has shown promising applications in various scientific domains, including biochemical analysis research. However, the effectiveness of AI in modeling small-scale, imbalanced datasets remains an open question in such fields. This study explores the capabilities of eight basic AI algorithms, including ridge regression, logistic regression, random forest regression, and others, in modeling a small, imbalanced clinical dataset (total n = 387, class 0 = 27, class 1 = 360) related to the records of the biochemical blood tests from the patients with multiple wasp stings (MWS). Through rigorous evaluation using k-fold cross-validation and comprehensive scoring, we found that none of the models could effectively model the data. Even after fine-tuning the hyperparameters of the best-performing models, the results remained below acceptable thresholds. The study highlights the challenges of applying AI to small-scale datasets with imbalanced groups in biochemical or clinical research and emphasizes the need for novel algorithms tailored to small-scale data. The findings also call for further exploration into techniques such as transfer learning and data augmentation, and they underline the importance of understanding the minimum dataset scale required for effective AI modeling in biochemical contexts.
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Affiliation(s)
- Gang Li
- Department of ICU, 3201 Hospital, Hanzhong, Shaanxi, China
| | - Chenbi Li
- Department of ICU, 3201 Hospital, Hanzhong, Shaanxi, China
| | - Chengli Wang
- Department of ICU, 3201 Hospital, Hanzhong, Shaanxi, China
| | - Zeheng Wang
- Data61, CSIRO, Clayton, VIC, Australia
- Manufacturing, CSIRO, West Lindfield, NSW, Australia
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21
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Fan W, Yang Y, Qi J, Zhang Q, Liao C, Wen L, Wang S, Wang G, Xia Y, Wu Q, Fan X, Chen X, He M, Xiao J, Yang L, Liu Y, Chen J, Wang B, Zhang L, Yang L, Gan H, Zhang S, Liu G, Ge X, Cai Y, Zhao G, Zhang X, Xie M, Xu H, Zhang Y, Chen J, Li J, Han S, Mu K, Xiao S, Xiong T, Nian Y, Zhang D. A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray. Nat Commun 2024; 15:1347. [PMID: 38355644 PMCID: PMC10867134 DOI: 10.1038/s41467-024-45599-z] [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: 09/19/2022] [Accepted: 01/30/2024] [Indexed: 02/16/2024] Open
Abstract
Accurate identification and localization of multiple abnormalities are crucial steps in the interpretation of chest X-rays (CXRs); however, the lack of a large CXR dataset with bounding boxes severely constrains accurate localization research based on deep learning. We created a large CXR dataset named CXR-AL14, containing 165,988 CXRs and 253,844 bounding boxes. On the basis of this dataset, a deep-learning-based framework was developed to identify and localize 14 common abnormalities and calculate the cardiothoracic ratio (CTR) simultaneously. The mean average precision values obtained by the model for 14 abnormalities reached 0.572-0.631 with an intersection-over-union threshold of 0.5, and the intraclass correlation coefficient of the CTR algorithm exceeded 0.95 on the held-out, multicentre and prospective test datasets. This framework shows an excellent performance, good generalization ability and strong clinical applicability, which is superior to senior radiologists and suitable for routine clinical settings.
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Affiliation(s)
- Weijie Fan
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Yi Yang
- Department of Digital Medicine, School of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, 400038, P. R. China
| | - Jing Qi
- Department of Digital Medicine, School of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, 400038, P. R. China
| | - Qichuan Zhang
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Cuiwei Liao
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Li Wen
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Shuang Wang
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Guangxian Wang
- Department of Radiology, People's Hospital of Banan, Chongqing Medical University, Chongqing, 401320, P. R. China
| | - Yu Xia
- Department of Radiology, Xishui hospital of Traditional Chinese Medicine, Zunyi of Guizhou province, 564600, P. R. China
| | - Qihua Wu
- Department of Radiology, People's Hospital of Nanchuan, Chongqing, 408400, P. R. China
| | - Xiaotao Fan
- Department of Radiology, Fengdu People's Hospital, Chongqing, 408200, P. R. China
| | - Xingcai Chen
- Department of Digital Medicine, School of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, 400038, P. R. China
| | - Mi He
- Department of Digital Medicine, School of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, 400038, P. R. China
| | - JingJing Xiao
- Department of Medical Engineering, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Liu Yang
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Yun Liu
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Jia Chen
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Bing Wang
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Lei Zhang
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Liuqing Yang
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Hui Gan
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Shushu Zhang
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Guofang Liu
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Xiaodong Ge
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Yuanqing Cai
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Gang Zhao
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Xi Zhang
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Mingxun Xie
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Huilin Xu
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Yi Zhang
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Jiao Chen
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Jun Li
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Shuang Han
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Ke Mu
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Shilin Xiao
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Tingwei Xiong
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China
| | - Yongjian Nian
- Department of Digital Medicine, School of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, 400038, P. R. China.
| | - Dong Zhang
- Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China.
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22
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Wu CC, Islam MM, Poly TN, Weng YC. Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research. Diagnostics (Basel) 2024; 14:397. [PMID: 38396436 PMCID: PMC10887584 DOI: 10.3390/diagnostics14040397] [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: 12/04/2023] [Revised: 02/03/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a promising tool in the field of healthcare, with an increasing number of research articles evaluating its applications in the domain of kidney disease. To comprehend the evolving landscape of AI research in kidney disease, a bibliometric analysis is essential. The purposes of this study are to systematically analyze and quantify the scientific output, research trends, and collaborative networks in the application of AI to kidney disease. This study collected AI-related articles published between 2012 and 20 November 2023 from the Web of Science. Descriptive analyses of research trends in the application of AI in kidney disease were used to determine the growth rate of publications by authors, journals, institutions, and countries. Visualization network maps of country collaborations and author-provided keyword co-occurrences were generated to show the hotspots and research trends in AI research on kidney disease. The initial search yielded 673 articles, of which 631 were included in the analyses. Our findings reveal a noteworthy exponential growth trend in the annual publications of AI applications in kidney disease. Nephrology Dialysis Transplantation emerged as the leading publisher, accounting for 4.12% (26 out of 631 papers), followed by the American Journal of Transplantation at 3.01% (19/631) and Scientific Reports at 2.69% (17/631). The primary contributors were predominantly from the United States (n = 164, 25.99%), followed by China (n = 156, 24.72%) and India (n = 62, 9.83%). In terms of institutions, Mayo Clinic led with 27 contributions (4.27%), while Harvard University (n = 19, 3.01%) and Sun Yat-Sen University (n = 16, 2.53%) secured the second and third positions, respectively. This study summarized AI research trends in the field of kidney disease through statistical analysis and network visualization. The findings show that the field of AI in kidney disease is dynamic and rapidly progressing and provides valuable information for recognizing emerging patterns, technological shifts, and interdisciplinary collaborations that contribute to the advancement of knowledge in this critical domain.
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Affiliation(s)
- Chieh-Chen Wu
- Department of Healthcare Information and Management, School of Health and Medical Engineering, Ming Chuan University, Taipei 111, Taiwan;
| | - Md. Mohaimenul Islam
- Outcomes and Translational Sciences, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA;
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan;
| | - Yung-Ching Weng
- Department of Healthcare Information and Management, School of Health and Medical Engineering, Ming Chuan University, Taipei 111, Taiwan;
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23
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Feng X, Shu W, Li M, Li J, Xu J, He M. Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview. J Transl Med 2024; 22:131. [PMID: 38310237 PMCID: PMC10837897 DOI: 10.1186/s12967-024-04915-3] [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: 10/31/2023] [Accepted: 01/20/2024] [Indexed: 02/05/2024] Open
Abstract
The capability to gather heterogeneous data, alongside the increasing power of artificial intelligence to examine it, leading a revolution in harnessing multimodal data in the life sciences. However, most approaches are limited to unimodal data, leaving integrated approaches across modalities relatively underdeveloped in computational pathology. Pathogenomics, as an invasive method to integrate advanced molecular diagnostics from genomic data, morphological information from histopathological imaging, and codified clinical data enable the discovery of new multimodal cancer biomarkers to propel the field of precision oncology in the coming decade. In this perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods in pathogenomics. It includes correlation between the pathological and genomic profile of cancer, fusion of histology, and genomics profile of cancer. We also present challenges, opportunities, and avenues for future work.
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Affiliation(s)
- Xiaobing Feng
- College of Electrical and Information Engineering, Hunan University, Changsha, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Wen Shu
- College of Electrical and Information Engineering, Hunan University, Changsha, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Mingya Li
- College of Electrical and Information Engineering, Hunan University, Changsha, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Junyu Li
- College of Electrical and Information Engineering, Hunan University, Changsha, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Junyao Xu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Min He
- College of Electrical and Information Engineering, Hunan University, Changsha, China.
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
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24
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Tian W, Zhang Z, Bouffard D, Wu H, Xin K, Gu X, Liao Z. Enhancing interpretability and generalizability of deep learning-based emulator in three-dimensional lake hydrodynamics using Koopman operator and transfer learning: Demonstrated on the example of lake Zurich. WATER RESEARCH 2024; 249:120996. [PMID: 38103441 DOI: 10.1016/j.watres.2023.120996] [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: 06/02/2023] [Revised: 11/02/2023] [Accepted: 12/07/2023] [Indexed: 12/19/2023]
Abstract
Three-dimensional lake hydrodynamic model is a powerful tool widely used to assess hydrological condition changes of lake. However, its computational cost becomes problematic when forecasting the state of large lakes or using high-resolution simulation in small-to-medium size lakes. One possible solution is to employ a data-driven emulator, such as a deep learning (DL) based emulator, to replace the original model for fast computing. However, existing DL-based emulators are often black-box and data-dependent models, causing poor interpretability and generalizability in practical applications. In this study, a data-driven emulator is established using deep neural network (DNN) to replace the original model for fast computing of three-dimensional lake hydrodynamics. Then, the Koopman operator and transfer learning (TL) are employed to enhance the interpretability and generalizability of the emulator. Finally, the generalizability of DL-based emulators is comprehensively analyzed through linear regression and correlation analysis. These methods are tested against an existing hydrodynamic model of Lake Zurich (Switzerland) whose data was provided by an open-source web-based platform called Meteolakes/Alplakes. According to the results, (1) The DLEDMD offers better interpretability than DNN because its Koopman operator reveals the linear structure behind the hydrodynamics; (2) The generalization of the DL-based emulators in three-dimensional lake hydrodynamics are influenced by the similarity between the training and testing data; (3) TL effectively improves the generalizability of the DL-based emulators.
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Affiliation(s)
- Wenchong Tian
- College of Environmental Science and Engineering, Tongji University, 200092 Shanghai, China; Key Laboratory of Yangtze River Water Environment, Ministry of Education, Tongji University, 200092 Shanghai, China; Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, Shanghai 200092, P.R. China
| | - Zhiyu Zhang
- College of Environmental Science and Engineering, Tongji University, 200092 Shanghai, China; Key Laboratory of Yangtze River Water Environment, Ministry of Education, Tongji University, 200092 Shanghai, China
| | - Damien Bouffard
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Surface Waters Research and Management, 6047 Kastanienbaum, Switzerland; Faculty of Geosciences and Environment, Institute of Earth Surface Dynamics, University of Lausanne, Geopolis, Mouline, CH-1015 Lausanne, Switzerland
| | - Hao Wu
- School of Mathematical Sciences, Institute of Natural Sciences, and MOE-LSC, Shanghai Jiao Tong University, Shanghai, China; School of Mathematical Sciences, Tongji University, Shanghai, China
| | - Kunlun Xin
- College of Environmental Science and Engineering, Tongji University, 200092 Shanghai, China
| | - Xianyong Gu
- National Engineering Research Center of Dredging Technology and Equipment Co., Ltd., 201208 Shanghai, China
| | - Zhenliang Liao
- College of Environmental Science and Engineering, Tongji University, 200092 Shanghai, China; Key Laboratory of Yangtze River Water Environment, Ministry of Education, Tongji University, 200092 Shanghai, China.
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25
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Kobayashi K, Gu L, Hataya R, Mizuno T, Miyake M, Watanabe H, Takahashi M, Takamizawa Y, Yoshida Y, Nakamura S, Kouno N, Bolatkan A, Kurose Y, Harada T, Hamamoto R. Sketch-based semantic retrieval of medical images. Med Image Anal 2024; 92:103060. [PMID: 38104401 DOI: 10.1016/j.media.2023.103060] [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: 12/16/2022] [Revised: 08/31/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023]
Abstract
The volume of medical images stored in hospitals is rapidly increasing; however, the utilization of these accumulated medical images remains limited. Existing content-based medical image retrieval (CBMIR) systems typically require example images, leading to practical limitations, such as the lack of customizable, fine-grained image retrieval, the inability to search without example images, and difficulty in retrieving rare cases. In this paper, we introduce a sketch-based medical image retrieval (SBMIR) system that enables users to find images of interest without the need for example images. The key concept is feature decomposition of medical images, which allows the entire feature of a medical image to be decomposed into and reconstructed from normal and abnormal features. Building on this concept, our SBMIR system provides an easy-to-use two-step graphical user interface: users first select a template image to specify a normal feature and then draw a semantic sketch of the disease on the template image to represent an abnormal feature. The system integrates both types of input to construct a query vector and retrieves reference images. For evaluation, ten healthcare professionals participated in a user test using two datasets. Consequently, our SBMIR system enabled users to overcome previous challenges, including image retrieval based on fine-grained image characteristics, image retrieval without example images, and image retrieval for rare cases. Our SBMIR system provides on-demand, customizable medical image retrieval, thereby expanding the utility of medical image databases.
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Affiliation(s)
- Kazuma Kobayashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan.
| | - Lin Gu
- Machine Intelligence for Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan.
| | - Ryuichiro Hataya
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan.
| | - Takaaki Mizuno
- Department of Experimental Therapeutics, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan.
| | - Mototaka Miyake
- Department of Diagnostic Radiology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan.
| | - Hirokazu Watanabe
- Department of Diagnostic Radiology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan.
| | - Masamichi Takahashi
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan.
| | - Yasuyuki Takamizawa
- Department of Colorectal Surgery, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan.
| | - Yukihiro Yoshida
- Department of Thoracic Surgery, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan.
| | - Satoshi Nakamura
- Radiation Safety and Quality Assurance Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; Division of Research and Development for Boron Neutron Capture Therapy, National Cancer Center, Exploratory Oncology Research & Clinical Trial Center, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; Medical Physics Laboratory, Division of Health Science, Graduate School of Medicine, Osaka University, Yamadaoka 1-7, Suita-shi, Osaka 565-0871, Japan.
| | - Nobuji Kouno
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; Department of Surgery, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan.
| | - Amina Bolatkan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan.
| | - Yusuke Kurose
- Machine Intelligence for Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan.
| | - Tatsuya Harada
- Machine Intelligence for Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan.
| | - Ryuji Hamamoto
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan.
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26
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Hartley J, Sanchez PP, Haider F, Tsaftaris SA. Neural networks memorise personal information from one sample. Sci Rep 2023; 13:21366. [PMID: 38049432 PMCID: PMC10695936 DOI: 10.1038/s41598-023-48034-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 11/21/2023] [Indexed: 12/06/2023] Open
Abstract
Deep neural networks (DNNs) have achieved high accuracy in diagnosing multiple diseases/conditions at a large scale. However, a number of concerns have been raised about safeguarding data privacy and algorithmic bias of the neural network models. We demonstrate that unique features (UFs), such as names, IDs, or other patient information can be memorised (and eventually leaked) by neural networks even when it occurs on a single training data sample within the dataset. We explain this memorisation phenomenon by showing that it is more likely to occur when UFs are an instance of a rare concept. We propose methods to identify whether a given model does or does not memorise a given (known) feature. Importantly, our method does not require access to the training data and therefore can be deployed by an external entity. We conclude that memorisation does have implications on model robustness, but it can also pose a risk to the privacy of patients who consent to the use of their data for training models.
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27
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Chen E, Prakash S, Janapa Reddi V, Kim D, Rajpurkar P. A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring. Nat Biomed Eng 2023:10.1038/s41551-023-01115-0. [PMID: 37932379 DOI: 10.1038/s41551-023-01115-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 09/26/2023] [Indexed: 11/08/2023]
Abstract
The complex relationships between continuously monitored health signals and therapeutic regimens can be modelled via machine learning. However, the clinical implementation of the models will require changes to clinical workflows. Here we outline ClinAIOps ('clinical artificial-intelligence operations'), a framework that integrates continuous therapeutic monitoring and the development of artificial intelligence (AI) for clinical care. ClinAIOps leverages three feedback loops to enable the patient to make treatment adjustments using AI outputs, the clinician to oversee patient progress with AI assistance, and the AI developer to receive continuous feedback from both the patient and the clinician. We lay out the central challenges and opportunities in the deployment of ClinAIOps by means of examples of its application in the management of blood pressure, diabetes and Parkinson's disease. By enabling more frequent and accurate measurements of a patient's health and more timely adjustments to their treatment, ClinAIOps may substantially improve patient outcomes.
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Affiliation(s)
- Emma Chen
- Harvard John A. Paulson School of Engineering and Applied Sciences, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Shvetank Prakash
- Harvard John A. Paulson School of Engineering and Applied Sciences, Boston, MA, USA
| | - Vijay Janapa Reddi
- Harvard John A. Paulson School of Engineering and Applied Sciences, Boston, MA, USA
| | - David Kim
- Department of Emergency Medicine, Stanford School of Medicine, Stanford, CA, USA
| | - Pranav Rajpurkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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28
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Gallagher MT, Krasauskaite I, Kirkman-Brown JC. Only the Best of the Bunch-Sperm Preparation Is Not Just about Numbers. Semin Reprod Med 2023; 41:273-278. [PMID: 38113923 DOI: 10.1055/s-0043-1777756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
In this Seminar, we present an overview of the current and emerging methods and technologies for optimizing the man and the sperm sample for fertility treatment. We argue that sperms are the secret to success, and that there are many avenues for improving both treatment and basic understanding of their role in outcomes. These outcomes encompass not just whether treatment is successful or not, but the wider intergenerational health of the offspring. We discuss outstanding challenges and opportunities of new technologies such as microfluidics and artificial intelligence, including potential pitfalls and advantages. This article aims to provide a comprehensive overview of the importance of sperm in fertility treatment and suggests future directions for research and innovation.
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Affiliation(s)
- Meurig T Gallagher
- Centre for Human Reproductive Science, Institute of Metabolism and Systems Research, University of Birmingham and Birmingham Women's Fertility Centre, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, B15 2TT, United Kingdom
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Ingrida Krasauskaite
- Centre for Human Reproductive Science, Institute of Metabolism and Systems Research, University of Birmingham and Birmingham Women's Fertility Centre, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, B15 2TT, United Kingdom
| | - Jackson C Kirkman-Brown
- Centre for Human Reproductive Science, Institute of Metabolism and Systems Research, University of Birmingham and Birmingham Women's Fertility Centre, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, B15 2TT, United Kingdom
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29
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Peng T, Dong Y, Di G, Zhao J, Li T, Ren G, Zhang L, Cai J. Boundary delineation in transrectal ultrasound images for region of interest of prostate. Phys Med Biol 2023; 68:195008. [PMID: 37652058 DOI: 10.1088/1361-6560/acf5c5] [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: 06/03/2023] [Accepted: 08/31/2023] [Indexed: 09/02/2023]
Abstract
Accurate and robust prostate segmentation in transrectal ultrasound (TRUS) images is of great interest for ultrasound-guided brachytherapy for prostate cancer. However, the current practice of manual segmentation is difficult, time-consuming, and prone to errors. To overcome these challenges, we developed an accurate prostate segmentation framework (A-ProSeg) for TRUS images. The proposed segmentation method includes three innovation steps: (1) acquiring the sequence of vertices by using an improved polygonal segment-based method with a small number of radiologist-defined seed points as prior points; (2) establishing an optimal machine learning-based method by using the improved evolutionary neural network; and (3) obtaining smooth contours of the prostate region of interest using the optimized machine learning-based method. The proposed method was evaluated on 266 patients who underwent prostate cancer brachytherapy. The proposed method achieved a high performance against the ground truth with a Dice similarity coefficient of 96.2% ± 2.4%, a Jaccard similarity coefficient of 94.4% ± 3.3%, and an accuracy of 95.7% ± 2.7%; these values are all higher than those obtained using state-of-the-art methods. A sensitivity evaluation on different noise levels demonstrated that our method achieved high robustness against changes in image quality. Meanwhile, an ablation study was performed, and the significance of all the key components of the proposed method was demonstrated.
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Affiliation(s)
- Tao Peng
- School of Future Science and Engineering, Soochow University, Suzhou, People's Republic of China
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, People's Republic of China
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Yan Dong
- Department of Ultrasonography, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Gongye Di
- Department of Ultrasonic, Taizhou People's Hospital Affiliated to Nanjing Medical University, Taizhou, People's Republic of China
| | - Jing Zhao
- Department of Ultrasound, Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital, Beijing, People's Republic of China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, People's Republic of China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, People's Republic of China
| | - Lei Zhang
- Medical Physics Graduate Program and Data Science Research Center, Duke Kunshan University, Kunshan, Jiangsu, People's Republic of China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, People's Republic of China
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30
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Debnath R, Creutzig F, Sovacool BK, Shuckburgh E. Harnessing human and machine intelligence for planetary-level climate action. NPJ CLIMATE ACTION 2023; 2:20. [PMID: 38694954 PMCID: PMC11062317 DOI: 10.1038/s44168-023-00056-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 07/24/2023] [Indexed: 05/04/2024]
Abstract
The ongoing global race for bigger and better artificial intelligence (AI) systems is expected to have a profound societal and environmental impact by altering job markets, disrupting business models, and enabling new governance and societal welfare structures that can affect global consensus for climate action pathways. However, the current AI systems are trained on biased datasets that could destabilize political agencies impacting climate change mitigation and adaptation decisions and compromise social stability, potentially leading to societal tipping events. Thus, the appropriate design of a less biased AI system that reflects both direct and indirect effects on societies and planetary challenges is a question of paramount importance. In this paper, we tackle the question of data-centric knowledge generation for climate action in ways that minimize biased AI. We argue for the need to co-align a less biased AI with an epistemic web on planetary health challenges for more trustworthy decision-making. A human-in-the-loop AI can be designed to align with three goals. First, it can contribute to a planetary epistemic web that supports climate action. Second, it can directly enable mitigation and adaptation interventions through knowledge of social tipping elements. Finally, it can reduce the data injustices associated with AI pretraining datasets.
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Affiliation(s)
- Ramit Debnath
- Cambridge Zero and Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD United Kingdom
- Division of Humanities and Social Science, California Institute of Technology, Pasadena, CA, 91125 USA
| | - Felix Creutzig
- Mercator Research Institute on Global Commons and Climate Change, Berlin, 10829 Germany
- Technical University Berlin, Berlin, 10827 Germany
| | - Benjamin K. Sovacool
- Center for Energy Technologies, Department of Business Development and Technology, Aarhus University, Aarhus, Denmark
- Science Policy Research Unit, University of Sussex Business School, Brighton, United Kingdom
- Institute for Global Sustainability, Department of Earth and Environment, Boston University, Boston, MA, USA
| | - Emily Shuckburgh
- Cambridge Zero and Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD United Kingdom
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31
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Alammar Z, Alzubaidi L, Zhang J, Li Y, Lafta W, Gu Y. Deep Transfer Learning with Enhanced Feature Fusion for Detection of Abnormalities in X-ray Images. Cancers (Basel) 2023; 15:4007. [PMID: 37568821 PMCID: PMC10417687 DOI: 10.3390/cancers15154007] [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: 06/16/2023] [Revised: 07/29/2023] [Accepted: 08/05/2023] [Indexed: 08/13/2023] Open
Abstract
Medical image classification poses significant challenges in real-world scenarios. One major obstacle is the scarcity of labelled training data, which hampers the performance of image-classification algorithms and generalisation. Gathering sufficient labelled data is often difficult and time-consuming in the medical domain, but deep learning (DL) has shown remarkable performance, although it typically requires a large amount of labelled data to achieve optimal results. Transfer learning (TL) has played a pivotal role in reducing the time, cost, and need for a large number of labelled images. This paper presents a novel TL approach that aims to overcome the limitations and disadvantages of TL that are characteristic of an ImageNet dataset, which belongs to a different domain. Our proposed TL approach involves training DL models on numerous medical images that are similar to the target dataset. These models were then fine-tuned using a small set of annotated medical images to leverage the knowledge gained from the pre-training phase. We specifically focused on medical X-ray imaging scenarios that involve the humerus and wrist from the musculoskeletal radiographs (MURA) dataset. Both of these tasks face significant challenges regarding accurate classification. The models trained with the proposed TL were used to extract features and were subsequently fused to train several machine learning (ML) classifiers. We combined these diverse features to represent various relevant characteristics in a comprehensive way. Through extensive evaluation, our proposed TL and feature-fusion approach using ML classifiers achieved remarkable results. For the classification of the humerus, we achieved an accuracy of 87.85%, an F1-score of 87.63%, and a Cohen's Kappa coefficient of 75.69%. For wrist classification, our approach achieved an accuracy of 85.58%, an F1-score of 82.70%, and a Cohen's Kappa coefficient of 70.46%. The results demonstrated that the models trained using our proposed TL approach outperformed those trained with ImageNet TL. We employed visualisation techniques to further validate these findings, including a gradient-based class activation heat map (Grad-CAM) and locally interpretable model-independent explanations (LIME). These visualisation tools provided additional evidence to support the superior accuracy of models trained with our proposed TL approach compared to those trained with ImageNet TL. Furthermore, our proposed TL approach exhibited greater robustness in various experiments compared to ImageNet TL. Importantly, the proposed TL approach and the feature-fusion technique are not limited to specific tasks. They can be applied to various medical image applications, thus extending their utility and potential impact. To demonstrate the concept of reusability, a computed tomography (CT) case was adopted. The results obtained from the proposed method showed improvements.
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Affiliation(s)
- Zaenab Alammar
- School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia; (J.Z.); (Y.L.)
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Laith Alzubaidi
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia;
- ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Jinglan Zhang
- School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia; (J.Z.); (Y.L.)
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Yuefeng Li
- School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia; (J.Z.); (Y.L.)
| | | | - Yuantong Gu
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia;
- ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
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32
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Peng T, Gu Y, Zhang J, Dong Y, DI G, Wang W, Zhao J, Cai J. A Robust and Explainable Structure-Based Algorithm for Detecting the Organ Boundary From Ultrasound Multi-Datasets. J Digit Imaging 2023; 36:1515-1532. [PMID: 37231289 PMCID: PMC10406792 DOI: 10.1007/s10278-023-00839-4] [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: 02/22/2023] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 05/27/2023] Open
Abstract
Detecting the organ boundary in an ultrasound image is challenging because of the poor contrast of ultrasound images and the existence of imaging artifacts. In this study, we developed a coarse-to-refinement architecture for multi-organ ultrasound segmentation. First, we integrated the principal curve-based projection stage into an improved neutrosophic mean shift-based algorithm to acquire the data sequence, for which we utilized a limited amount of prior seed point information as the approximate initialization. Second, a distribution-based evolution technique was designed to aid in the identification of a suitable learning network. Then, utilizing the data sequence as the input of the learning network, we achieved the optimal learning network after learning network training. Finally, a scaled exponential linear unit-based interpretable mathematical model of the organ boundary was expressed via the parameters of a fraction-based learning network. The experimental outcomes indicated that our algorithm 1) achieved more satisfactory segmentation outcomes than state-of-the-art algorithms, with a Dice score coefficient value of 96.68 ± 2.2%, a Jaccard index value of 95.65 ± 2.16%, and an accuracy of 96.54 ± 1.82% and 2) discovered missing or blurry areas.
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Affiliation(s)
- Tao Peng
- School of Future Science and Engineering, Soochow University, Suzhou, China
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, China
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX USA
| | - Yidong Gu
- School of Future Science and Engineering, Soochow University, Suzhou, China
- Department of Medical Ultrasound, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, Jiangsu China
| | - Ji Zhang
- Department of Radiology, The Affiliated Taizhou People’s Hospital of Nanjing Medical University, Taizhou, Jiangsu Province, China
| | - Yan Dong
- Department of Ultrasonography, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Gongye DI
- Department of Ultrasonic, The Affiliated Taizhou People’s Hospital of Nanjing Medical University, Taizhou, Jiangsu Province, China
| | - Wenjie Wang
- Department of Radio-Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, Jiangsu China
| | - Jing Zhao
- Department of Ultrasound, Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Jing Cai
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, China
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Zou J, Gichoya JW, Ho DE, Obermeyer Z. Implications of predicting race variables from medical images. Science 2023; 381:149-150. [PMID: 37440627 DOI: 10.1126/science.adh4260] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/15/2023]
Abstract
AI-predicted race variables pose risks and opportunities for studying health disparities.
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Affiliation(s)
- James Zou
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Judy Wawira Gichoya
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Daniel E Ho
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Ziad Obermeyer
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
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Yan X, Yue T, Winkler DA, Yin Y, Zhu H, Jiang G, Yan B. Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation. Chem Rev 2023. [PMID: 37262026 DOI: 10.1021/acs.chemrev.3c00070] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.
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Affiliation(s)
- Xiliang Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Tongtao Yue
- Key Laboratory of Marine Environment and Ecology, Ministry of Education, Institute of Coastal Environmental Pollution Control, Ocean University of China, Qingdao 266100, China
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia
- School of Pharmacy, University of Nottingham, Nottingham NG7 2QL, U.K
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, Australia
| | - Yongguang Yin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Bing Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
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Steyaert S, Pizurica M, Nagaraj D, Khandelwal P, Hernandez-Boussard T, Gentles AJ, Gevaert O. Multimodal data fusion for cancer biomarker discovery with deep learning. NAT MACH INTELL 2023; 5:351-362. [PMID: 37693852 PMCID: PMC10484010 DOI: 10.1038/s42256-023-00633-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 02/17/2023] [Indexed: 09/12/2023]
Abstract
Technological advances now make it possible to study a patient from multiple angles with high-dimensional, high-throughput multi-scale biomedical data. In oncology, massive amounts of data are being generated ranging from molecular, histopathology, radiology to clinical records. The introduction of deep learning has significantly advanced the analysis of biomedical data. However, most approaches focus on single data modalities leading to slow progress in methods to integrate complementary data types. Development of effective multimodal fusion approaches is becoming increasingly important as a single modality might not be consistent and sufficient to capture the heterogeneity of complex diseases to tailor medical care and improve personalised medicine. Many initiatives now focus on integrating these disparate modalities to unravel the biological processes involved in multifactorial diseases such as cancer. However, many obstacles remain, including lack of usable data as well as methods for clinical validation and interpretation. Here, we cover these current challenges and reflect on opportunities through deep learning to tackle data sparsity and scarcity, multimodal interpretability, and standardisation of datasets.
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Affiliation(s)
- Sandra Steyaert
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University
| | - Marija Pizurica
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University
| | | | | | - Tina Hernandez-Boussard
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University
- Department of Biomedical Data Science, Stanford University
| | - Andrew J Gentles
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University
- Department of Biomedical Data Science, Stanford University
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University
- Department of Biomedical Data Science, Stanford University
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36
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Kiyasseh D, Laca J, Haque TF, Miles BJ, Wagner C, Donoho DA, Anandkumar A, Hung AJ. A multi-institutional study using artificial intelligence to provide reliable and fair feedback to surgeons. COMMUNICATIONS MEDICINE 2023; 3:42. [PMID: 36997578 PMCID: PMC10063640 DOI: 10.1038/s43856-023-00263-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 02/17/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND Surgeons who receive reliable feedback on their performance quickly master the skills necessary for surgery. Such performance-based feedback can be provided by a recently-developed artificial intelligence (AI) system that assesses a surgeon's skills based on a surgical video while simultaneously highlighting aspects of the video most pertinent to the assessment. However, it remains an open question whether these highlights, or explanations, are equally reliable for all surgeons. METHODS Here, we systematically quantify the reliability of AI-based explanations on surgical videos from three hospitals across two continents by comparing them to explanations generated by humans experts. To improve the reliability of AI-based explanations, we propose the strategy of training with explanations -TWIX -which uses human explanations as supervision to explicitly teach an AI system to highlight important video frames. RESULTS We show that while AI-based explanations often align with human explanations, they are not equally reliable for different sub-cohorts of surgeons (e.g., novices vs. experts), a phenomenon we refer to as an explanation bias. We also show that TWIX enhances the reliability of AI-based explanations, mitigates the explanation bias, and improves the performance of AI systems across hospitals. These findings extend to a training environment where medical students can be provided with feedback today. CONCLUSIONS Our study informs the impending implementation of AI-augmented surgical training and surgeon credentialing programs, and contributes to the safe and fair democratization of surgery.
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Affiliation(s)
- Dani Kiyasseh
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.
| | - Jasper Laca
- Center for Robotic Simulation and Education, Catherine & Joseph Aresty Department of Urology, University of Southern California, Los Angeles, CA, USA
| | - Taseen F Haque
- Center for Robotic Simulation and Education, Catherine & Joseph Aresty Department of Urology, University of Southern California, Los Angeles, CA, USA
| | - Brian J Miles
- Department of Urology, Houston Methodist Hospital, Houston, TX, USA
| | - Christian Wagner
- Department of Urology, Pediatric Urology and Uro-Oncology, Prostate Center Northwest, St. Antonius-Hospital, Gronau, Germany
| | - Daniel A Donoho
- Division of Neurosurgery, Center for Neuroscience, Children's National Hospital, Washington DC, USA
| | - Animashree Anandkumar
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Andrew J Hung
- Center for Robotic Simulation and Education, Catherine & Joseph Aresty Department of Urology, University of Southern California, Los Angeles, CA, USA.
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37
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Vega C, Schneider R, Satagopam V. Analysis: Flawed Datasets of Monkeypox Skin Images. J Med Syst 2023; 47:37. [PMID: 36933065 PMCID: PMC10024024 DOI: 10.1007/s10916-023-01928-1] [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: 12/23/2022] [Accepted: 02/26/2023] [Indexed: 03/19/2023]
Abstract
The self-proclaimed first publicly available dataset of Monkeypox skin images consists of medically irrelevant images extracted from Google and photography repositories through a process denominated web-scrapping. Yet, this did not stop other researchers from employing it to build Machine Learning (ML) solutions aimed at computer-aided diagnosis of Monkeypox and other viral infections presenting skin lesions. Neither did it stop the reviewers or editors from publishing these subsequent works in peer-reviewed journals. Several of these works claimed extraordinary performance in the classification of Monkeypox, Chickenpox and Measles, employing ML and the aforementioned dataset. In this work, we analyse the initiator work that has catalysed the development of several ML solutions, and whose popularity is continuing to grow. Further, we provide a rebuttal experiment that showcases the risks of such methodologies, proving that the ML solutions do not necessarily obtain their performance from the features relevant to the diseases at issue.
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Affiliation(s)
- Carlos Vega
- Bioinformatics Core, University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Av. du Swing 6, Belvaux, 4367, Luxembourg.
| | - Reinhard Schneider
- Bioinformatics Core, University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Av. du Swing 6, Belvaux, 4367, Luxembourg
| | - Venkata Satagopam
- Bioinformatics Core, University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Av. du Swing 6, Belvaux, 4367, Luxembourg
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38
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Mitra R, McGough SF, Chakraborti T, Holmes C, Copping R, Hagenbuch N, Biedermann S, Noonan J, Lehmann B, Shenvi A, Doan XV, Leslie D, Bianconi G, Sanchez-Garcia R, Davies A, Mackintosh M, Andrinopoulou ER, Basiri A, Harbron C, MacArthur BD. Learning from data with structured missingness. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-022-00596-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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39
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Choudhary M, Sentil S, Jones JB, Paret ML. Non-coding deep learning models for tomato biotic and abiotic stress classification using microscopic images. FRONTIERS IN PLANT SCIENCE 2023; 14:1292643. [PMID: 38259932 PMCID: PMC10800394 DOI: 10.3389/fpls.2023.1292643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 11/24/2023] [Indexed: 01/24/2024]
Abstract
Plant disease classification is quite complex and, in most cases, requires trained plant pathologists and sophisticated labs to accurately determine the cause. Our group for the first time used microscopic images (×30) of tomato plant diseases, for which representative plant samples were diagnostically validated to classify disease symptoms using non-coding deep learning platforms (NCDL). The mean F1 scores (SD) of the NCDL platforms were 98.5 (1.6) for Amazon Rekognition Custom Label, 93.9 (2.5) for Clarifai, 91.6 (3.9) for Teachable Machine, 95.0 (1.9) for Google AutoML Vision, and 97.5 (2.7) for Microsoft Azure Custom Vision. The accuracy of the NCDL platform for Amazon Rekognition Custom Label was 99.8% (0.2), for Clarifai 98.7% (0.5), for Teachable Machine 98.3% (0.4), for Google AutoML Vision 98.9% (0.6), and for Apple CreateML 87.3 (4.3). Upon external validation, the model's accuracy of the tested NCDL platforms dropped no more than 7%. The potential future use for these models includes the development of mobile- and web-based applications for the classification of plant diseases and integration with a disease management advisory system. The NCDL models also have the potential to improve the early triage of symptomatic plant samples into classes that may save time in diagnostic lab sample processing.
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Affiliation(s)
- Manoj Choudhary
- North Florida Research and Education Center, University of Florida, Quincy, FL, United States
- Plant Pathology Department, University of Florida, Gainesville, FL, United States
- Indian Council of Agricultural Research (ICAR) - National Centre for Integrated Pest Management, New Delhi, India
| | - Sruthi Sentil
- North Florida Research and Education Center, University of Florida, Quincy, FL, United States
| | - Jeffrey B. Jones
- North Florida Research and Education Center, University of Florida, Quincy, FL, United States
| | - Mathews L. Paret
- North Florida Research and Education Center, University of Florida, Quincy, FL, United States
- Plant Pathology Department, University of Florida, Gainesville, FL, United States
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40
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Rodgers CM, Ellingson SR, Chatterjee P. Open Data and transparency in artificial intelligence and machine learning: A new era of research. F1000Res 2023; 12:387. [PMID: 37065505 PMCID: PMC10098385 DOI: 10.12688/f1000research.133019.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/04/2023] [Indexed: 04/18/2023] Open
Abstract
Artificial Intelligence (AI) and machine learning are the current forefront of computer science and technology. AI and related sub-disciplines, including machine learning, are essential technologies which have enabled the widespread use of smart technology, such as smart phones, smart home appliances and even electric toothbrushes. It is AI that allows the devices used day-to-day across people's personal lives, working lives and in industry to better anticipate and respond to our needs. However, the use of AI technology comes with a range of ethical questions - including issues around privacy, security, reliability, copyright/plagiarism and whether AI is capable of independent, conscious thought. We have seen several issues related to racial and sexual bias in AI in the recent times, putting the reliability of AI in question. Many of these issues have been brought to the forefront of cultural awareness in late 2022, early 2023, with the rise of AI art programs (and the copyright issues arising from the deep-learning methods employed to train this AI), and the popularity of ChatGPT alongside its ability to be used to mimic human output, particularly in regard to academic work. In critical areas like healthcare, the errors of AI can be fatal. With the incorporation of AI in almost every sector of our everyday life, we need to keep asking ourselves- can we trust AI, and how much? This Editorial outlines the importance of openness and transparency in the development and applications of AI to allow all users to fully understand both the benefits and risks of this ubiquitous technology, and outlines how the Artificial Intelligence and Machine Learning Gateway on F1000Research meets these needs.
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Affiliation(s)
| | | | - Parag Chatterjee
- Universidad Tecnologica Nacional Facultad Regional Buenos Aires, Buenos Aires, Autonomous City of Buenos Aires, Argentina
- Universidad de la Republica Uruguay, Montevideo, Montevideo Department, Uruguay
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41
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Qin K, Huang W, Zhang T, Tang S. Machine learning and deep learning for blood pressure prediction: a methodological review from multiple perspectives. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10353-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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42
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Simulation of a machine learning enabled learning health system for risk prediction using synthetic patient data. Sci Rep 2022; 12:17917. [PMID: 36289292 PMCID: PMC9606301 DOI: 10.1038/s41598-022-23011-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 10/21/2022] [Indexed: 01/20/2023] Open
Abstract
When enabled by machine learning (ML), Learning Health Systems (LHS) hold promise for improving the effectiveness of healthcare delivery to patients. One major barrier to LHS research and development is the lack of access to EHR patient data. To overcome this challenge, this study demonstrated the feasibility of developing a simulated ML-enabled LHS using synthetic patient data. The ML-enabled LHS was initialized using a dataset of 30,000 synthetic Synthea patients and a risk prediction XGBoost base model for lung cancer. 4 additional datasets of 30,000 patients were generated and added to the previous updated dataset sequentially to simulate addition of new patients, resulting in datasets of 60,000, 90,000, 120,000 and 150,000 patients. New XGBoost models were built in each instance, and performance improved with data size increase, attaining 0.936 recall and 0.962 AUC (area under curve) in the 150,000 patients dataset. The effectiveness of the new ML-enabled LHS process was verified by implementing XGBoost models for stroke risk prediction on the same Synthea patient populations. By making the ML code and synthetic patient data publicly available for testing and training, this first synthetic LHS process paves the way for more researchers to start developing LHS with real patient data.
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Dang F, Wang Q, Yan X, Zhang Y, Yan J, Zhong H, Zhou D, Luo Y, Zhu YG, Xing B, Wang Y. Threats to Terrestrial Plants from Emerging Nanoplastics. ACS NANO 2022; 16:17157-17167. [PMID: 36200753 DOI: 10.1021/acsnano.2c07627] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Nanoplastics are ubiquitous in ecosystems and impact planetary health. However, our current understanding on the impacts of nanoplastics upon terrestrial plants is fragmented. The lack of systematic approaches to evaluating these impacts limits our ability to generalize from existing studies and perpetuates regulatory barriers. Here, we undertook a meta-analysis to quantify the overall strength of nanoplastic impacts upon terrestrial plants and developed a machine learning approach to predict adverse impacts and identify contributing features. We show that adverse impacts are primarily associated with toxicity metrics, followed by plant species, nanoplastic mass concentration and size, and exposure time and medium. These results highlight that the threats of nanoplastics depend on a diversity of reactions across molecular to ecosystem scales. These reactions are rooted in both the spatial and functional complexities of nanoplastics and, as such, are specific to both the plastic characteristics and environmental conditions. These findings demonstrate the utility of interrogating the diversity of toxicity data in the literature to update both risk assessments and evidence-based policy actions.
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Affiliation(s)
- Fei Dang
- Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing210008, P.R. China
| | - Qingyu Wang
- Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing210008, P.R. China
- University of Chinese Academy of Sciences, Beijing100049, P.R. China
| | - Xiliang Yan
- Institute of Environmental Research at Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou510006, P.R. China
| | - Yuanye Zhang
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian361102, P.R. China
| | - Jiachen Yan
- Institute of Environmental Research at Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou510006, P.R. China
| | - Huan Zhong
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing210023, P.R. China
| | - Dongmei Zhou
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing210023, P.R. China
| | - Yongming Luo
- Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing210008, P.R. China
| | - Yong-Guan Zhu
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen361021, P.R. China
| | - Baoshan Xing
- Stockbridge School of Agriculture, University of Massachusetts, Amherst, Massachusetts01003, United States
| | - Yujun Wang
- Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing210008, P.R. China
- University of Chinese Academy of Sciences, Beijing100049, P.R. China
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