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An innovative artificial intelligence-based method to compress complex models into explainable, model-agnostic and reduced decision support systems with application to healthcare (NEAR) Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-12 Karim Kassem, Michela Sperti, Andrea Cavallo, Andrea Mario Vergani, Davide Fassino, Monica Moz, Alessandro Liscio, Riccardo Banali, Michael Dahlweid, Luciano Benetti, Francesco Bruno, Guglielmo Gallone, Ovidio De Filippo, Mario Iannaccone, Fabrizio D'Ascenzo, Gaetano Maria De Ferrari, Umberto Morbiducci, Emanuele Della Valle, Marco Agostino Deriu
In everyday clinical practice, medical decision is currently based on clinical guidelines which are often static and rigid, and do not account for population variability, while individualized, patient-oriented decision and/or treatment are the paradigm change necessary to enter into the era of precision medicine. Most of the limitations of a guideline-based system could be overcome through the adoption
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Stable feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation for biomarker discovery in breast cancer Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-11 Hryhorii Chereda, Andreas Leha, Tim Beißbarth
High-throughput technologies are becoming increasingly important in discovering prognostic biomarkers and in identifying novel drug targets. With Mammaprint, Oncotype DX, and many other prognostic molecular signatures breast cancer is one of the paradigmatic examples of the utility of high-throughput data to deliver prognostic biomarkers, that can be represented in a form of a rather short gene list
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PGKD-Net: Prior-guided and Knowledge Diffusive Network for Choroid Segmentation Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-11 Yaqi Wang, Zehua Yang, Xindi Liu, Zhi Li, Chengyu Wu, Yizhen Wang, Kai Jin, Dechao Chen, Gangyong Jia, Xiaodiao Chen, Juan Ye, Xingru Huang
The thickness of the choroid is considered to be an important indicator of clinical diagnosis. Therefore, accurate choroid segmentation in retinal OCT images is crucial for monitoring various ophthalmic diseases. However, this is still challenging due to the blurry boundaries and interference from other lesions. To address these issues, we propose a novel prior-guided and knowledge diffusive network
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Monitoring multistage healthcare processes using state space models and a machine learning based framework Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-10 Ali Yeganeh, Arne Johannssen, Nataliya Chukhrova, Mohammad Rasouli
Monitoring healthcare processes, such as surgical outcomes, with a keen focus on detecting changes and unnatural conditions at an early stage is crucial for healthcare professionals and administrators. In line with this goal, control charts, which are the most popular tool in the field of Statistical Process Monitoring, are widely employed to monitor therapeutic processes. Healthcare processes are
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Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-04 Benjamin Lambert, Florence Forbes, Senan Doyle, Harmonie Dehaene, Michel Dojat
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. End users are particularly reluctant to rely on the opaque predictions of DL models. Uncertainty quantification methods have been proposed in the literature as a potential solution, to reduce the black-box effect of DL models and
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HR-BGCN [formula omitted] Predicting readmission for heart failure from electronic health records Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-02 Huiting Ma, Dengao Li, Jumin Zhao, Wenjing Li, Jian Fu, Chunxia Li
Heart failure has become a huge public health problem, and failure to accurately predict readmission will further lead to the disease’s high cost and high mortality. The construction of readmission prediction model can assist doctors in making decisions to prevent patients from deteriorating and reduce the cost burden. This paper extracts the patient discharge records from the MIMIC-III database. It
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Never tell me the odds: Investigating pro-hoc explanations in medical decision making Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-01 Federico Cabitza, Chiara Natali, Lorenzo Famiglini, Andrea Campagner, Valerio Caccavella, Enrico Gallazzi
This paper examines a kind of explainable AI, centered around what we term , that is a form of support that consists of offering alternative explanations (one for each possible outcome) a specific explanation following specific advice. Specifically, our support mechanism utilizes , featuring analogous cases for each category in a binary setting. Pro-hoc explanations are an instance of what we called
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Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-27 Zhanzhong Gu, Xiangjian He, Ping Yu, Wenjing Jia, Xiguang Yang, Gang Peng, Penghui Hu, Shiyan Chen, Hongjie Chen, Yiguang Lin
Stroke is a prevalent disease with a significant global impact. Effective assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and optimal clinical outcomes. The National Institutes of Health Stroke Scale (NIHSS) is a widely used scale for quantitatively assessing stroke severity. However, the current manual scoring of NIHSS is labor-intensive, time-consuming, and
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RM-GPT: Enhance the comprehensive generative ability of molecular GPT model via LocalRNN and RealFormer Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-27 Wenfeng Fan, Yue He, Fei Zhu
Due to the surging of cost, artificial intelligence-assisted de novo drug design has supplanted conventional methods and become an emerging option for drug discovery. Although there have arisen many successful examples of applying generative models to the molecular field, these methods struggle to deal with conditional generation that meet chemists’ practical requirements which ask for a controllable
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Machine learning algorithms to predict outcomes in children and adolescents with COVID-19: A systematic review Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-25 Adriano Lages dos Santos, Clara Pinhati, Jonathan Perdigão, Stella Galante, Ludmilla Silva, Isadora Veloso, Ana Cristina Simões e Silva, Eduardo Araújo Oliveira
We aimed to analyze the study designs, modeling approaches, and performance evaluation metrics in studies using machine learning techniques to develop clinical prediction models for children and adolescents with COVID-19. We searched four databases for articles published between 01/01/2020 and 10/25/2023, describing the development of multivariable prediction models using any machine learning technique
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ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial–temporal and long-range dependency features Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-24 Sadia Din, Marwa Qaraqe, Omar Mourad, Khalid Qaraqe, Erchin Serpedin
Cardiac arrhythmia is one of the prime reasons for death globally. Early diagnosis of heart arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed by analyzing the electrocardiogram (ECG) of patients. Manual analysis of ECG is time-consuming and challenging. Hence, effective automated detection of heart arrhythmias is important to produce reliable results. Different
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Automated peripancreatic vessel segmentation and labeling based on iterative trunk growth and weakly supervised mechanism Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-24 Liwen Zou, Zhenghua Cai, Liang Mao, Ziwei Nie, Yudong Qiu, Xiaoping Yang
Peripancreatic vessel segmentation and anatomical labeling are pivotal aspects in aiding surgical planning and prognosis for patients with pancreatic tumors. Nevertheless, prevailing techniques often fall short in achieving satisfactory segmentation performance for the peripancreatic vein (PPV), leading to predictions characterized by poor integrity and connectivity. Besides, unsupervised labeling
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Predicting drug activity against cancer through genomic profiles and SMILES Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-23 Maryam Abbasi, Filipa G. Carvalho, Bernardete Ribeiro, Joel P. Arrais
Due to the constant increase in cancer rates, the disease has become a leading cause of death worldwide, enhancing the need for its detection and treatment. In the era of personalized medicine, the main goal is to incorporate individual variability in order to choose more precisely which therapy and prevention strategies suit each person. However, predicting the sensitivity of tumors to anticancer
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Predicting time-to-intubation after critical care admission using machine learning and cured fraction information Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-22 Michela Venturini, Ingrid Van Keilegom, Wouter De Corte, Celine Vens
Intubation for mechanical ventilation (MV) is one of the most common high-risk procedures performed in Intensive Care Units (ICUs). Early prediction of intubation may have a positive impact by providing timely alerts to clinicians and consequently avoiding high-risk late intubations. In this work, we propose a new machine learning method to predict the time to intubation during the first five days
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Analyzing entropy features in time-series data for pattern recognition in neurological conditions Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-22 Yushan Huang, Yuchen Zhao, Alexander Capstick, Francesca Palermo, Hamed Haddadi, Payam Barnaghi
In the field of medical diagnosis and patient monitoring, effective pattern recognition in neurological time-series data is essential. Traditional methods predominantly based on statistical or probabilistic learning and inference often struggle with multivariate, multi-source, state-varying, and noisy data while also posing privacy risks due to excessive information collection and modeling. Furthermore
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A label information fused medical image report generation framework Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-22 Shuifa Sun, Zhoujunsen Mei, Xiaolong Li, Tinglong Tang, Zhanglin Su, Yirong Wu
Medical imaging is an important tool for clinical diagnosis. Nevertheless, it is very time-consuming and error-prone for physicians to prepare imaging diagnosis reports. Therefore, it is necessary to develop some methods to generate medical imaging reports automatically. Currently, the task of medical imaging report generation is challenging in at least two aspects: (1) medical images are very similar
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Online biomedical named entities recognition by data and knowledge-driven model Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-21 Lulu Cao, Chaochen Wu, Guan Luo, Chao Guo, Anni Zheng
Named entity recognition (NER) is an important task for the natural language processing of biomedical text. Currently, most NER studies standardized biomedical text, but NER for unstandardized biomedical text draws less attention from researchers. Named entities in online biomedical text exist with errors and polymorphisms, which negatively impact NER models’ performance and impede support from knowledge
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Intelligent decision support systems for dementia care: A scoping review Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-19 Amirhossein Eslami Andargoli, Nalika Ulapane, Tuan Anh Nguyen, Nadeem Shuakat, John Zelcer, Nilmini Wickramasinghe
In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support systems have the potential to enhance diagnosis and management. However, the scope and challenges of applying these technologies remain unclear. This scoping review aims to investigate the current state of AI applications in the development of intelligent decision support systems for dementia care. We conducted
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Patient-specific game-based transfer method for Parkinson's disease severity prediction Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-17 Zaifa Xue, Huibin Lu, Tao Zhang, Max A. Little
Dysphonia is one of the early symptoms of Parkinson's disease (PD). Most existing methods use feature selection methods to find the optimal subset of voice features for all PD patients. Few have considered the heterogeneity between patients, which implies the need to provide specific prediction models for different patients. However, building the specific model faces the challenge of small sample size
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GCNGAT: Drug–disease association prediction based on graph convolution neural network and graph attention network Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-17 Runtao Yang, Yao Fu, Qian Zhang, Lina Zhang
Predicting drug–disease associations can contribute to discovering new therapeutic potentials of drugs, and providing important association information for new drug research and development. Many existing drug–disease association prediction methods have not distinguished relevant background information for the same drug targeted to different diseases. Therefore, this paper proposes a drug–disease association
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A bimodal feature fusion convolutional neural network for detecting obstructive sleep apnea/hypopnea from nasal airflow and oximetry signals Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-16 Dandan Peng, Huijun Yue, Wenjun Tan, Wenbin Lei, Guozhu Chen, Wen Shi, Yanchun Zhang
The most prevalent sleep-disordered breathing condition is Obstructive Sleep Apnea (OSA), which has been linked to various health consequences, including cardiovascular disease (CVD) and even sudden death. Therefore, early detection of OSA can effectively help patients prevent the diseases induced by it. However, many existing methods have low accuracy in detecting hypopnea events or even ignore them
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Clinical knowledge-guided deep reinforcement learning for sepsis antibiotic dosing recommendations Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-15 Yuan Wang, Anqi Liu, Jucheng Yang, Lin Wang, Ning Xiong, Yisong Cheng, Qin Wu
Sepsis is the third leading cause of death worldwide. Antibiotics are an important component in the treatment of sepsis. The use of antibiotics is currently facing the challenge of increasing antibiotic resistance (Evans et al., 2021). Sepsis medication prediction can be modeled as a Markov decision process, but existing methods fail to integrate with medical knowledge, making the decision process
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Triplet-branch network with contrastive prior-knowledge embedding for disease grading Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-15 Yuexiang Li, Yanping Wang, Guang Lin, Yawen Huang, Jingxin Liu, Yi Lin, Dong Wei, Qirui Zhang, Kai Ma, Zhiqiang Zhang, Guangming Lu, Yefeng Zheng
Since different disease grades require different treatments from physicians, the low-grade patients may recover with follow-up observations whereas the high-grade may need immediate surgery, the accuracy of disease grading is pivotal in clinical practice. In this paper, we propose a Triplet-Branch Network with ContRastive priOr-knoWledge embeddiNg (TBN-CROWN) for the accurate disease grading, which
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CSCA U-Net: A channel and space compound attention CNN for medical image segmentation Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-14 Xin Shu, Jiashu Wang, Aoping Zhang, Jinlong Shi, Xiao-Jun Wu
Image segmentation is one of the vital steps in medical image analysis. A large number of methods based on convolutional neural networks have emerged, which can extract abstract features from multiple-modality medical images, learn valuable information that is difficult to recognize by humans, and obtain more reliable results than traditional image segmentation approaches. U-Net, due to its simple
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Detecting mental and physical disorders using multi-task learning equipped with knowledge graph attention network Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-14 Wei Zhang, Ling Kong, Soobin Lee, Yan Chen, Guangxu Zhang, Hao Wang, Min Song
Mental and physical disorders (MPD) are inextricably linked in many medical cases; psychosomatic diseases can be induced by mental concerns and psychological discomfort can ensue from physiological diseases. However, existing medical informatics studies focus on identifying mental or physical disorders from a unilateral perspective. Consequently, no existing domain knowledge base, corpus, or detection
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Automated image label extraction from radiology reports — A review Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-14 Sofia C. Pereira, Ana Maria Mendonça, Aurélio Campilho, Pedro Sousa, Carla Teixeira Lopes
Machine Learning models need large amounts of annotated data for training. In the field of medical imaging, labeled data is especially difficult to obtain because the annotations have to be performed by qualified physicians. Natural Language Processing (NLP) tools can be applied to radiology reports to extract labels for medical images automatically. Compared to manual labeling, this approach requires
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Healthcare facilities management: A novel data-driven model for predictive maintenance of computed tomography equipment Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-12 Haopeng Zhou, Qilin Liu, Haowen Liu, Zhu Chen, Zhenlin Li, Yixuan Zhuo, Kang Li, Changxi Wang, Jin Huang
The breakdown of healthcare facilities is a huge challenge for hospitals. Medical images obtained by Computed Tomography (CT) provide information about the patients' physical conditions and play a critical role in diagnosis of disease. To deliver high-quality medical images on time, it is essential to minimize the occurrence frequencies of anomalies and failures of the equipment. We extracted the real-time
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Prognostic prediction of sepsis patient using transformer with skip connected token for tabular data Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-12 Jee-Woo Choi, Minuk Yang, Jae-Woo Kim, Yoon Mi Shin, Yong-Goo Shin, Seung Park
Sepsis is known as a common syndrome in intensive care units (ICU), and severe sepsis and septic shock are among the leading causes of death worldwide. The purpose of this study is to develop a deep learning model that supports clinicians in efficiently managing sepsis patients in the ICU by predicting mortality, ICU length of stay (>14 days), and hospital length of stay (>30 days). The proposed model
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Temporal self-attention for risk prediction from electronic health records using non-stationary kernel approximation Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-10 Rawan AlSaad, Qutaibah Malluhi, Alaa Abd-alrazaq, Sabri Boughorbel
Effective modeling of patient representation from electronic health records (EHRs) is increasingly becoming a vital research topic. Yet, modeling the non-stationarity in EHR data has received less attention. Most existing studies follow a strong assumption of stationarity in patient representation from EHRs. However, in practice, a patient’s visits are irregularly spaced over a relatively long period
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Teleconsultation dynamic scheduling with a deep reinforcement learning approach Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-09 Wenjia Chen, Jinlin Li
In this study, the start time of teleconsultations is optimized for the clinical departments of class A tertiary hospitals to improve service quality and efficiency. For this purpose, first, a general teleconsultation scheduling model is formulated. In the formulation, the number of services (NS) is one of the objectives because of demand intermittency and service mobility. Demand intermittency means
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Improving deep-learning electrocardiogram classification with an effective coloring method Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-09 Wei-Wen Chen, Chien-Chao Tseng, Ching-Chun Huang, Henry Horng-Shing Lu
Cardiovascular diseases, particularly arrhythmias, remain a leading cause of mortality worldwide. Electrocardiogram (ECG) analysis plays a pivotal role in cardiovascular disease diagnosis. Although previous studies have focused on waveform analysis and model training, integrating additional clinical information, especially demographic data, remains challenging. In this study, we present an innovative
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A novel intelligent model for visualized inference of medical diagnosis: A case of TCM Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-08 Jiang Qi-yu, Huang Wen-heng, Liang Jia-fen, Sun Xiao-sheng
How to present an intelligent model based on known diagnostic knowledge to assist medical diagnosis and display the reasoning process is an interesting issue worth exploring. This study developed a novel intelligent model for visualized inference of medical diagnosis with a case of Traditional Chinese Medicine (TCM). Four classes of TCM's diagnosis composed of Yin deficiency, Liver Yin deficiency,
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Corrigendum to “DeepGA for automatically estimating fetal gestational age through ultrasound imaging” [Artif. Intell. Med. 135 (2023) 102453] Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-06 Tingting Dan, Xijie Chen, Miao He, Hongmei Guo, Xiaoqin He, Jiazhou Chen, Jianbo Xian, Yu Hu, Bin Zhang, Nan Wang, Hongning Xie, Hongmin Cai
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DISCOVER: 2-D multiview summarization of Optical Coherence Tomography Angiography for automatic diabetic retinopathy diagnosis Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-06 Mostafa El Habib Daho, Yihao Li, Rachid Zeghlache, Hugo Le Boité, Pierre Deman, Laurent Borderie, Hugang Ren, Niranchana Mannivanan, Capucine Lepicard, Béatrice Cochener, Aude Couturier, Ramin Tadayoni, Pierre-Henri Conze, Mathieu Lamard, Gwenolé Quellec
Diabetic Retinopathy (DR), an ocular complication of diabetes, is a leading cause of blindness worldwide. Traditionally, DR is monitored using Color Fundus Photography (CFP), a widespread 2-D imaging modality. However, DR classifications based on CFP have poor predictive power, resulting in suboptimal DR management. Optical Coherence Tomography Angiography (OCTA) is a recent 3-D imaging modality offering
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NPB-REC: A non-parametric Bayesian deep-learning approach for undersampled MRI reconstruction with uncertainty estimation Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-05 Samah Khawaled, Moti Freiman
The ability to reconstruct high-quality images from undersampled MRI data is vital in improving MRI temporal resolution and reducing acquisition times. Deep learning methods have been proposed for this task, but the lack of verified methods to quantify the uncertainty in the reconstructed images hampered clinical applicability. We introduce “NPB-REC”, a non-parametric fully Bayesian framework, for
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Scalable Swin Transformer network for brain tumor segmentation from incomplete MRI modalities Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-02 Dongsong Zhang, Changjian Wang, Tianhua Chen, Weidao Chen, Yiqing Shen
Deep learning methods have shown great potential in processing multi-modal Magnetic Resonance Imaging (MRI) data, enabling improved accuracy in brain tumor segmentation. However, the performance of these methods can suffer when dealing with incomplete modalities, which is a common issue in clinical practice. Existing solutions, such as missing modality synthesis, knowledge distillation, and architecture-based
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Modelling-based joint embedding of histology and genomics using canonical correlation analysis for breast cancer survival prediction Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-26 Vaishnavi Subramanian, Tanveer Syeda-Mahmood, Minh N. Do
Traditional approaches to predicting breast cancer patients’ survival outcomes were based on clinical subgroups, the PAM50 genes, or the histological tissue’s evaluation. With the growth of multi-modality datasets capturing diverse information (such as genomics, histology, radiology and clinical data) about the same cancer, information can be integrated using advanced tools and have improved survival
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Development and validation of a deep interpretable network for continuous acute kidney injury prediction in critically ill patients Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-26 Meicheng Yang, Songqiao Liu, Tong Hao, Caiyun Ma, Hui Chen, Yuwen Li, Changde Wu, Jianfeng Xie, Haibo Qiu, Jianqing Li, Yi Yang, Chengyu Liu
Early detection of acute kidney injury (AKI) may provide a crucial window of opportunity to prevent further injury, which helps improve clinical outcomes. This study aimed to develop a deep interpretable network for continuously predicting the 24-hour AKI risk in real-time and evaluate its performance internally and externally in critically ill patients. A total of 21,163 patients' electronic health
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Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-24 Sarina Aminizadeh, Arash Heidari, Mahshid Dehghan, Shiva Toumaj, Mahsa Rezaei, Nima Jafari Navimipour, Fabio Stroppa, Mehmet Unal
The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Traditional healthcare methods, often characterized by limitations in disease prevention, predominantly react to illnesses after their onset rather than proactively averting them. The advent of Artificial Intelligence (AI) has ushered in a wave of transformative
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Multi input–Multi output 3D CNN for dementia severity assessment with incomplete multimodal data Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-24 Michela Gravina, Angel García-Pedrero, Consuelo Gonzalo-Martín, Carlo Sansone, Paolo Soda
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Multilevel Bayesian network to model child morbidity using Gibbs sampling Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-24 Bezalem Eshetu Yirdaw, Legesse Kassa Debusho
Bayesian networks (BNs) are suitable models for studying complex interdependencies between multiple health outcomes, simultaneously. However, these models fail the assumption of independent observation in the case of hierarchical data. Therefore, this study proposes a two and three-level random intercept multilevel Bayesian network (MBN) models to study the conditional dependencies between multiple
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Subspace corrected relevance learning with application in neuroimaging Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-24 Rick van Veen, Neha Rajendra Bari Tamboli, Sofie Lövdal, Sanne K. Meles, Remco J. Renken, Gert-Jan de Vries, Dario Arnaldi, Silvia Morbelli, Pedro Clavero, José A. Obeso, Maria C. Rodriguez Oroz, Klaus L. Leenders, Thomas Villmann, Michael Biehl
In machine learning, data often comes from different sources, but combining them can introduce extraneous variation that affects both generalization and interpretability. For example, we investigate the classification of neurodegenerative diseases using FDG-PET data collected from multiple neuroimaging centers. However, data collected at different centers introduces unwanted variation due to differences
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Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-22 Ivan De Boi, Elissa Embrechts, Quirine Schatteman, Rudi Penne, Steven Truijen, Wim Saeys
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A deep convolutional neural network for the automatic segmentation of glioblastoma brain tumor: Joint spatial pyramid module and attention mechanism network Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-21 Hengxin Liu, Jingteng Huang, Qiang Li, Xin Guan, Minglang Tseng
This study proposes a deep convolutional neural network for the automatic segmentation of glioblastoma brain tumors, aiming sat replacing the manual segmentation method that is both time-consuming and labor-intensive. There are many challenges for automatic segmentation to finely segment sub-regions from multi-sequence magnetic resonance images because of the complexity and variability of glioblastomas
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A clinically actionable and explainable real-time risk assessment framework for stroke-associated pneumonia Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-20 Lutao Dai, Xin Yang, Hao Li, Xingquan Zhao, Lin Lin, Yong Jiang, Yongjun Wang, Zixiao Li, Haipeng Shen
The current medical practice is more responsive rather than proactive, despite the widely recognized value of early disease detection, including improving the quality of care and reducing medical costs. One of the cornerstones of early disease detection is clinically actionable predictions, where predictions are expected to be accurate, stable, real-time and interpretable. As an example, we used stroke-associated
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AI in medical diagnosis: AI prediction & human judgment Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-20 Dóra Göndöcs, Viktor Dörfler
AI has long been regarded as a panacea for decision-making and many other aspects of knowledge work; as something that will help humans get rid of their shortcomings. We believe that AI can be a useful asset to support decision-makers, but not that it should replace decision-makers. Decision-making uses algorithmic analysis, but it is not solely algorithmic analysis; it also involves other factors
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Designing explainable AI to improve human-AI team performance: A medical stakeholder-driven scoping review Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-20 Harishankar V. Subramanian, Casey Canfield, Daniel B. Shank
The rise of complex AI systems in healthcare and other sectors has led to a growing area of research called Explainable AI (XAI) designed to increase transparency. In this area, quantitative and qualitative studies focus on improving user trust and task performance by providing system- and prediction-level XAI features. We analyze stakeholder engagement events (interviews and workshops) on the use
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A clinical consensus-compliant deep learning approach to quantitatively evaluate human in vitro fertilization early embryonic development with optical microscope images Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-19 Zaowen Liao, Chaoyu Yan, Jianbo Wang, Ningfeng Zhang, Huan Yang, Chenghao Lin, Haiyue Zhang, Wenjun Wang, Weizhong Li
The selection of embryos is a key for the success of fertilization (IVF). However, automatic quality assessment on human IVF embryos with optical microscope images is still challenging. In this study, we developed a clinical consensus-compliant deep learning approach, named Esava (Embryo Segmentation and Viability Assessment), to quantitatively evaluate the development of IVF embryos using optical
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Prediction on nature of cancer by fuzzy graphoidal covering number using artificial neural network Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-18 Anushree Bhattacharya, Madhumangal Pal
Predicting the chances of various types of cancers for different organs in the human body is a typical decision-making process in medicine and health. The signalling pathways have played a vital role in increasing or decreasing the possibility of the deadliest disease, cancer. To combine the pathways concept and ambiguity in the prediction techniques of such diseases, we have used the proposed research
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MSEF-Net: Multi-scale edge fusion network for lumbosacral plexus segmentation with MR image Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-17 Junyong Zhao, Liang Sun, Zhi Sun, Xin Zhou, Haipeng Si, Daoqiang Zhang
Nerve damage of spine areas is a common cause of disability and paralysis. The lumbosacral plexus segmentation from magnetic resonance imaging (MRI) scans plays an important role in many computer-aided diagnosis and surgery of spinal nerve lesions. Due to complex structure and low contrast of the lumbosacral plexus, it is difficult to delineate the regions of edges accurately. To address this issue
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The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-17 Abdallah Alabdallah, Mattias Ohlsson, Sepideh Pashami, Thorsteinn Rögnvaldsson
The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained
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Stacked deep learning approach for efficient SARS-CoV-2 detection in blood samples Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-14 Wu Wang, Fouzi Harrou, Abdelkader Dairi, Ying Sun
Identifying COVID-19 through blood sample analysis is crucial in managing the disease and improving patient outcomes. Despite its advantages, the current test demands certified laboratories, expensive equipment, trained personnel, and 3–4 h for results, with a notable false-negative rate of 15%–20%. This study proposes a stacked deep-learning approach for detecting COVID-19 in blood samples to distinguish
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SSLDTI: A novel method for drug-target interaction prediction based on self-supervised learning Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-18 Zhixian Liu, Qingfeng Chen, Wei Lan, Huihui Lu, Shichao Zhang
Many computational methods have been proposed to identify potential drug-target interactions (DTIs) to expedite drug development. Graph neural network (GNN) methods are considered to be one of the most effective approaches. However, shallow GNN methods can only aggregate local information from nodes. Also, deep GNN methods may result in over-smoothing while obtaining long-distance neighbourhood information
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Integrated block-wise neural network with auto-learning search framework for finger gesture recognition using sEMG signals Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-17 Shurun Wang, Hao Tang, Feng Chen, Qi Tan, Qi Jiang
Accurate finger gesture recognition with surface electromyography (sEMG) is essential and long-challenge in the muscle-computer interface, and many high-performance deep learning models have been developed to predict gestures. For these models, problem-specific tuning of network architecture is essential for improving the performance, yet it requires substantial knowledge of network architecture design
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An interpretable dual attention network for diabetic retinopathy grading: IDANet Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-17 Amit Bhati, Neha Gour, Pritee Khanna, Aparajita Ojha, Naoufel Werghi
Diabetic retinopathy (DR) is the most prevalent cause of visual impairment in adults worldwide. Typically, patients with DR do not show symptoms until later stages, by which time it may be too late to receive effective treatment. DR Grading is challenging because of the small size and variation in lesion patterns. The key to fine-grained DR grading is to discover more discriminating elements such as
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HoRDA: Learning higher-order structure information for predicting RNA-disease associations Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-15 Julong Li, Jianrui Chen, Zhihui Wang, Xiujuan Lei
CircRNA and miRNA are crucial non-coding RNAs, which are associated with biological diseases. Exploring the associations between RNAs and diseases often requires a significant time and financial investments, which has been greatly alleviated and improved with the application of deep learning methods in bioinformatics. However, existing methods often fail to achieve higher accuracy and can not be universal
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Combining general and personal models for epilepsy detection with hyperdimensional computing Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-05 Una Pale, Tomas Teijeiro, Sylvain Rheims, Philippe Ryvlin, David Atienza
Epilepsy is a highly prevalent chronic neurological disorder with great negative impact on patients’ daily lives. Despite this there is still no adequate technological support to enable epilepsy detection and continuous outpatient monitoring in everyday life. Hyperdimensional (HD) computing is a promising method for epilepsy detection via wearable devices, characterized by a simpler learning process
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A new word embedding model integrated with medical knowledge for deep learning-based sentiment classification Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-08 Aye Hninn Khine, Wiphada Wettayaprasit, Jarunee Duangsuwan
The development of intelligent systems that use social media data for decision-making processes in numerous domains such as politics, business, marketing, and finance, has been made possible by the popularity of social media platforms. However, the utilization of textual data from social media in the healthcare management industry is still somewhat limited when it is compared to other industries. Investigating