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Tafavvoghi M, Bongo LA, Shvetsov N, Busund LTR, Møllersen K. Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review. J Pathol Inform 2024; 15:100363. [PMID: 38405160 PMCID: PMC10884505 DOI: 10.1016/j.jpi.2024.100363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/24/2023] [Accepted: 01/23/2024] [Indexed: 02/27/2024] Open
Abstract
Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and robust deep learning models. In this scoping review, we identified the publicly available datasets of breast H&E-stained whole-slide images (WSIs) that can be used to develop deep learning algorithms. We systematically searched 9 scientific literature databases and 9 research data repositories and found 17 publicly available datasets containing 10 385 H&E WSIs of breast cancer. Moreover, we reported image metadata and characteristics for each dataset to assist researchers in selecting proper datasets for specific tasks in breast cancer computational pathology. In addition, we compiled 2 lists of breast H&E patches and private datasets as supplementary resources for researchers. Notably, only 28% of the included articles utilized multiple datasets, and only 14% used an external validation set, suggesting that the performance of other developed models may be susceptible to overestimation. The TCGA-BRCA was used in 52% of the selected studies. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms. There is also a lack of consistent metadata reporting of breast WSI datasets that can be an issue in developing accurate deep learning models, indicating the necessity of establishing explicit guidelines for documenting breast WSI dataset characteristics and metadata.
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Affiliation(s)
- Masoud Tafavvoghi
- Department of Community Medicine, Uit The Arctic University of Norway, Tromsø, Norway
| | - Lars Ailo Bongo
- Department of Computer Science, Uit The Arctic University of Norway, Tromsø, Norway
| | - Nikita Shvetsov
- Department of Computer Science, Uit The Arctic University of Norway, Tromsø, Norway
| | | | - Kajsa Møllersen
- Department of Community Medicine, Uit The Arctic University of Norway, Tromsø, Norway
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2
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Yu B, Kaku A, Liu K, Parnandi A, Fokas E, Venkatesan A, Pandit N, Ranganath R, Schambra H, Fernandez-Granda C. Quantifying impairment and disease severity using AI models trained on healthy subjects. NPJ Digit Med 2024; 7:180. [PMID: 38969786 PMCID: PMC11226623 DOI: 10.1038/s41746-024-01173-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 06/21/2024] [Indexed: 07/07/2024] Open
Abstract
Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a framework to address this challenge, which leverages AI models trained exclusively on healthy individuals. The COnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the decrease in confidence of these models when presented with impaired or diseased patients to quantify their deviation from the healthy population. We applied the COBRA score to address a key limitation of current clinical evaluation of upper-body impairment in stroke patients. The gold-standard Fugl-Meyer Assessment (FMA) requires in-person administration by a trained assessor for 30-45 minutes, which restricts monitoring frequency and precludes physicians from adapting rehabilitation protocols to the progress of each patient. The COBRA score, computed automatically in under one minute, is shown to be strongly correlated with the FMA on an independent test cohort for two different data modalities: wearable sensors (ρ = 0.814, 95% CI [0.700,0.888]) and video (ρ = 0.736, 95% C.I [0.584, 0.838]). To demonstrate the generalizability of the approach to other conditions, the COBRA score was also applied to quantify severity of knee osteoarthritis from magnetic-resonance imaging scans, again achieving significant correlation with an independent clinical assessment (ρ = 0.644, 95% C.I [0.585,0.696]).
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Affiliation(s)
- Boyang Yu
- Center for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA
| | - Aakash Kaku
- Center for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA
| | - Kangning Liu
- Center for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA
| | - Avinash Parnandi
- Department of Neurology, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
- Department of Rehabilitation Medicine, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
| | - Emily Fokas
- Department of Neurology, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
| | - Anita Venkatesan
- Department of Neurology, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
| | - Natasha Pandit
- Department of Rehabilitation Medicine, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
| | - Rajesh Ranganath
- Center for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA
- Courant Institute of Mathematical Sciences, New York University, 251 Mercer St, New York, NY, 10012, USA
| | - Heidi Schambra
- Department of Neurology, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA.
- Department of Rehabilitation Medicine, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA.
| | - Carlos Fernandez-Granda
- Center for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA.
- Courant Institute of Mathematical Sciences, New York University, 251 Mercer St, New York, NY, 10012, USA.
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3
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Gangwal A, Ansari A, Ahmad I, Azad AK, Wan Sulaiman WMA. Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review. Comput Biol Med 2024; 179:108734. [PMID: 38964243 DOI: 10.1016/j.compbiomed.2024.108734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 06/01/2024] [Accepted: 06/08/2024] [Indexed: 07/06/2024]
Abstract
Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This development has been further accelerated with the increasing use of machine learning (ML), mainly deep learning (DL), and computing hardware and software advancements. As a result, initial doubts about the application of AI in drug discovery have been dispelled, leading to significant benefits in medicinal chemistry. At the same time, it is crucial to recognize that AI is still in its infancy and faces a few limitations that need to be addressed to harness its full potential in drug discovery. Some notable limitations are insufficient, unlabeled, and non-uniform data, the resemblance of some AI-generated molecules with existing molecules, unavailability of inadequate benchmarks, intellectual property rights (IPRs) related hurdles in data sharing, poor understanding of biology, focus on proxy data and ligands, lack of holistic methods to represent input (molecular structures) to prevent pre-processing of input molecules (feature engineering), etc. The major component in AI infrastructure is input data, as most of the successes of AI-driven efforts to improve drug discovery depend on the quality and quantity of data, used to train and test AI algorithms, besides a few other factors. Additionally, data-gulping DL approaches, without sufficient data, may collapse to live up to their promise. Current literature suggests a few methods, to certain extent, effectively handle low data for better output from the AI models in the context of drug discovery. These are transferring learning (TL), active learning (AL), single or one-shot learning (OSL), multi-task learning (MTL), data augmentation (DA), data synthesis (DS), etc. One different method, which enables sharing of proprietary data on a common platform (without compromising data privacy) to train ML model, is federated learning (FL). In this review, we compare and discuss these methods, their recent applications, and limitations while modeling small molecule data to get the improved output of AI methods in drug discovery. Article also sums up some other novel methods to handle inadequate data.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal's Institute of Pharmacy, Dhule, 424001, Maharashtra, India.
| | - Azim Ansari
- Computer Aided Drug Design Center, Shri Vile Parle Kelavani Mandal's Institute of Pharmacy, Dhule, 424001, Maharashtra, India
| | - Iqrar Ahmad
- Department of Pharmaceutical Chemistry, Prof. Ravindra Nikam College of Pharmacy, Gondur, Dhule, 424002, Maharashtra, India.
| | - Abul Kalam Azad
- Faculty of Pharmacy, University College of MAIWP International, Batu Caves, 68100, Kuala Lumpur, Malaysia.
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Wee NK, Git KA, Lee WJ, Raval G, Pattokhov A, Ho ELM, Chuapetcharasopon C, Tomiyama N, Ng KH, Tan CH. Position Statements of the Emerging Trends Committee of the Asian Oceanian Society of Radiology on the Adoption and Implementation of Artificial Intelligence for Radiology. Korean J Radiol 2024; 25:603-612. [PMID: 38942454 PMCID: PMC11214917 DOI: 10.3348/kjr.2024.0419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/12/2024] [Accepted: 05/14/2024] [Indexed: 06/30/2024] Open
Abstract
Artificial intelligence (AI) is rapidly gaining recognition in the radiology domain as a greater number of radiologists are becoming AI-literate. However, the adoption and implementation of AI solutions in clinical settings have been slow, with points of contention. A group of AI users comprising mainly clinical radiologists across various Asian countries, including India, Japan, Malaysia, Singapore, Taiwan, Thailand, and Uzbekistan, formed the working group. This study aimed to draft position statements regarding the application and clinical deployment of AI in radiology. The primary aim is to raise awareness among the general public, promote professional interest and discussion, clarify ethical considerations when implementing AI technology, and engage the radiology profession in the ever-changing clinical practice. These position statements highlight pertinent issues that need to be addressed between care providers and care recipients. More importantly, this will help legalize the use of non-human instruments in clinical deployment without compromising ethical considerations, decision-making precision, and clinical professional standards. We base our study on four main principles of medical care-respect for patient autonomy, beneficence, non-maleficence, and justice.
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Affiliation(s)
- Nicole Kessa Wee
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, National Healthcare Group, Singapore
| | - Kim-Ann Git
- Department of Diagnostic Radiology, Pantai Hospital, Kuala Lumpur, Malaysia
| | - Wen-Jeng Lee
- Department of Diagnostic Radiology, National Taiwan University Hospital, Taipei, Taiwan
| | - Gaurang Raval
- Department of Diagnostic Radiology, Workhardt Hospitals Limited, Mumbai, India
| | - Aziz Pattokhov
- Faculty of Medicine, Tashkent State Dental Institute, Tashkent, Uzbekistan
| | - Evelyn Lai Ming Ho
- Department of Diagnostic Radiology, ParkCity Medical Centre, Kuala Lumpur, Malaysia
| | | | - Noriyuki Tomiyama
- Department of Diagnostic and Interventional Radiology Suita, Osaka University Hospital, Osaka, Japan
| | - Kwan Hoong Ng
- Department of Biomedical Imaging and University of Malaya Research Imaging Centre, University of Malaya, Kuala Lumpur, Malaysia
- Faculty of Medicine and Health Sciences, UCSI University Springhill Campus, Port Dickson, Malaysia
| | - Cher Heng Tan
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, National Healthcare Group, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
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Xia W, Gao Y, Fang X, Jin L, Liu R, Wang LS, Deng Y, Gao J, Yang H, Wu W, Gao H. Simulated gastrointestinal digestion of walnut protein yields anti-inflammatory peptides. Food Chem 2024; 445:138646. [PMID: 38382250 DOI: 10.1016/j.foodchem.2024.138646] [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: 11/07/2023] [Revised: 01/06/2024] [Accepted: 01/30/2024] [Indexed: 02/23/2024]
Abstract
The impact of the simulated gastrointestinal digestion process on walnut protein and the potential anti-inflammatory properties of its metabolites was studied. Structural changes induced by digestion, notably in α-Helix, β-Turn, and Random Coil configurations, were unveiled. Proteins over 10,000 Da significantly decreased by 35.6 %. Antioxidant activity in these metabolites paralleled increased amino acid content. Molecular docking identified three walnut polypeptides-IPAGTPVYLINR, FQGQLPR, and VVYVLR-with potent anti-inflammatory properties. RMSD and RMSF analysis demonstrated the stable and flexible interaction of these polypeptides with their target proteins. In lipopolysaccharide (LPS)-induced inflammation in normal human colon mucosal epithelial NCM460 cells, these peptides decreased 5-hydroxytryptamine (5-HT), tumor necrosis factor-alpha (TNF-α), and vascular endothelial growth factor (VEGF) expression, while mitigating cell apoptosis and inflammation. Our study offers valuable insights into walnut protein physiology, shedding light on its potential health benefits.
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Affiliation(s)
- Wei Xia
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Key Laboratory of Post-Harvest Handling of Fruits, Ministry of Agriculture and Rural Affairs, Key Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang Province, Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Yuan Gao
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
| | - Xiangjun Fang
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Key Laboratory of Post-Harvest Handling of Fruits, Ministry of Agriculture and Rural Affairs, Key Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang Province, Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Long Jin
- Chacha Food Co., Ltd., Hefei 230061, China
| | - Ruiling Liu
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Key Laboratory of Post-Harvest Handling of Fruits, Ministry of Agriculture and Rural Affairs, Key Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang Province, Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Li-Shu Wang
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA; Department of Hematology and Hematopoietic Cell Transplantation, Comprehensive, Cancer Center, City of Hope National Medical Center, Duarte, CA, USA
| | - Yangyong Deng
- Hangzhou Yaoshengji Food Co., Ltd., Hangzhou 310052, China
| | - Junlong Gao
- Hangzhou Yaoshengji Food Co., Ltd., Hangzhou 310052, China
| | - Hailong Yang
- College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China.
| | - Weijie Wu
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Key Laboratory of Post-Harvest Handling of Fruits, Ministry of Agriculture and Rural Affairs, Key Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang Province, Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.
| | - Haiyan Gao
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Key Laboratory of Post-Harvest Handling of Fruits, Ministry of Agriculture and Rural Affairs, Key Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang Province, Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.
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6
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Su JM, Hsu SY, Fang TY, Wang PC. Developing and validating a knowledge-based AI assessment system for learning clinical core medical knowledge in otolaryngology. Comput Biol Med 2024; 178:108765. [PMID: 38897143 DOI: 10.1016/j.compbiomed.2024.108765] [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: 11/24/2023] [Revised: 05/11/2024] [Accepted: 06/13/2024] [Indexed: 06/21/2024]
Abstract
BACKGROUND Clinical core medical knowledge (CCMK) learning is essential for medical trainees. Adaptive assessment systems can facilitate self-learning, but extracting experts' CCMK is challenging, especially using modern data-driven artificial intelligence (AI) approaches (e.g., deep learning). OBJECTIVES This study aims to develop a multi-expert knowledge-aggregated adaptive assessment scheme (MEKAS) using knowledge-based AI approaches to facilitate the learning of CCMK in otolaryngology (CCMK-OTO) and validate its effectiveness through a one-month training program for CCMK-OTO education at a tertiary referral hospital. METHODS The MEKAS utilized the repertory grid technique and case-based reasoning to aggregate experts' knowledge to construct a representative CCMK base, thereby enabling adaptive assessment for CCMK-OTO training. The effects of longitudinal training were compared between the experimental group (EG) and the control group (CG). Both groups received a normal training program (routine meeting, outpatient/operation room teaching, and classroom teaching), while EG received MEKAS for self-learning. The EG comprised 22 UPGY trainees (6 postgraduate [PGY] and 16 undergraduate [UGY] trainees) and 8 otolaryngology residents (ENT-R); the CG comprised 24 UPGY trainees (8 PGY and 16 UGY trainees). The training effectiveness was compared through pre- and post-test CCMK-OTO scores, and user experiences were evaluated using a technology acceptance model-based questionnaire. RESULTS Both UPGY (z = -3.976, P < 0.001) and ENT-R (z = -2.038, P = 0.042) groups in EG exhibited significant improvements in their CCMK-OTO scores, while UPGY in CG did not (z = -1.204, P = 0.228). The UPGY group in EG also demonstrated a substantial improvement compared to the UPGY group in CG (z = -4.943, P < 0.001). The EG participants were highly satisfied with the MEKAS system concerning self-learning assistance, adaptive testing, perceived satisfaction, intention to use, perceived usefulness, perceived ease of use, and perceived enjoyment, rating it between an overall average of 3.8 and 4.1 out of 5.0 on all scales. CONCLUSIONS The MEKAS system facilitates CCMK-OTO learning and provides an efficient knowledge aggregation scheme that can be applied to other medical subjects to efficiently build adaptive assessment systems for CCMK learning. Larger-scale validation across diverse institutions and settings is warranted further to assess MEKAS's scalability, generalizability, and long-term impact.
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Affiliation(s)
- Jun-Ming Su
- Department of Information and Learning Technology, National University of Tainan, Tainan, Taiwan.
| | - Su-Yi Hsu
- Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan; School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan; School of Medicine, National Tsing Hua University, Hsinchu, Taiwan.
| | - Te-Yung Fang
- Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan; School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan; Department of Otolaryngology, Sijhih Cathay General Hospital, New Taipei City, Taiwan.
| | - Pa-Chun Wang
- Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan; School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan; Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan.
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7
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Ahmed FS, Aly S, Liu X. EPI-Trans: an effective transformer-based deep learning model for enhancer promoter interaction prediction. BMC Bioinformatics 2024; 25:216. [PMID: 38890584 PMCID: PMC11184834 DOI: 10.1186/s12859-024-05784-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 04/15/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Recognition of enhancer-promoter Interactions (EPIs) is crucial for human development. EPIs in the genome play a key role in regulating transcription. However, experimental approaches for classifying EPIs are too expensive in terms of effort, time, and resources. Therefore, more and more studies are being done on developing computational techniques, particularly using deep learning and other machine learning techniques, to address such problems. Unfortunately, the majority of current computational methods are based on convolutional neural networks, recurrent neural networks, or a combination of them, which don't take into consideration contextual details and the long-range interactions between the enhancer and promoter sequences. A new transformer-based model called EPI-Trans is presented in this study to overcome the aforementioned limitations. The multi-head attention mechanism in the transformer model automatically learns features that represent the long interrelationships between enhancer and promoter sequences. Furthermore, a generic model is created with transferability that can be utilized as a pre-trained model for various cell lines. Moreover, the parameters of the generic model are fine-tuned using a particular cell line dataset to improve performance. RESULTS Based on the results obtained from six benchmark cell lines, the average AUROC for the specific, generic, and best models is 94.2%, 95%, and 95.7%, while the average AUPR is 80.5%, 66.1%, and 79.6% respectively. CONCLUSIONS This study proposed a transformer-based deep learning model for EPI prediction. The comparative results on certain cell lines show that EPI-Trans outperforms other cutting-edge techniques and can provide superior performance on the challenge of recognizing EPI.
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Affiliation(s)
- Fatma S Ahmed
- Department of Computer Science and Technology, Xiamen University, Xiamen, 361005, China.
- Department of Electrical Engineering, Aswan University, Aswan, 81542, Egypt.
| | - Saleh Aly
- Department of Electrical Engineering, Aswan University, Aswan, 81542, Egypt.
- Department of Information Technology, Majmaah University, 11952, Majmaah, Saudi Arabia.
| | - Xiangrong Liu
- Department of Computer Science and Technology, Xiamen University, Xiamen, 361005, China
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Xue Y, Friedl V, Ding H, Wong CK, Stuart JM. Single-cell signatures identify microenvironment factors in tumors associated with patient outcomes. CELL REPORTS METHODS 2024; 4:100799. [PMID: 38889686 DOI: 10.1016/j.crmeth.2024.100799] [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: 11/09/2023] [Revised: 04/30/2024] [Accepted: 05/21/2024] [Indexed: 06/20/2024]
Abstract
The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.
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Affiliation(s)
- Yuanqing Xue
- UC Santa Cruz Department, Biomolecular Engineering, Genomics Institute, Santa Cruz, CA, USA
| | - Verena Friedl
- UC Santa Cruz Department, Biomolecular Engineering, Genomics Institute, Santa Cruz, CA, USA
| | - Hongxu Ding
- UC Santa Cruz Department, Biomolecular Engineering, Genomics Institute, Santa Cruz, CA, USA
| | - Christopher K Wong
- UC Santa Cruz Department, Biomolecular Engineering, Genomics Institute, Santa Cruz, CA, USA
| | - Joshua M Stuart
- UC Santa Cruz Department, Biomolecular Engineering, Genomics Institute, Santa Cruz, CA, USA.
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Hwang H, Jeon H, Yeo N, Baek D. Big data and deep learning for RNA biology. Exp Mol Med 2024:10.1038/s12276-024-01243-w. [PMID: 38871816 DOI: 10.1038/s12276-024-01243-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 06/15/2024] Open
Abstract
The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.
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Affiliation(s)
- Hyeonseo Hwang
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Hyeonseong Jeon
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
- Genome4me Inc., Seoul, Republic of Korea
| | - Nagyeong Yeo
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Daehyun Baek
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea.
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
- Genome4me Inc., Seoul, Republic of Korea.
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Ahmad F, Muhmood T. Clinical translation of nanomedicine with integrated digital medicine and machine learning interventions. Colloids Surf B Biointerfaces 2024; 241:114041. [PMID: 38897022 DOI: 10.1016/j.colsurfb.2024.114041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 06/11/2024] [Accepted: 06/13/2024] [Indexed: 06/21/2024]
Abstract
Nanomaterials based therapeutics transform the ways of disease prevention, diagnosis and treatment with increasing sophistications in nanotechnology at a breakneck pace, but very few could reach to the clinic due to inconsistencies in preclinical studies followed by regulatory hinderances. To tackle this, integrating the nanomedicine discovery with digital medicine provide technologies as tools of specific biological activity measurement. Hence, overcome the redundancies in nanomedicine discovery by the on-site data acquisition and analytics through integrating intelligent sensors and artificial intelligence (AI) or machine learning (ML). Integrated AI/ML wearable sensors directly gather clinically relevant biochemical information from the subject's body and process data for physicians to make right clinical decision(s) in a time and cost-effective way. This review summarizes insights and recommend the infusion of actionable big data computation enabled sensors in burgeoning field of nanomedicine at academia, research institutes, and pharmaceutical industries, with a potential of clinical translation. Furthermore, many blind spots are present in modern clinically relevant computation, one of which could prevent ML-guided low-cost new nanomedicine development from being successfully translated into the clinic was also discussed.
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Affiliation(s)
- Farooq Ahmad
- State Key Laboratory of Chemistry and Utilization of Carbon Based Energy Resources, College of Chemistry, Xinjiang University, Urumqi 830017, China.
| | - Tahir Muhmood
- International Iberian Nanotechnology Laboratory (INL), Avenida Mestre José Veiga, Braga 4715-330, Portugal.
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11
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Yoon S, Goh H, Lee PC, Tan HC, Teh MM, Lim DST, Kwee A, Suresh C, Carmody D, Swee DS, Tan SYT, Wong AJW, Choo CHM, Wee Z, Bee YM. Assessing the Utility, Impact, and Adoption Challenges of an Artificial Intelligence-Enabled Prescription Advisory Tool for Type 2 Diabetes Management: Qualitative Study. JMIR Hum Factors 2024; 11:e50939. [PMID: 38869934 PMCID: PMC11211700 DOI: 10.2196/50939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/07/2023] [Accepted: 05/05/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND The clinical management of type 2 diabetes mellitus (T2DM) presents a significant challenge due to the constantly evolving clinical practice guidelines and growing array of drug classes available. Evidence suggests that artificial intelligence (AI)-enabled clinical decision support systems (CDSSs) have proven to be effective in assisting clinicians with informed decision-making. Despite the merits of AI-driven CDSSs, a significant research gap exists concerning the early-stage implementation and adoption of AI-enabled CDSSs in T2DM management. OBJECTIVE This study aimed to explore the perspectives of clinicians on the use and impact of the AI-enabled Prescription Advisory (APA) tool, developed using a multi-institution diabetes registry and implemented in specialist endocrinology clinics, and the challenges to its adoption and application. METHODS We conducted focus group discussions using a semistructured interview guide with purposively selected endocrinologists from a tertiary hospital. The focus group discussions were audio-recorded and transcribed verbatim. Data were thematically analyzed. RESULTS A total of 13 clinicians participated in 4 focus group discussions. Our findings suggest that the APA tool offered several useful features to assist clinicians in effectively managing T2DM. Specifically, clinicians viewed the AI-generated medication alterations as a good knowledge resource in supporting the clinician's decision-making on drug modifications at the point of care, particularly for patients with comorbidities. The complication risk prediction was seen as positively impacting patient care by facilitating early doctor-patient communication and initiating prompt clinical responses. However, the interpretability of the risk scores, concerns about overreliance and automation bias, and issues surrounding accountability and liability hindered the adoption of the APA tool in clinical practice. CONCLUSIONS Although the APA tool holds great potential as a valuable resource for improving patient care, further efforts are required to address clinicians' concerns and improve the tool's acceptance and applicability in relevant contexts.
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Affiliation(s)
- Sungwon Yoon
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Centre for Population Health Research and Implementation, SingHealth Regional Health System, SingHealth, Singapore, Singapore
| | - Hendra Goh
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Phong Ching Lee
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Hong Chang Tan
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Ming Ming Teh
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Dawn Shao Ting Lim
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Ann Kwee
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Chandran Suresh
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - David Carmody
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Du Soon Swee
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Sarah Ying Tse Tan
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Andy Jun-Wei Wong
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | | | - Zongwen Wee
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Yong Mong Bee
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
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12
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Zhao W, Fan L. Time-series representation learning via Time-Frequency Fusion Contrasting. Front Artif Intell 2024; 7:1414352. [PMID: 38933470 PMCID: PMC11199859 DOI: 10.3389/frai.2024.1414352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 05/27/2024] [Indexed: 06/28/2024] Open
Abstract
Time series is a typical data type in numerous domains; however, labeling large amounts of time series data can be costly and time-consuming. Learning effective representation from unlabeled time series data is a challenging task. Contrastive learning stands out as a promising method to acquire representations of unlabeled time series data. Therefore, we propose a self-supervised time-series representation learning framework via Time-Frequency Fusion Contrasting (TF-FC) to learn time-series representation from unlabeled data. Specifically, TF-FC combines time-domain augmentation with frequency-domain augmentation to generate the diverse samples. For time-domain augmentation, the raw time series data pass through the time-domain augmentation bank (such as jitter, scaling, permutation, and masking) and get time-domain augmentation data. For frequency-domain augmentation, first, the raw time series undergoes conversion into frequency domain data following Fast Fourier Transform (FFT) analysis. Then, the frequency data passes through the frequency-domain augmentation bank (such as low pass filter, remove frequency, add frequency, and phase shift) and gets frequency-domain augmentation data. The fusion method of time-domain augmentation data and frequency-domain augmentation data is kernel PCA, which is useful for extracting nonlinear features in high-dimensional spaces. By capturing both the time and frequency domains of the time series, the proposed approach is able to extract more informative features from the data, enhancing the model's capacity to distinguish between different time series. To verify the effectiveness of the TF-FC method, we conducted experiments on four time series domain datasets (i.e., SleepEEG, HAR, Gesture, and Epilepsy). Experimental results show that TF-FC significantly improves in recognition accuracy compared with other SOTA methods.
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Affiliation(s)
- Wenbo Zhao
- International School, Beijing University of Posts and Telecommunications, Beijing, China
| | - Ling Fan
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
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13
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Marinov GK, Ramalingam V, Greenleaf WJ, Kundaje A. An updated compendium and reevaluation of the evidence for nuclear transcription factor occupancy over the mitochondrial genome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.04.597442. [PMID: 38895386 PMCID: PMC11185660 DOI: 10.1101/2024.06.04.597442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
In most eukaryotes, mitochondrial organelles contain their own genome, usually circular, which is the remnant of the genome of the ancestral bacterial endosymbiont that gave rise to modern mitochondria. Mitochondrial genomes are dramatically reduced in their gene content due to the process of endosymbiotic gene transfer to the nucleus; as a result most mitochondrial proteins are encoded in the nucleus and imported into mitochondria. This includes the components of the dedicated mitochondrial transcription and replication systems and regulatory factors, which are entirely distinct from the information processing systems in the nucleus. However, since the 1990s several nuclear transcription factors have been reported to act in mitochondria, and previously we identified 8 human and 3 mouse transcription factors (TFs) with strong localized enrichment over the mitochondrial genome using ChIP-seq (Chromatin Immunoprecipitation) datasets from the second phase of the ENCODE (Encyclopedia of DNA Elements) Project Consortium. Here, we analyze the greatly expanded in the intervening decade ENCODE compendium of TF ChIP-seq datasets (a total of 6,153 ChIP experiments for 942 proteins, of which 763 are sequence-specific TFs) combined with interpretative deep learning models of TF occupancy to create a comprehensive compendium of nuclear TFs that show evidence of association with the mitochondrial genome. We find some evidence for chrM occupancy for 50 nuclear TFs and two other proteins, with bZIP TFs emerging as most likely to be playing a role in mitochondria. However, we also observe that in cases where the same TF has been assayed with multiple antibodies and ChIP protocols, evidence for its chrM occupancy is not always reproducible. In the light of these findings, we discuss the evidential criteria for establishing chrM occupancy and reevaluate the overall compendium of putative mitochondrial-acting nuclear TFs.
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Affiliation(s)
- Georgi K Marinov
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | | | - William J Greenleaf
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, California 94305, USA
- Department of Applied Physics, Stanford University, Stanford, California 94305, USA
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
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14
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Alami H, Lehoux P, Papoutsi C, Shaw SE, Fleet R, Fortin JP. Understanding the integration of artificial intelligence in healthcare organisations and systems through the NASSS framework: a qualitative study in a leading Canadian academic centre. BMC Health Serv Res 2024; 24:701. [PMID: 38831298 PMCID: PMC11149257 DOI: 10.1186/s12913-024-11112-x] [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: 02/03/2023] [Accepted: 05/14/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) technologies are expected to "revolutionise" healthcare. However, despite their promises, their integration within healthcare organisations and systems remains limited. The objective of this study is to explore and understand the systemic challenges and implications of their integration in a leading Canadian academic hospital. METHODS Semi-structured interviews were conducted with 29 stakeholders concerned by the integration of a large set of AI technologies within the organisation (e.g., managers, clinicians, researchers, patients, technology providers). Data were collected and analysed using the Non-Adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework. RESULTS Among enabling factors and conditions, our findings highlight: a supportive organisational culture and leadership leading to a coherent organisational innovation narrative; mutual trust and transparent communication between senior management and frontline teams; the presence of champions, translators, and boundary spanners for AI able to build bridges and trust; and the capacity to attract technical and clinical talents and expertise. Constraints and barriers include: contrasting definitions of the value of AI technologies and ways to measure such value; lack of real-life and context-based evidence; varying patients' digital and health literacy capacities; misalignments between organisational dynamics, clinical and administrative processes, infrastructures, and AI technologies; lack of funding mechanisms covering the implementation, adaptation, and expertise required; challenges arising from practice change, new expertise development, and professional identities; lack of official professional, reimbursement, and insurance guidelines; lack of pre- and post-market approval legal and governance frameworks; diversity of the business and financing models for AI technologies; and misalignments between investors' priorities and the needs and expectations of healthcare organisations and systems. CONCLUSION Thanks to the multidimensional NASSS framework, this study provides original insights and a detailed learning base for analysing AI technologies in healthcare from a thorough socio-technical perspective. Our findings highlight the importance of considering the complexity characterising healthcare organisations and systems in current efforts to introduce AI technologies within clinical routines. This study adds to the existing literature and can inform decision-making towards a judicious, responsible, and sustainable integration of these technologies in healthcare organisations and systems.
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Affiliation(s)
- Hassane Alami
- Department of Health Management, Evaluation and Policy, School of Public Health, University of Montreal, P.O. Box 6128, Branch Centre-Ville, Montreal, QC, H3C 3J7, Canada.
- Center for Public Health Research of the University of Montreal, Montreal, QC, Canada.
- Institute for Data Valorization (IVADO), Montreal, QC, Canada.
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
| | - Pascale Lehoux
- Department of Health Management, Evaluation and Policy, School of Public Health, University of Montreal, P.O. Box 6128, Branch Centre-Ville, Montreal, QC, H3C 3J7, Canada
- Center for Public Health Research of the University of Montreal, Montreal, QC, Canada
| | - Chrysanthi Papoutsi
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Sara E Shaw
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Richard Fleet
- Faculty of Medicine, Laval University, Quebec, QC, Canada
- VITAM Research Centre on Sustainable Health, Faculty of Medicine, Laval University, Quebec, QC, Canada
| | - Jean-Paul Fortin
- Faculty of Medicine, Laval University, Quebec, QC, Canada
- VITAM Research Centre on Sustainable Health, Faculty of Medicine, Laval University, Quebec, QC, Canada
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15
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Rivero-Garcia I, Torres M, Sánchez-Cabo F. Deep generative models in single-cell omics. Comput Biol Med 2024; 176:108561. [PMID: 38749321 DOI: 10.1016/j.compbiomed.2024.108561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 04/30/2024] [Accepted: 05/05/2024] [Indexed: 05/31/2024]
Abstract
Deep Generative Models (DGMs) are becoming instrumental for inferring probability distributions inherent to complex processes, such as most questions in biomedical research. For many years, there was a lack of mathematical methods that would allow this inference in the scarce data scenario of biomedical research. The advent of single-cell omics has finally made square the so-called "skinny matrix", allowing to apply mathematical methods already extensively used in other areas. Moreover, it is now possible to integrate data at different molecular levels in thousands or even millions of samples, thanks to the number of single-cell atlases being collaboratively generated. Additionally, DGMs have proven useful in other frequent tasks in single-cell analysis pipelines, from dimensionality reduction, cell type annotation to RNA velocity inference. In spite of its promise, DGMs need to be used with caution in biomedical research, paying special attention to its use to answer the right questions and the definition of appropriate error metrics and validation check points that confirm not only its correct use but also its relevance. All in all, DGMs provide an exciting tool that opens a bright future for the integrative analysis of single-cell -omics to understand health and disease.
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Affiliation(s)
- Inés Rivero-Garcia
- Universidad Politécnica de Madrid, Madrid, 28040, Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, 28029, Spain
| | - Miguel Torres
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, 28029, Spain
| | - Fátima Sánchez-Cabo
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, 28029, Spain.
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Thribhuvan Reddy D, Grewal I, García Pinzon LF, Latchireddy B, Goraya S, Ali Alansari B, Gadwal A. The Role of Artificial Intelligence in Healthcare: Enhancing Coronary Computed Tomography Angiography for Coronary Artery Disease Management. Cureus 2024; 16:e61523. [PMID: 38957241 PMCID: PMC11218716 DOI: 10.7759/cureus.61523] [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] [Accepted: 06/02/2024] [Indexed: 07/04/2024] Open
Abstract
This review aims to explore the potential of artificial intelligence (AI) in coronary CT angiography (CCTA), a key tool for diagnosing coronary artery disease (CAD). Because CAD is still a major cause of death worldwide, effective and accurate diagnostic methods are required to identify and manage the condition. CCTA certainly is a noninvasive alternative for diagnosing CAD, but it requires a large amount of data as input. We intend to discuss the idea of incorporating AI into CCTA, which enhances its diagnostic accuracy and operational efficiency. Using such AI technologies as machine learning (ML) and deep learning (DL) tools, CCTA images are automated to perfection and the analysis is significantly refined. It enables the characterization of a plaque, assesses the severity of the stenosis, and makes more accurate risk stratifications than traditional methods, with pinpoint accuracy. Automating routine tasks through AI-driven CCTA will reduce the radiologists' workload considerably, which is a standard benefit of such technologies. More importantly, it would enable radiologists to allocate more time and expertise to complex cases, thereby improving overall patient care. However, the field of AI in CCTA is not without its challenges, which include data protection, algorithm transparency, as well as criteria for standardization encoding. Despite such obstacles, it appears that the integration of AI technology into CCTA in the future holds great promise for keeping CAD itself in check, thereby aiding the fight against this disease and begetting better clinical outcomes and more optimized modes of healthcare. Future research on AI algorithms for CCTA, making ethical use of AI, and thereby overcoming the technical and clinical barriers to widespread adoption of this new tool, will hopefully pave the way for profound AI-driven transformations in healthcare.
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Affiliation(s)
| | - Inayat Grewal
- Department of Medicine, Government Medical College and Hospital, Chandigarh, IND
| | | | | | - Simran Goraya
- Department of Medicine, Kharkiv National Medical University, Kharkiv, UKR
| | | | - Aishwarya Gadwal
- Department of Radiodiagnosis, St. John's Medical College and Hospital, Bengaluru, IND
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Meier TA, Refahi MS, Hearne G, Restifo DS, Munoz-Acuna R, Rosen GL, Woloszynek S. The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain. Curr Pain Headache Rep 2024:10.1007/s11916-024-01264-0. [PMID: 38822995 DOI: 10.1007/s11916-024-01264-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2024] [Indexed: 06/03/2024]
Abstract
PURPOSE OF REVIEW This review aims to explore the interface between artificial intelligence (AI) and chronic pain, seeking to identify areas of focus for enhancing current treatments and yielding novel therapies. RECENT FINDINGS In the United States, the prevalence of chronic pain is estimated to be upwards of 40%. Its impact extends to increased healthcare costs, reduced economic productivity, and strain on healthcare resources. Addressing this condition is particularly challenging due to its complexity and the significant variability in how patients respond to treatment. Current options often struggle to provide long-term relief, with their benefits rarely outweighing the risks, such as dependency or other side effects. Currently, AI has impacted four key areas of chronic pain treatment and research: (1) predicting outcomes based on clinical information; (2) extracting features from text, specifically clinical notes; (3) modeling 'omic data to identify meaningful patient subgroups with potential for personalized treatments and improved understanding of disease processes; and (4) disentangling complex neuronal signals responsible for pain, which current therapies attempt to modulate. As AI advances, leveraging state-of-the-art architectures will be essential for improving chronic pain treatment. Current efforts aim to extract meaningful representations from complex data, paving the way for personalized medicine. The identification of unique patient subgroups should reveal targets for tailored chronic pain treatments. Moreover, enhancing current treatment approaches is achievable by gaining a more profound understanding of patient physiology and responses. This can be realized by leveraging AI on the increasing volume of data linked to chronic pain.
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Affiliation(s)
| | - Mohammad S Refahi
- Ecological and Evolutionary Signal-Processing and Informatics (EESI) Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | - Gavin Hearne
- Ecological and Evolutionary Signal-Processing and Informatics (EESI) Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | | | - Ricardo Munoz-Acuna
- Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Gail L Rosen
- Ecological and Evolutionary Signal-Processing and Informatics (EESI) Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | - Stephen Woloszynek
- Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
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18
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Ge S, Chen J, Wang W, Zhang LB, Teng Y, Yang C, Wang H, Tao Y, Chen Z, Li R, Niu Y, Zuo C, Tan L. Predicting who has delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using machine learning approach: a multicenter, retrospective cohort study. BMC Neurol 2024; 24:177. [PMID: 38802769 PMCID: PMC11129362 DOI: 10.1186/s12883-024-03630-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 04/08/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the early identification of clinical outcomes. This study aimed to construct and compare the ability of different ML models to predict DCI after aSAH. Then, we identified and analyzed the essential risk of DCI occurrence by preoperative clinical scores and postoperative laboratory test results. METHODS This was a multicenter, retrospective cohort study. A total of 1039 post-operation patients with aSAH were finally included from three hospitals in China. The training group contained 919 patients, and the test group comprised 120 patients. We used five popular machine-learning algorithms to construct the models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and f1 score were used to evaluate and compare the five models. Finally, we performed a Shapley Additive exPlanations analysis for the model with the best performance and significance analysis for each feature. RESULTS A total of 239 patients with aSAH (23.003%) developed DCI after the operation. Our results showed that in the test cohort, Random Forest (RF) had an AUC of 0.79, which was better than other models. The five most important features for predicting DCI in the RF model were the admitted modified Rankin Scale, D-Dimer, intracranial parenchymal hematoma, neutrophil/lymphocyte ratio, and Fisher score. Interestingly, clamping or embolization for the aneurysm treatment was the fourth button-down risk factor in the ML model. CONCLUSIONS In this multicenter study, we compared five ML methods, among which RF performed the best in DCI prediction. In addition, the essential risks were identified to help clinicians monitor the patients at high risk for DCI more precisely and facilitate timely intervention.
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Affiliation(s)
- Sihan Ge
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Junxin Chen
- School of Software, Dalian University of Technology, Dalian, China
| | - Wei Wang
- Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing, Artificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen, China
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Li-Bo Zhang
- Department of Radiology, General Hospital of the Northern Theater of the Chinese People's Liberation Army, Shenyang, China
| | - Yue Teng
- Emergency Department, General Hospital of the Northern Theater of the Chinese People's Liberation Army, Shenyang, China
| | - Cheng Yang
- Department of Neurosurgery, Southwest Hospital, Army Medical University, (Third Military Medical University), Chongqing, China
| | - Hao Wang
- Department of Neurosurgery, Daping Hospital, Army Medical University, (Third Military Medical University), Chongqing, China
| | - Yihao Tao
- Department of Neurosurgery, the Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Zhi Chen
- Department of Neurosurgery, Southwest Hospital, Army Medical University, (Third Military Medical University), Chongqing, China
| | - Ronghao Li
- Department of Basic Medicine, Army Medical University, Chongqing, China
| | - Yin Niu
- Department of Neurosurgery, Southwest Hospital, Army Medical University, (Third Military Medical University), Chongqing, China.
| | - Chenghai Zuo
- Department of Neurosurgery, Southwest Hospital, Army Medical University, (Third Military Medical University), Chongqing, China.
| | - Liang Tan
- Department of Critical Care Medicine, Southwest Hospital, Army Medical University, (Third Military Medical University), Chongqing, China.
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Jardim LL, Schieber TA, Santana MP, Cerqueira MH, Lorenzato CS, Franco VKB, Zuccherato LW, da Silva Santos BA, Chaves DG, Ravetti MG, Rezende SM. Prediction of inhibitor development in previously untreated and minimally treated children with severe and moderately severe hemophilia A using a machine-learning network. J Thromb Haemost 2024:S1538-7836(24)00303-9. [PMID: 38810700 DOI: 10.1016/j.jtha.2024.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/02/2024] [Accepted: 05/12/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND Prediction of inhibitor development in patients with hemophilia A (HA) remains a challenge. OBJECTIVES To construct a predictive model for inhibitor development in HA using a network of clinical variables and biomarkers based on the individual similarity network. METHODS Previously untreated and minimally treated children with severe/moderately severe HA, participants of the HEMFIL Cohort Study, were followed up until reaching 75 exposure days (EDs) without inhibitor (INH-) or upon inhibitor development (INH+). Clinical data and biological samples were collected before the start of factor (F)VIII replacement (T0). A predictive model (HemfilNET) was built to compare the networks and potential global topological differences between INH- and INH+ at T0, considering the network robustness. For validation, the "leave-one-out" cross-validation technique was employed. Accuracy, precision, recall, and F1-score were used as evaluation metrics for the machine-learning model. RESULTS We included 95 children with HA (CHA), of whom 31 (33%) developed inhibitors. The algorithm, featuring 37 variables, identified distinct patterns of networks at T0 for INH+ and INH-. The accuracy of the model was 74.2% for CHA INH+ and 98.4% for INH-. By focusing the analysis on CHA with high-risk F8 mutations for inhibitor development, the accuracy in identifying CHA INH+ increased to 82.1%. CONCLUSION Our machine-learning algorithm demonstrated an overall accuracy of 90.5% for predicting inhibitor development in CHA, which further improved when restricting the analysis to CHA with a high-risk F8 genotype. However, our model requires validation in other cohorts. Yet, missing data for some variables hindered more precise predictions.
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Affiliation(s)
- Letícia Lemos Jardim
- Instituto René Rachou (Fiocruz Minas), Belo Horizonte, Minas Gerais, Brazil; Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, the Netherlands
| | - Tiago A Schieber
- Faculdade de Ciências Econômicas, School of Economics, Universidade Federal de Minas Gerais, Brazil
| | | | | | | | | | | | | | | | - Martín Gomez Ravetti
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Suely Meireles Rezende
- Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
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Alsulimani A, Akhter N, Jameela F, Ashgar RI, Jawed A, Hassani MA, Dar SA. The Impact of Artificial Intelligence on Microbial Diagnosis. Microorganisms 2024; 12:1051. [PMID: 38930432 PMCID: PMC11205376 DOI: 10.3390/microorganisms12061051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.
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Affiliation(s)
- Ahmad Alsulimani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Naseem Akhter
- Department of Biology, Arizona State University, Lake Havasu City, AZ 86403, USA;
| | - Fatima Jameela
- Modern American Dental Clinic, West Warren Avenue, Dearborn, MI 48126, USA;
| | - Rnda I. Ashgar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Arshad Jawed
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Mohammed Ahmed Hassani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Sajad Ahmad Dar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
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Sun G, Cai L, Yan X, Nie W, Liu X, Xu J, Zou X. A prediction model based on digital breast pathology image information. PLoS One 2024; 19:e0294923. [PMID: 38758814 PMCID: PMC11101065 DOI: 10.1371/journal.pone.0294923] [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: 03/23/2023] [Accepted: 11/11/2023] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND The workload of breast cancer pathological diagnosis is very heavy. The purpose of this study is to establish a nomogram model based on pathological images to predict the benign and malignant nature of breast diseases and to validate its predictive performance. METHODS In retrospect, a total of 2,723 H&E-stained pathological images were collected from 1,474 patients at Qingdao Central Hospital between 2019 and 2022. The dataset consisted of 509 benign tumor images (adenosis and fibroadenoma) and 2,214 malignant tumor images (infiltrating ductal carcinoma). The images were divided into a training set (1,907) and a validation set (816). Python3.7 was used to extract the values of the R channel, G channel, B channel, and one-dimensional information entropy from all images. Multivariable logistic regression was used to select variables and establish the breast tissue pathological image prediction model. RESULTS The R channel value, B channel value, and one-dimensional information entropy of the images were identified as independent predictive factors for the classification of benign and malignant pathological images (P < 0.05). The area under the curve (AUC) of the nomogram model in the training set was 0.889 (95% CI: 0.869, 0.909), and the AUC in the validation set was 0.838 (95% CI: 0.7980.877). The calibration curve results showed that the calibration curve of this nomogram model was close to the ideal curve. The decision curve results indicated that the predictive model curve had a high value for auxiliary diagnosis. CONCLUSION The nomogram model for the prediction of benign and malignant breast diseases based on pathological images demonstrates good predictive performance. This model can assist in the diagnosis of breast tissue pathological images.
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Affiliation(s)
- Guoxin Sun
- School of Clinical Medicine, Qingdao University, Qingdao, China
| | - Liying Cai
- College of Nursing and Rehabilitation, North China University of Science and Technology, Tangshan City, China
| | - Xiong Yan
- Department of Pathology, Qingdao Central Hospital, Qingdao, China
| | - Weihong Nie
- School of Clinical Medicine, Qingdao University, Qingdao, China
| | - Xin Liu
- School of Clinical Medicine, Qingdao University, Qingdao, China
| | - Jing Xu
- Department of Pathology, Qingdao Central Hospital, Qingdao, China
| | - Xiao Zou
- Department of Breast Surgery, Xiangdong Hospital Affiliated to Hunan Normal University, Hunan, China
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22
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Rennie S. Deep Learning for Elucidating Modifications to RNA-Status and Challenges Ahead. Genes (Basel) 2024; 15:629. [PMID: 38790258 PMCID: PMC11121098 DOI: 10.3390/genes15050629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 05/11/2024] [Accepted: 05/11/2024] [Indexed: 05/26/2024] Open
Abstract
RNA-binding proteins and chemical modifications to RNA play vital roles in the co- and post-transcriptional regulation of genes. In order to fully decipher their biological roles, it is an essential task to catalogue their precise target locations along with their preferred contexts and sequence-based determinants. Recently, deep learning approaches have significantly advanced in this field. These methods can predict the presence or absence of modification at specific genomic regions based on diverse features, particularly sequence and secondary structure, allowing us to decipher the highly non-linear sequence patterns and structures that underlie site preferences. This article provides an overview of how deep learning is being applied to this area, with a particular focus on the problem of mRNA-RBP binding, while also considering other types of chemical modification to RNA. It discusses how different types of model can handle sequence-based and/or secondary-structure-based inputs, the process of model training, including choice of negative regions and separating sets for testing and training, and offers recommendations for developing biologically relevant models. Finally, it highlights four key areas that are crucial for advancing the field.
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Affiliation(s)
- Sarah Rennie
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark
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23
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Burger PB, Hu X, Balabin I, Muller M, Stanley M, Joubert F, Kaiser TM. FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology. J Chem Inf Model 2024; 64:3812-3825. [PMID: 38651738 PMCID: PMC11094716 DOI: 10.1021/acs.jcim.4c00071] [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: 01/12/2024] [Revised: 04/01/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024]
Abstract
In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist's toolbox to enhance the efficiency of both hit optimization and candidate design. Both computational methods come with their own set of limitations, and they are often used independently of each other. ML's capability to screen extensive compound libraries expediently is tempered by its reliance on quality data, which can be scarce especially during early-stage optimization. Contrarily, physics-based approaches like free energy perturbation (FEP) are frequently constrained by low throughput and high cost by comparison; however, physics-based methods are capable of making highly accurate binding affinity predictions. In this study, we harnessed the strength of FEP to overcome data paucity in ML by generating virtual activity data sets which then inform the training of algorithms. Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. Throughout the paper, we emphasize key mechanistic considerations that must be taken into account when aiming to augment data sets and lay the groundwork for successful implementation. Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. We believe that the physics-based augmentation of ML will significantly benefit drug discovery, as these techniques continue to evolve.
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Affiliation(s)
- Pieter B. Burger
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
| | - Xiaohu Hu
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Ilya Balabin
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
| | - Morné Muller
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
| | - Megan Stanley
- Microsoft
Research AI4Science, 21 Station Road, Cambridge CB1 2FB, U.K.
| | - Fourie Joubert
- Centre
for Bioinformatics and Computational Biology, Department of Biochemistry,
Genetics and Microbiology, University of
Pretoria, Pretoria 0001, South Africa
| | - Thomas M. Kaiser
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
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24
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Pang W, Chen M, Qin Y. Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning. BMC Bioinformatics 2024; 25:182. [PMID: 38724920 PMCID: PMC11080240 DOI: 10.1186/s12859-024-05669-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 01/22/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND The prediction of drug sensitivity plays a crucial role in improving the therapeutic effect of drugs. However, testing the effectiveness of drugs is challenging due to the complex mechanism of drug reactions and the lack of interpretability in most machine learning and deep learning methods. Therefore, it is imperative to establish an interpretable model that receives various cell line and drug feature data to learn drug response mechanisms and achieve stable predictions between available datasets. RESULTS This study proposes a new and interpretable deep learning model, DrugGene, which integrates gene expression, gene mutation, gene copy number variation of cancer cells, and chemical characteristics of anticancer drugs to predict their sensitivity. This model comprises two different branches of neural networks, where the first involves a hierarchical structure of biological subsystems that uses the biological processes of human cells to form a visual neural network (VNN) and an interpretable deep neural network for human cancer cells. DrugGene receives genotype input from the cell line and detects changes in the subsystem states. We also employ a traditional artificial neural network (ANN) to capture the chemical structural features of drugs. DrugGene generates final drug response predictions by combining VNN and ANN and integrating their outputs into a fully connected layer. The experimental results using drug sensitivity data extracted from the Cancer Drug Sensitivity Genome Database and the Cancer Treatment Response Portal v2 reveal that the proposed model is better than existing prediction methods. Therefore, our model achieves higher accuracy, learns the reaction mechanisms between anticancer drugs and cell lines from various features, and interprets the model's predicted results. CONCLUSIONS Our method utilizes biological pathways to construct neural networks, which can use genotypes to monitor changes in the state of network subsystems, thereby interpreting the prediction results in the model and achieving satisfactory prediction accuracy. This will help explore new directions in cancer treatment. More available code resources can be downloaded for free from GitHub ( https://github.com/pangweixiong/DrugGene ).
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Affiliation(s)
- Weixiong Pang
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China
- Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China
| | - Ming Chen
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China
- Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China
| | - Yufang Qin
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China.
- Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China.
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25
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Yan JK. A methodological showcase: utilizing minimal clinical parameters for early-stage mortality risk assessment in COVID-19-positive patients. PeerJ Comput Sci 2024; 10:e2017. [PMID: 38855224 PMCID: PMC11157615 DOI: 10.7717/peerj-cs.2017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 04/03/2024] [Indexed: 06/11/2024]
Abstract
The scarcity of data is likely to have a negative effect on machine learning (ML). Yet, in the health sciences, data is diverse and can be costly to acquire. Therefore, it is critical to develop methods that can reach similar accuracy with minimal clinical features. This study explores a methodology that aims to build a model using minimal clinical parameters to reach comparable performance to a model trained with a more extensive list of parameters. To develop this methodology, a dataset of over 1,000 COVID-19-positive patients was used. A machine learning model was built with over 90% accuracy when combining 24 clinical parameters using Random Forest (RF) and logistic regression. Furthermore, to obtain minimal clinical parameters to predict the mortality of COVID-19 patients, the features were weighted using both Shapley values and RF feature importance to get the most important factors. The six most highly weighted features that could produce the highest performance metrics were combined for the final model. The accuracy of the final model, which used a combination of six features, is 90% with the random forest classifier and 91% with the logistic regression model. This performance is close to that of a model using 24 combined features (92%), suggesting that highly weighted minimal clinical parameters can be used to reach similar performance. The six clinical parameters identified here are acute kidney injury, glucose level, age, troponin, oxygen level, and acute hepatic injury. Among those parameters, acute kidney injury was the highest-weighted feature. Together, a methodology was developed using significantly minimal clinical parameters to reach performance metrics similar to a model trained with a large dataset, highlighting a novel approach to address the problems of clinical data collection for machine learning.
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26
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Barea Mendoza JA, Valiente Fernandez M, Pardo Fernandez A, Gómez Álvarez J. Current perspectives on the use of artificial intelligence in critical patient safety. Med Intensiva 2024:S2173-5727(24)00080-8. [PMID: 38677902 DOI: 10.1016/j.medine.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 03/11/2024] [Indexed: 04/29/2024]
Abstract
Intensive Care Units (ICUs) have undergone enhancements in patient safety, and artificial intelligence (AI) emerges as a disruptive technology offering novel opportunities. While the published evidence is limited and presents methodological issues, certain areas show promise, such as decision support systems, detection of adverse events, and prescription error identification. The application of AI in safety may pursue predictive or diagnostic objectives. Implementing AI-based systems necessitates procedures to ensure secure assistance, addressing challenges including trust in such systems, biases, data quality, scalability, and ethical and confidentiality considerations. The development and application of AI demand thorough testing, encompassing retrospective data assessments, real-time validation with prospective cohorts, and efficacy demonstration in clinical trials. Algorithmic transparency and explainability are essential, with active involvement of clinical professionals being crucial in the implementation process.
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Affiliation(s)
- Jesús Abelardo Barea Mendoza
- UCI de Trauma y Emergencias. Servicio de Medicina Intensiva. Hospital Universitario 12 de Octubre. Instituto de Investigación Hospital 12 de Octubre, Spain.
| | - Marcos Valiente Fernandez
- UCI de Trauma y Emergencias. Servicio de Medicina Intensiva. Hospital Universitario 12 de Octubre. Instituto de Investigación Hospital 12 de Octubre, Spain
| | | | - Josep Gómez Álvarez
- Hospital Universitari de Tarragona Joan XXIII. Universitat Rovira i Virgili. Institut d'Investigació Sanitària Pere i Virgili, Tarragona, Spain
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27
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Meimetis N, Lauffenburger DA, Nilsson A. Inference of drug off-target effects on cellular signaling using interactome-based deep learning. iScience 2024; 27:109509. [PMID: 38591003 PMCID: PMC11000001 DOI: 10.1016/j.isci.2024.109509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 02/04/2024] [Accepted: 03/13/2024] [Indexed: 04/10/2024] Open
Abstract
Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors' activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.
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Affiliation(s)
- Nikolaos Meimetis
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Douglas A. Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Avlant Nilsson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Cell and Molecular Biology, SciLifeLab, Karolinska Institutet, Stockholm, Sweden
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE 41296, Sweden
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28
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Baskaya F, Lemainque T, Klinkhammer B, Koletnik S, von Stillfried S, Talbot SR, Boor P, Schulz V, Lederle W, Kiessling F. Pathophysiologic Mapping of Chronic Liver Diseases With Longitudinal Multiparametric MRI in Animal Models. Invest Radiol 2024:00004424-990000000-00209. [PMID: 38598653 DOI: 10.1097/rli.0000000000001075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
OBJECTIVES Chronic liver diseases (CLDs) have diverse etiologies. To better classify CLDs, we explored the ability of longitudinal multiparametric MRI (magnetic resonance imaging) in depicting alterations in liver morphology, inflammation, and hepatocyte and macrophage activity in murine high-fat diet (HFD)- and carbon tetrachloride (CCl4)-induced CLD models. MATERIALS AND METHODS Mice were either untreated, fed an HFD for 24 weeks, or injected with CCl4 for 8 weeks. Longitudinal multiparametric MRI was performed every 4 weeks using a 7 T MRI scanner, including T1/T2 relaxometry, morphological T1/T2-weighted imaging, and fat-selective imaging. Diffusion-weighted imaging was applied to assess fibrotic remodeling and T1-weighted and T2*-weighted dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI using gadoxetic acid and ferucarbotran to target hepatocytes and the mononuclear phagocyte system, respectively. Imaging data were associated with histopathological and serological analyses. Principal component analysis and clustering were used to reveal underlying disease patterns. RESULTS The MRI parameters significantly correlated with histologically confirmed steatosis, fibrosis, and liver damage, with varying importance. No single MRI parameter exclusively correlated with 1 pathophysiological feature, underscoring the necessity for using parameter patterns. Clustering revealed early-stage, model-specific patterns. Although the HFD model exhibited pronounced liver fat content and fibrosis, the CCl4 model indicated reduced liver fat content and impaired hepatocyte and macrophage function. In both models, MRI biomarkers of inflammation were elevated. CONCLUSIONS Multiparametric MRI patterns can be assigned to pathophysiological processes and used for murine CLD classification and progression tracking. These MRI biomarker patterns can directly be explored clinically to improve early CLD detection and differentiation and to refine treatments.
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Affiliation(s)
- Ferhan Baskaya
- From the Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany (F.B., T.L., S.K., V.S., W.L., F.K.); Department for Diagnostic and Interventional Radiology, RWTH Aachen University, Aachen, Germany (T.L.); Institute of Pathology, RWTH Aachen University, Aachen, Germany (B.K., S.S., P.B.); and Institute for Laboratory Animal Science, Hannover Medical School, Hannover, Germany (S.R.T.)
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29
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Capponi S, Wang S. AI in cellular engineering and reprogramming. Biophys J 2024:S0006-3495(24)00245-5. [PMID: 38576162 DOI: 10.1016/j.bpj.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/19/2024] [Accepted: 04/01/2024] [Indexed: 04/06/2024] Open
Abstract
During the last decade, artificial intelligence (AI) has increasingly been applied in biophysics and related fields, including cellular engineering and reprogramming, offering novel approaches to understand, manipulate, and control cellular function. The potential of AI lies in its ability to analyze complex datasets and generate predictive models. AI algorithms can process large amounts of data from single-cell genomics and multiomic technologies, allowing researchers to gain mechanistic insights into the control of cell identity and function. By integrating and interpreting these complex datasets, AI can help identify key molecular events and regulatory pathways involved in cellular reprogramming. This knowledge can inform the design of precision engineering strategies, such as the development of new transcription factor and signaling molecule cocktails, to manipulate cell identity and drive authentic cell fate across lineage boundaries. Furthermore, when used in combination with computational methods, AI can accelerate and improve the analysis and understanding of the intricate relationships between genes, proteins, and cellular processes. In this review article, we explore the current state of AI applications in biophysics with a specific focus on cellular engineering and reprogramming. Then, we showcase a couple of recent applications where we combined machine learning with experimental and computational techniques. Finally, we briefly discuss the challenges and prospects of AI in cellular engineering and reprogramming, emphasizing the potential of these technologies to revolutionize our ability to engineer cells for a variety of applications, from disease modeling and drug discovery to regenerative medicine and biomanufacturing.
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Affiliation(s)
- Sara Capponi
- IBM Almaden Research Center, San Jose, California; Center for Cellular Construction, San Francisco, California.
| | - Shangying Wang
- Bay Area Institute of Science, Altos Labs, Redwood City, California.
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30
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Tang X, Huo M, Chen Y, Huang H, Qin S, Luo J, Qin Z, Jiang X, Liu Y, Duan X, Wang R, Chen L, Li H, Fan N, He Z, He X, Shen B, Li SC, Song X. A novel deep generative model for mRNA vaccine development: Designing 5' UTRs with N1-methyl-pseudouridine modification. Acta Pharm Sin B 2024; 14:1814-1826. [PMID: 38572113 PMCID: PMC10985129 DOI: 10.1016/j.apsb.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/26/2023] [Accepted: 11/01/2023] [Indexed: 04/05/2024] Open
Abstract
Efficient translation mediated by the 5' untranslated region (5' UTR) is essential for the robust efficacy of mRNA vaccines. However, the N1-methyl-pseudouridine (m1Ψ) modification of mRNA can impact the translation efficiency of the 5' UTR. We discovered that the optimal 5' UTR for m1Ψ-modified mRNA (m1Ψ-5' UTR) differs significantly from its unmodified counterpart, highlighting the need for a specialized tool for designing m1Ψ-5' UTRs rather than directly utilizing high-expression endogenous gene 5' UTRs. In response, we developed a novel machine learning-based tool, Smart5UTR, which employs a deep generative model to identify superior m1Ψ-5' UTRs in silico. The tailored loss function and network architecture enable Smart5UTR to overcome limitations inherent in existing models. As a result, Smart5UTR can successfully design superior 5' UTRs, greatly benefiting mRNA vaccine development. Notably, Smart5UTR-designed superior 5' UTRs significantly enhanced antibody titers induced by COVID-19 mRNA vaccines against the Delta and Omicron variants of SARS-CoV-2, surpassing the performance of vaccines using high-expression endogenous gene 5' UTRs.
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Affiliation(s)
- Xiaoshan Tang
- Institute of Systems Genetics, Department of Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610000, China
| | - Miaozhe Huo
- Department of Computer Science, City University of Hong Kong, Hong Kong 99907, China
| | - Yuting Chen
- Institute of Systems Genetics, Department of Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610000, China
| | - Hai Huang
- Institute of Systems Genetics, Department of Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610000, China
| | - Shugang Qin
- Institute of Systems Genetics, Department of Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610000, China
| | - Jiaqi Luo
- Department of Computer Science, City University of Hong Kong, Hong Kong 99907, China
| | - Zeyi Qin
- Department of Biology, Brandeis University, Boston, MA 02453, USA
| | - Xin Jiang
- Institute of Systems Genetics, Department of Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610000, China
| | - Yongmei Liu
- Institute of Systems Genetics, Department of Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610000, China
| | - Xing Duan
- Institute of Systems Genetics, Department of Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610000, China
| | - Ruohan Wang
- Department of Computer Science, City University of Hong Kong, Hong Kong 99907, China
| | - Lingxi Chen
- Department of Computer Science, City University of Hong Kong, Hong Kong 99907, China
| | - Hao Li
- Institute of Systems Genetics, Department of Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610000, China
| | - Na Fan
- Institute of Systems Genetics, Department of Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610000, China
| | - Zhongshan He
- Institute of Systems Genetics, Department of Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610000, China
| | - Xi He
- Institute of Systems Genetics, Department of Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610000, China
| | - Bairong Shen
- Institute of Systems Genetics, Department of Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610000, China
| | - Shuai Cheng Li
- Department of Computer Science, City University of Hong Kong, Hong Kong 99907, China
| | - Xiangrong Song
- Institute of Systems Genetics, Department of Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610000, China
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31
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Singh H, Nim DK, Randhawa AS, Ahluwalia S. Integrating clinical pharmacology and artificial intelligence: potential benefits, challenges, and role of clinical pharmacologists. Expert Rev Clin Pharmacol 2024; 17:381-391. [PMID: 38340012 DOI: 10.1080/17512433.2024.2317963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 02/08/2024] [Indexed: 02/12/2024]
Abstract
INTRODUCTION The integration of artificial intelligence (AI) into clinical pharmacology could be a potential approach for accelerating drug discovery and development, improving patient care, and streamlining medical research processes. AREAS COVERED We reviewed the current state of AI applications in clinical pharmacology, focusing on drug discovery and development, precision medicine, pharmacovigilance, and other ventures. Key AI applications in clinical pharmacology are examined, including machine learning, natural language processing, deep learning, and reinforcement learning etc. Additionally, the evolving role of clinical pharmacologists, ethical considerations, and challenges in implementing AI in clinical pharmacology are discussed. EXPERT OPINION The AI could be instrumental in accelerating drug discovery, predicting drug safety and efficacy, and optimizing clinical trial designs. It can play a vital role in precision medicine by helping in personalized drug dosing, treatment selection, and predicting drug response based on genetic, clinical, and environmental factors. The role of AI in pharmacovigilance, such as signal detection and adverse event prediction, is also promising. The collaboration between clinical pharmacologists and AI experts also poses certain ethical and practical challenges. Clinical pharmacologists can be instrumental in shaping the future of AI-driven clinical pharmacology and contribute to the improvement of healthcare systems.
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Affiliation(s)
- Harmanjit Singh
- Department of Pharmacology, Government Medical College & Hospital, Chandigarh, India
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32
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Zhou T, Gu S, Shao F, Li P, Wu Y, Xiong J, Wang B, Zhou C, Gao P, Hua X. Prediction of preeclampsia from retinal fundus images via deep learning in singleton pregnancies: a prospective cohort study. J Hypertens 2024; 42:701-710. [PMID: 38230614 PMCID: PMC10906188 DOI: 10.1097/hjh.0000000000003658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 12/01/2023] [Accepted: 12/30/2023] [Indexed: 01/18/2024]
Abstract
INTRODUCTION Early prediction of preeclampsia (PE) is of universal importance in controlling the disease process. Our study aimed to assess the feasibility of using retinal fundus images to predict preeclampsia via deep learning in singleton pregnancies. METHODS This prospective cohort study was conducted at Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine. Eligible participants included singleton pregnancies who presented for prenatal visits before 14 weeks of gestation from September 1, 2020, to February 1, 2022. Retinal fundus images were obtained using a nonmydriatic digital retinal camera during their initial prenatal visit upon admission before 20 weeks of gestation. In addition, we generated fundus scores, which indicated the predictive value of hypertension, using a hypertension detection model. To evaluate the predictive value of the retinal fundus image-based deep learning algorithm for preeclampsia, we conducted stratified analyses and measured the area under the curve (AUC), sensitivity, and specificity. We then conducted sensitivity analyses for validation. RESULTS Our study analyzed a total of 1138 women, 92 pregnancies developed into hypertension disorders of pregnancy (HDP), including 26 cases of gestational hypertension and 66 cases of preeclampsia. The adjusted odds ratio (aOR) of the fundus scores was 2.582 (95% CI, 1.883-3.616; P < 0.001). Otherwise, in the categories of prepregnancy BMI less than 28.0 and at least 28.0, the aORs were 3.073 (95%CI, 2.265-4.244; P < 0.001) and 5.866 (95% CI, 3.292-11.531; P < 0.001). In the categories of maternal age less than 35.0 and at least 35.0, the aORs were 2.845 (95% CI, 1.854-4.463; P < 0.001) and 2.884 (95% CI, 1.794-4.942; P < 0.001). The AUC of the fundus score combined with risk factors was 0.883 (sensitivity, 0.722; specificity, 0.934; 95% CI, 0.834-0.932) for predicting preeclampsia. CONCLUSION Our study demonstrates that the use of deep learning algorithm-based retinal fundus images offers promising predictive value for the early detection of preeclampsia.
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Affiliation(s)
- Tianfan Zhou
- Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University
| | - Shengyi Gu
- Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University
| | - Feixue Shao
- Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University
| | - Ping Li
- Department of Ophthalmology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University
| | - Yuelin Wu
- Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University
| | | | - Bin Wang
- Beijing Airdoc Technology Co., Ltd., Beijing, China
| | - Chenchen Zhou
- Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University
| | - Peng Gao
- Department of Ophthalmology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University
| | - Xiaolin Hua
- Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University
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Wang Y, Liu A, Yang J, Wang L, Xiong N, Cheng Y, Wu Q. Clinical knowledge-guided deep reinforcement learning for sepsis antibiotic dosing recommendations. Artif Intell Med 2024; 150:102811. [PMID: 38553154 DOI: 10.1016/j.artmed.2024.102811] [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: 06/06/2023] [Revised: 12/27/2023] [Accepted: 02/11/2024] [Indexed: 04/02/2024]
Abstract
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 potentially deviate from medical common sense and leading to underperformance. (Wang et al., 2021). In this paper, we use Deep Q-Network (DQN) to construct a Sepsis Anti-infection DQN (SAI-DQN) model to address the challenge of determining the optimal combination and duration of antibiotics in sepsis treatment. By setting sepsis clinical knowledge as reward functions to guide DQN complying with medical guidelines, we formed personalized treatment recommendations for antibiotic combinations. The results showed that our model had a higher average value for decision-making than clinical decisions. For the test set of patients, our model predicts that 79.07% of patients will achieve a favorable prognosis with the recommended combination of antibiotics. By statistically analyzing decision trajectories and drug action selection, our model was able to provide reasonable medication recommendations that comply with clinical practices. Our model was able to improve patient outcomes by recommending appropriate antibiotic combinations in line with certain clinical knowledge.
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Affiliation(s)
- Yuan Wang
- Tianjin University of Science and Technology, Tianjin, China
| | - Anqi Liu
- Tianjin University of Science and Technology, Tianjin, China
| | - Jucheng Yang
- Tianjin University of Science and Technology, Tianjin, China
| | - Lin Wang
- Tianjin University of Science and Technology, Tianjin, China
| | - Ning Xiong
- Tianjin University of Science and Technology, Tianjin, China
| | - Yisong Cheng
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
| | - Qin Wu
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
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Duman ŞB, Çelik Özen D, Bayrakdar IŞ, Baydar O, Alhaija ESA, Helvacioğlu Yiğit D, Çelik Ö, Jagtap R, Pileggi R, Orhan K. Second mesiobuccal canal segmentation with YOLOv5 architecture using cone beam computed tomography images. Odontology 2024; 112:552-561. [PMID: 37907818 DOI: 10.1007/s10266-023-00864-3] [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: 06/23/2023] [Accepted: 10/03/2023] [Indexed: 11/02/2023]
Abstract
The objective of this study is to use a deep-learning model based on CNN architecture to detect the second mesiobuccal (MB2) canals, which are seen as a variation in maxillary molars root canals. In the current study, 922 axial sections from 153 patients' cone beam computed tomography (CBCT) images were used. The segmentation method was employed to identify the MB2 canals in maxillary molars that had not previously had endodontic treatment. Labeled images were divided into training (80%), validation (10%) and testing (10%) groups. The artificial intelligence (AI) model was trained using the You Only Look Once v5 (YOLOv5x) architecture with 500 epochs and a learning rate of 0.01. Confusion matrix and receiver-operating characteristic (ROC) analysis were used in the statistical evaluation of the results. The sensitivity of the MB2 canal segmentation model was 0.92, the precision was 0.83, and the F1 score value was 0.87. The area under the curve (AUC) in the ROC graph of the model was 0.84. The mAP value at 0.5 inter-over union (IoU) was found as 0.88. The deep-learning algorithm used showed a high success in the detection of the MB2 canal. The success of the endodontic treatment can be increased and clinicians' time can be preserved using the newly created artificial intelligence-based models to identify variations in root canal anatomy before the treatment.
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Affiliation(s)
- Şuayip Burak Duman
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University Malatya, Malatya, Turkey.
| | - Duygu Çelik Özen
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University Malatya, Malatya, Turkey
| | - Ibrahim Şevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Oğuzhan Baydar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ege University İzmir, İzmir, Turkey
| | | | | | - Özer Çelik
- Department of Mathematics-Computer, Faculty of Science, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Rohan Jagtap
- Division of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, Medical Center School of Dentistry, University of Mississippi, Jackson, MS, USA
| | - Roberta Pileggi
- Department of Endodontics, College of Dentistry, University of Florida, Florida, USA
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
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Kumar N, Srivastava R. Deep learning in structural bioinformatics: current applications and future perspectives. Brief Bioinform 2024; 25:bbae042. [PMID: 38701422 PMCID: PMC11066934 DOI: 10.1093/bib/bbae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 01/05/2024] [Accepted: 01/18/2024] [Indexed: 05/05/2024] Open
Abstract
In this review article, we explore the transformative impact of deep learning (DL) on structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by extensive data, accessible toolkits and robust computing resources. As big data continue to advance, DL is poised to become an integral component in healthcare and biology, revolutionizing analytical processes. Our comprehensive review provides detailed insights into DL, featuring specific demonstrations of its notable applications in bioinformatics. We address challenges tailored for DL, spotlight recent successes in structural bioinformatics and present a clear exposition of DL-from basic shallow neural networks to advanced models such as convolution, recurrent, artificial and transformer neural networks. This paper discusses the emerging use of DL for understanding biomolecular structures, anticipating ongoing developments and applications in the realm of structural bioinformatics.
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Affiliation(s)
- Niranjan Kumar
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Rakesh Srivastava
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
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Harihar B, Saravanan KM, Gromiha MM, Selvaraj S. Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design. Mol Biotechnol 2024:10.1007/s12033-024-01119-4. [PMID: 38498284 DOI: 10.1007/s12033-024-01119-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 02/10/2024] [Indexed: 03/20/2024]
Abstract
Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.
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Affiliation(s)
- Balasubramanian Harihar
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu, 620024, India
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India
| | - Konda Mani Saravanan
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu, 620024, India
- Department of Biotechnology, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, 600073, India
| | - Michael M Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India
| | - Samuel Selvaraj
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu, 620024, India.
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Park YJ, Yang GJ, Sohn CB, Park SJ. GPDminer: a tool for extracting named entities and analyzing relations in biological literature. BMC Bioinformatics 2024; 25:101. [PMID: 38448845 PMCID: PMC10916184 DOI: 10.1186/s12859-024-05710-z] [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: 11/08/2023] [Accepted: 02/19/2024] [Indexed: 03/08/2024] Open
Abstract
PURPOSE The expansion of research across various disciplines has led to a substantial increase in published papers and journals, highlighting the necessity for reliable text mining platforms for database construction and knowledge acquisition. This abstract introduces GPDMiner(Gene, Protein, and Disease Miner), a platform designed for the biomedical domain, addressing the challenges posed by the growing volume of academic papers. METHODS GPDMiner is a text mining platform that utilizes advanced information retrieval techniques. It operates by searching PubMed for specific queries, extracting and analyzing information relevant to the biomedical field. This system is designed to discern and illustrate relationships between biomedical entities obtained from automated information extraction. RESULTS The implementation of GPDMiner demonstrates its efficacy in navigating the extensive corpus of biomedical literature. It efficiently retrieves, extracts, and analyzes information, highlighting significant connections between genes, proteins, and diseases. The platform also allows users to save their analytical outcomes in various formats, including Excel and images. CONCLUSION GPDMiner offers a notable additional functionality among the array of text mining tools available for the biomedical field. This tool presents an effective solution for researchers to navigate and extract relevant information from the vast unstructured texts found in biomedical literature, thereby providing distinctive capabilities that set it apart from existing methodologies. Its application is expected to greatly benefit researchers in this domain, enhancing their capacity for knowledge discovery and data management.
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Affiliation(s)
- Yeon-Ji Park
- Department of Electronics and Communications Engineering, Kwangwoon University, 20 Gwangun-ro, Seoul, 01897, Republic of Korea
| | - Geun-Je Yang
- Department of Electronics and Communications Engineering, Kwangwoon University, 20 Gwangun-ro, Seoul, 01897, Republic of Korea
| | - Chae-Bong Sohn
- Department of Electronics and Communications Engineering, Kwangwoon University, 20 Gwangun-ro, Seoul, 01897, Republic of Korea.
| | - Soo Jun Park
- Welfare & Medical ICT Research Department, Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Daejeon, 34129, Republic of Korea.
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Huang ZH, Chen L, Sun Y, Liu Q, Hu P. Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer. J Transl Med 2024; 22:226. [PMID: 38429796 PMCID: PMC10908206 DOI: 10.1186/s12967-024-05018-9] [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: 07/23/2023] [Accepted: 02/22/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Breast Cancer (BC) is a highly heterogeneous and complex disease. Personalized treatment options require the integration of multi-omic data and consideration of phenotypic variability. Radiogenomics aims to merge medical images with genomic measurements but encounter challenges due to unpaired data consisting of imaging, genomic, or clinical outcome data. In this study, we propose the utilization of a well-trained conditional generative adversarial network (cGAN) to address the unpaired data issue in radiogenomic analysis of BC. The generated images will then be used to predict the mutations status of key driver genes and BC subtypes. METHODS We integrated the paired MRI and multi-omic (mRNA gene expression, DNA methylation, and copy number variation) profiles of 61 BC patients from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). To facilitate this integration, we employed a Bayesian Tensor Factorization approach to factorize the multi-omic data into 17 latent features. Subsequently, a cGAN model was trained based on the matched side-view patient MRIs and their corresponding latent features to predict MRIs for BC patients who lack MRIs. Model performance was evaluated by calculating the distance between real and generated images using the Fréchet Inception Distance (FID) metric. BC subtype and mutation status of driver genes were obtained from the cBioPortal platform, where 3 genes were selected based on the number of mutated patients. A convolutional neural network (CNN) was constructed and trained using the generated MRIs for mutation status prediction. Receiver operating characteristic area under curve (ROC-AUC) and precision-recall area under curve (PR-AUC) were used to evaluate the performance of the CNN models for mutation status prediction. Precision, recall and F1 score were used to evaluate the performance of the CNN model in subtype classification. RESULTS The FID of the images from the well-trained cGAN model based on the test set is 1.31. The CNN for TP53, PIK3CA, and CDH1 mutation prediction yielded ROC-AUC values 0.9508, 0.7515, and 0.8136 and PR-AUC are 0.9009, 0.7184, and 0.5007, respectively for the three genes. Multi-class subtype prediction achieved precision, recall and F1 scores of 0.8444, 0.8435 and 0.8336 respectively. The source code and related data implemented the algorithms can be found in the project GitHub at https://github.com/mattthuang/BC_RadiogenomicGAN . CONCLUSION Our study establishes cGAN as a viable tool for generating synthetic BC MRIs for mutation status prediction and subtype classification to better characterize the heterogeneity of BC in patients. The synthetic images also have the potential to significantly augment existing MRI data and circumvent issues surrounding data sharing and patient privacy for future BC machine learning studies.
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Affiliation(s)
- Zi Huai Huang
- Department of Biochemistry, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
| | - Lianghong Chen
- Department of Computer Science, Western University, London, ON, Canada
| | - Yan Sun
- Department of Biochemistry, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
- Department of Computer Science, Western University, London, ON, Canada
| | - Qian Liu
- Department of Applied Computer Science, University of Winnipeg, CH Room 3C08B, 515 Portage Avenue, Winnipeg, MB, R3B 2E9, Canada.
| | - Pingzhao Hu
- Department of Biochemistry, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.
- Department of Computer Science, Western University, London, ON, Canada.
- Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.
- Department of Oncology, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.
- The Children's Health Research Institute, Lawson Health Research Institute, London, ON, Canada.
- Department of Biochemistry, Western University, Siebens Drake Research Institute, SDRI Room 201-203B, 1400 Western Road, London, ON, N6G 2V4, Canada.
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Ma H, Jiang F, Rong Y, Guo Y, Huang J. Toward Robust Self-Training Paradigm for Molecular Prediction Tasks. J Comput Biol 2024; 31:213-228. [PMID: 38531049 DOI: 10.1089/cmb.2023.0187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2024] Open
Abstract
Molecular prediction tasks normally demand a series of professional experiments to label the target molecule, which suffers from the limited labeled data problem. One of the semisupervised learning paradigms, known as self-training, utilizes both labeled and unlabeled data. Specifically, a teacher model is trained using labeled data and produces pseudo labels for unlabeled data. These labeled and pseudo-labeled data are then jointly used to train a student model. However, the pseudo labels generated from the teacher model are generally not sufficiently accurate. Thus, we propose a robust self-training strategy by exploring robust loss function to handle such noisy labels in two paradigms, that is, generic and adaptive. We have conducted experiments on three molecular biology prediction tasks with four backbone models to gradually evaluate the performance of the proposed robust self-training strategy. The results demonstrate that the proposed method enhances prediction performance across all tasks, notably within molecular regression tasks, where there has been an average enhancement of 41.5%. Furthermore, the visualization analysis confirms the superiority of our method. Our proposed robust self-training is a simple yet effective strategy that efficiently improves molecular biology prediction performance. It tackles the labeled data insufficient issue in molecular biology by taking advantage of both labeled and unlabeled data. Moreover, it can be easily embedded with any prediction task, which serves as a universal approach for the bioinformatics community.
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Affiliation(s)
- Hehuan Ma
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas, USA
| | - Feng Jiang
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas, USA
| | - Yu Rong
- Tecent AI Lab, Shenzhen, China
| | - Yuzhi Guo
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas, USA
| | - Junzhou Huang
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas, USA
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Shen B, Li Z, Pan Y, Guo Y, Yin Z, Hu R, Qu J, Liu L. Noninvasive Nonlinear Optical Computational Histology. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308630. [PMID: 38095543 PMCID: PMC10916666 DOI: 10.1002/advs.202308630] [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: 11/11/2023] [Revised: 11/28/2023] [Indexed: 03/07/2024]
Abstract
Cancer remains a global health challenge, demanding early detection and accurate diagnosis for improved patient outcomes. An intelligent paradigm is introduced that elevates label-free nonlinear optical imaging with contrastive patch-wise learning, yielding stain-free nonlinear optical computational histology (NOCH). NOCH enables swift, precise diagnostic analysis of fresh tissues, reducing patient anxiety and healthcare costs. Nonlinear modalities are evaluated, including stimulated Raman scattering and multiphoton imaging, for their ability to enhance tumor microenvironment sensitivity, pathological analysis, and cancer examination. Quantitative analysis confirmed that NOCH images accurately reproduce nuclear morphometric features across different cancer stages. Key diagnostic features, such as nuclear morphology, size, and nuclear-cytoplasmic contrast, are well preserved. NOCH models also demonstrate promising generalization when applied to other pathological tissues. The study unites label-free nonlinear optical imaging with histopathology using contrastive learning to establish stain-free computational histology. NOCH provides a rapid, non-invasive, and precise approach to surgical pathology, holding immense potential for revolutionizing cancer diagnosis and surgical interventions.
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Affiliation(s)
- Binglin Shen
- Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of EducationCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen518060China
| | - Zhenglin Li
- Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of EducationCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen518060China
| | - Ying Pan
- China–Japan Union Hospital of Jilin UniversityChangchun130033China
| | - Yuan Guo
- Shaanxi Provincial Cancer HospitalXi'an710065China
| | - Zongyi Yin
- Shenzhen University General HospitalShenzhen518055China
| | - Rui Hu
- Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of EducationCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen518060China
| | - Junle Qu
- Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of EducationCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen518060China
| | - Liwei Liu
- Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of EducationCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen518060China
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Song A, Lusk JB, Roh KM, Hsu ST, Valikodath NG, Lad EM, Muir KW, Engelhard MM, Limkakeng AT, Izatt JA, McNabb RP, Kuo AN. RobOCTNet: Robotics and Deep Learning for Referable Posterior Segment Pathology Detection in an Emergency Department Population. Transl Vis Sci Technol 2024; 13:12. [PMID: 38488431 PMCID: PMC10946693 DOI: 10.1167/tvst.13.3.12] [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: 09/26/2023] [Accepted: 01/31/2024] [Indexed: 03/19/2024] Open
Abstract
Purpose To evaluate the diagnostic performance of a robotically aligned optical coherence tomography (RAOCT) system coupled with a deep learning model in detecting referable posterior segment pathology in OCT images of emergency department patients. Methods A deep learning model, RobOCTNet, was trained and internally tested to classify OCT images as referable versus non-referable for ophthalmology consultation. For external testing, emergency department patients with signs or symptoms warranting evaluation of the posterior segment were imaged with RAOCT. RobOCTNet was used to classify the images. Model performance was evaluated against a reference standard based on clinical diagnosis and retina specialist OCT review. Results We included 90,250 OCT images for training and 1489 images for internal testing. RobOCTNet achieved an area under the curve (AUC) of 1.00 (95% confidence interval [CI], 0.99-1.00) for detection of referable posterior segment pathology in the internal test set. For external testing, RAOCT was used to image 72 eyes of 38 emergency department patients. In this set, RobOCTNet had an AUC of 0.91 (95% CI, 0.82-0.97), a sensitivity of 95% (95% CI, 87%-100%), and a specificity of 76% (95% CI, 62%-91%). The model's performance was comparable to two human experts' performance. Conclusions A robotically aligned OCT coupled with a deep learning model demonstrated high diagnostic performance in detecting referable posterior segment pathology in a cohort of emergency department patients. Translational Relevance Robotically aligned OCT coupled with a deep learning model may have the potential to improve emergency department patient triage for ophthalmology referral.
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Affiliation(s)
- Ailin Song
- Duke University School of Medicine, Durham, NC, USA
- Department of Ophthalmology, Duke University, Durham, NC, USA
| | - Jay B. Lusk
- Duke University School of Medicine, Durham, NC, USA
| | - Kyung-Min Roh
- Department of Ophthalmology, Duke University, Durham, NC, USA
| | - S. Tammy Hsu
- Department of Ophthalmology, Duke University, Durham, NC, USA
| | | | - Eleonora M. Lad
- Department of Ophthalmology, Duke University, Durham, NC, USA
| | - Kelly W. Muir
- Department of Ophthalmology, Duke University, Durham, NC, USA
| | - Matthew M. Engelhard
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | | | - Joseph A. Izatt
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Ryan P. McNabb
- Department of Ophthalmology, Duke University, Durham, NC, USA
| | - Anthony N. Kuo
- Department of Ophthalmology, Duke University, Durham, NC, USA
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
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Lin H, Ni L, Phuong C, Hong JC. Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways. Pharmgenomics Pers Med 2024; 17:65-76. [PMID: 38370334 PMCID: PMC10874185 DOI: 10.2147/pgpm.s396971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 01/29/2024] [Indexed: 02/20/2024] Open
Abstract
Natural language processing (NLP), a technology that translates human language into machine-readable data, is revolutionizing numerous sectors, including cancer care. This review outlines the evolution of NLP and its potential for crafting personalized treatment pathways for cancer patients. Leveraging NLP's ability to transform unstructured medical data into structured learnable formats, researchers can tap into the potential of big data for clinical and research applications. Significant advancements in NLP have spurred interest in developing tools that automate information extraction from clinical text, potentially transforming medical research and clinical practices in radiation oncology. Applications discussed include symptom and toxicity monitoring, identification of social determinants of health, improving patient-physician communication, patient education, and predictive modeling. However, several challenges impede the full realization of NLP's benefits, such as privacy and security concerns, biases in NLP models, and the interpretability and generalizability of these models. Overcoming these challenges necessitates a collaborative effort between computer scientists and the radiation oncology community. This paper serves as a comprehensive guide to understanding the intricacies of NLP algorithms, their performance assessment, past research contributions, and the future of NLP in radiation oncology research and clinics.
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Affiliation(s)
- Hui Lin
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
- UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, Berkeley and San Francisco, San Francisco, CA, USA
| | - Lisa Ni
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Christina Phuong
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Julian C Hong
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Joint Program in Computational Precision Health, University of California, Berkeley and San Francisco, Berkeley, CA, USA
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Lee YH, Won JH, Auh QS, Noh YK, Lee SW. Prediction of xerostomia in elderly based on clinical characteristics and salivary flow rate with machine learning. Sci Rep 2024; 14:3423. [PMID: 38341514 PMCID: PMC10858905 DOI: 10.1038/s41598-024-54120-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 02/08/2024] [Indexed: 02/12/2024] Open
Abstract
Xerostomia may be accompanied by changes in salivary flow rate and the incidence increases in elderly. We aimed to use machine learning algorithms, to identify significant predictors for the presence of xerostomia. This study is the first to predict xerostomia with salivary flow rate in elderly based on artificial intelligence. In a cross-sectional study, 829 patients with oral discomfort were enrolled, and six features (sex, age, unstimulated and stimulated salivary flow rates (UFR and SFR, respectively), number of systemic diseases, and medication usage) were used in four machine learning algorithms to predict the presence of xerostomia. The incidence of xerostomia increased with age. The SFR was significantly higher than the UFR, and the UFR and SFR were significantly correlated. The UFR, but not SFR, decreased with age significantly. In patients more than 60 years of age, the UFR had a significantly higher predictive accuracy for xerostomia than the SFR. Using machine learning algorithms with tenfold cross-validation, the prediction accuracy increased significantly. In particular, the prediction accuracy of the multilayer perceptron (MLP) algorithm that combined UFR and SFR data was significantly better than either UFR or SFR individually. Moreover, when sex, age, number of systemic diseases, and number of medications were added to the MLP model, the prediction accuracy increased from 56 to 68%.
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Affiliation(s)
- Yeon-Hee Lee
- Department of Orofacial Pain and Oral Medicine, Kyung Hee University Dental Hospital, #613 Hoegi-dong, Dongdaemun-gu, Seoul, 02447, Korea.
| | - Jong Hyun Won
- Department of Computer Science, Hanyang University, Seoul, 02455, Korea
| | - Q-Schick Auh
- Department of Orofacial Pain and Oral Medicine, Kyung Hee University Dental Hospital, #613 Hoegi-dong, Dongdaemun-gu, Seoul, 02447, Korea
| | - Yung-Kyun Noh
- Department of Computer Science, Hanyang University, Seoul, 02455, Korea
- School of Computational Sciences, Korea Institute for Advanced Study (KIAS), Seoul, 02455, Korea
| | - Sung-Woo Lee
- Department of Oral Medicine and Oral Diagnosis, Seoul National University School of Dentistry, #101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
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Haj-Hosseini N, Lindblad J, Hasséus B, Kumar VV, Subramaniam N, Hirsch JM. Early Detection of Oral Potentially Malignant Disorders: A Review on Prospective Screening Methods with Regard to Global Challenges. J Maxillofac Oral Surg 2024; 23:23-32. [PMID: 38312957 PMCID: PMC10831018 DOI: 10.1007/s12663-022-01710-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/10/2022] [Indexed: 11/28/2022] Open
Abstract
Oral cancer is a cancer type that is widely prevalent in low-and middle-income countries with a high mortality rate, and poor quality of life for patients after treatment. Early treatment of cancer increases patient survival, improves quality of life and results in less morbidity and a better prognosis. To reach this goal, early detection of malignancies using technologies that can be used in remote and low resource areas is desirable. Such technologies should be affordable, accurate, and easy to use and interpret. This review surveys different technologies that have the potentials of implementation in primary health and general dental practice, considering global perspectives and with a focus on the population in India, where oral cancer is highly prevalent. The technologies reviewed include both sample-based methods, such as saliva and blood analysis and brush biopsy, and more direct screening of the oral cavity including fluorescence, Raman techniques, and optical coherence tomography. Digitalisation, followed by automated artificial intelligence based analysis, are key elements in facilitating wide access to these technologies, to non-specialist personnel and in rural areas, increasing quality and objectivity of the analysis while simultaneously reducing the labour and need for highly trained specialists.
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Affiliation(s)
- Neda Haj-Hosseini
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Centre for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Joakim Lindblad
- Centre for Image Analysis, Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Bengt Hasséus
- Department of Oral Medicine and Pathology, Institute of Odontology, University of Gothenburg, The Sahlgrenska Academy, Gothenburg, Sweden
- Clinic of Oral Medicine, Public Dental Service, Gothenburg, Region Västra Götaland Sweden
| | - Vinay Vijaya Kumar
- Department of Head and Neck Oncology, Sri Shankara Cancer Hospital and Research Centre, Bangalore, India
- Department of Surgical Sciences, Odontology and Maxillofacial Surgery, Medical Faculty, Uppsala University, Uppsala, Sweden
| | - Narayana Subramaniam
- Department of Head and Neck Oncology, Sri Shankara Cancer Hospital and Research Centre, Bangalore, India
| | - Jan-Michaél Hirsch
- Department of Surgical Sciences, Odontology and Maxillofacial Surgery, Medical Faculty, Uppsala University, Uppsala, Sweden
- Department of Research & Development, Public Dental Services Region Stockholm, Stockholm, Sweden
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Quinn TP, Hess JL, Marshe VS, Barnett MM, Hauschild AC, Maciukiewicz M, Elsheikh SSM, Men X, Schwarz E, Trakadis YJ, Breen MS, Barnett EJ, Zhang-James Y, Ahsen ME, Cao H, Chen J, Hou J, Salekin A, Lin PI, Nicodemus KK, Meyer-Lindenberg A, Bichindaritz I, Faraone SV, Cairns MJ, Pandey G, Müller DJ, Glatt SJ. A primer on the use of machine learning to distil knowledge from data in biological psychiatry. Mol Psychiatry 2024; 29:387-401. [PMID: 38177352 DOI: 10.1038/s41380-023-02334-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/21/2023] [Accepted: 11/17/2023] [Indexed: 01/06/2024]
Abstract
Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.
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Affiliation(s)
- Thomas P Quinn
- Applied Artificial Intelligence Institute (A2I2), Burwood, VIC, 3125, Australia
| | - Jonathan L Hess
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Victoria S Marshe
- Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Michelle M Barnett
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Anne-Christin Hauschild
- Department of Medical Informatics, Medical University Center Göttingen, Göttingen, Lower Saxony, 37075, Germany
| | - Malgorzata Maciukiewicz
- Hospital Zurich, University of Zurich, Zurich, 8091, Switzerland
- Department of Rheumatology and Immunology, University Hospital Bern, Bern, 3010, Switzerland
- Department for Biomedical Research (DBMR), University of Bern, Bern, 3010, Switzerland
| | - Samar S M Elsheikh
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Xiaoyu Men
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, M5S 1A1, Canada
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Yannis J Trakadis
- Department Human Genetics, McGill University Health Centre, Montreal, QC, H4A 3J1, Canada
| | - Michael S Breen
- Psychiatry, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric J Barnett
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Yanli Zhang-James
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Mehmet Eren Ahsen
- Department of Business Administration, Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
- Department of Biomedical and Translational Sciences, Carle-Illinois School of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
| | - Han Cao
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Jiahui Hou
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Asif Salekin
- Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, 13244, USA
| | - Ping-I Lin
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, 2052, Australia
- Mental Health Research Unit, South Western Sydney Local Health District, Liverpool, NSW, 2170, Australia
| | | | - Andreas Meyer-Lindenberg
- Clinical Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Isabelle Bichindaritz
- Biomedical and Health Informatics/Computer Science Department, State University of New York at Oswego, Oswego, NY, 13126, USA
- Intelligent Bio Systems Lab, State University of New York at Oswego, Oswego, NY, 13126, USA
| | - Stephen V Faraone
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Daniel J Müller
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Würzburg, 97080, Germany
| | - Stephen J Glatt
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Public Health and Preventive Medicine, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
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Ma Y, Chen S, Xiong H, Yao R, Zhang W, Yuan J, Duan H. LVONet: automatic classification model for large vessel occlusion based on the difference information between left and right hemispheres. Phys Med Biol 2024; 69:035012. [PMID: 38211308 DOI: 10.1088/1361-6560/ad1d6a] [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: 09/20/2023] [Accepted: 01/11/2024] [Indexed: 01/13/2024]
Abstract
Objective.Stroke is a highly lethal condition, with intracranial vessel occlusion being one of its primary causes. Intracranial vessel occlusion can typically be categorized into four types, each requiring different intervention measures. Therefore, the automatic and accurate classification of intracranial vessel occlusions holds significant clinical importance for assessing vessel occlusion conditions. However, due to the visual similarities in shape and size among different vessels and variations in the degree of vessel occlusion, the automated classification of intracranial vessel occlusions remains a challenging task. Our study proposes an automatic classification model for large vessel occlusion (LVO) based on the difference information between the left and right hemispheres.Approach.Our approach is as follows. We first introduce a dual-branch attention module to learn long-range dependencies through spatial and channel attention, guiding the model to focus on vessel-specific features. Subsequently, based on the symmetry of vessel distribution, we design a differential information classification module to dynamically learn and fuse the differential information of vessel features between the two hemispheres, enhancing the sensitivity of the classification model to occluded vessels. To optimize the feature differential information among similar vessels, we further propose a novel cooperative learning loss function to minimize changes within classes and similarities between classes.Main results.We evaluate our proposed model on an intracranial LVO data set. Compared to state-of-the-art deep learning models, our model performs optimally, achieving a classification sensitivity of 93.73%, precision of 83.33%, accuracy of 89.91% and Macro-F1 score of 87.13%.Significance.This method can adaptively focus on occluded vessel regions and effectively train in scenarios with high inter-class similarity and intra-class variability, thereby improving the performance of LVO classification.
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Affiliation(s)
- Yuqi Ma
- College of Computer and Information Science, Southwest University, Chongqing, 400715, People's Republic of China
| | - Shanxiong Chen
- College of Computer and Information Science, Southwest University, Chongqing, 400715, People's Republic of China
| | - Hailing Xiong
- College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, People's Republic of China
| | - Rui Yao
- College of Computer and Information Science, Southwest University, Chongqing, 400715, People's Republic of China
| | - Wang Zhang
- College of Computer and Information Science, Southwest University, Chongqing, 400715, People's Republic of China
| | - Jiang Yuan
- College of Computer and Information Science, Southwest University, Chongqing, 400715, People's Republic of China
| | - Haowei Duan
- College of Computer and Information Science, Southwest University, Chongqing, 400715, People's Republic of China
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Meimetis N, Pullen KM, Zhu DY, Nilsson A, Hoang TN, Magliacane S, Lauffenburger DA. AutoTransOP: translating omics signatures without orthologue requirements using deep learning. NPJ Syst Biol Appl 2024; 10:13. [PMID: 38287079 PMCID: PMC10825146 DOI: 10.1038/s41540-024-00341-9] [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: 07/22/2023] [Accepted: 01/17/2024] [Indexed: 01/31/2024] Open
Abstract
The development of therapeutics and vaccines for human diseases requires a systematic understanding of human biology. Although animal and in vitro culture models can elucidate some disease mechanisms, they typically fail to adequately recapitulate human biology as evidenced by the predominant likelihood of clinical trial failure. To address this problem, we developed AutoTransOP, a neural network autoencoder framework, to map omics profiles from designated species or cellular contexts into a global latent space, from which germane information for different contexts can be identified without the typically imposed requirement of matched orthologues. This approach was found in general to perform at least as well as current alternative methods in identifying animal/culture-specific molecular features predictive of other contexts-most importantly without requiring homology matching. For an especially challenging test case, we successfully applied our framework to a set of inter-species vaccine serology studies, where 1-to-1 mapping between human and non-human primate features does not exist.
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Affiliation(s)
- Nikolaos Meimetis
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Krista M Pullen
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Daniel Y Zhu
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Avlant Nilsson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE, 41296, Sweden
| | - Trong Nghia Hoang
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99164-236, USA
| | - Sara Magliacane
- Institute of Informatics, University of Amsterdam, Amsterdam, The Netherlands
- MIT-IBM Watson AI Lab, Cambridge, MA, 02139, USA
| | - Douglas A Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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Liu GY, Yu D, Fan MM, Zhang X, Jin ZY, Tang C, Liu XF. Antimicrobial resistance crisis: could artificial intelligence be the solution? Mil Med Res 2024; 11:7. [PMID: 38254241 PMCID: PMC10804841 DOI: 10.1186/s40779-024-00510-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.
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Affiliation(s)
- Guang-Yu Liu
- Department of Immunology and Pathogen Biology, School of Basic Medical Sciences, Hangzhou Normal University, Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Key Laboratory of Inflammation and Immunoregulation of Hangzhou, Hangzhou Normal University, Hangzhou, 311121, China
| | - Dan Yu
- National Key Discipline of Pediatrics Key Laboratory of Major Diseases in Children Ministry of Education, Laboratory of Dermatology, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Mei-Mei Fan
- Department of Immunology and Pathogen Biology, School of Basic Medical Sciences, Hangzhou Normal University, Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Key Laboratory of Inflammation and Immunoregulation of Hangzhou, Hangzhou Normal University, Hangzhou, 311121, China
| | - Xu Zhang
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Ze-Yu Jin
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Christoph Tang
- Sir William Dunn School of Pathology, University of Oxford, Oxford, OX1 3RE, UK.
| | - Xiao-Fen Liu
- Institute of Antibiotics, Huashan Hospital, Fudan University, Key Laboratory of Clinical Pharmacology of Antibiotics, National Health Commission of the People's Republic of China, National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China.
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Li X, Chen F, Ma L. Exploring the Potential of Artificial Intelligence in Adolescent Suicide Prevention: Current Applications, Challenges, and Future Directions. Psychiatry 2024; 87:7-20. [PMID: 38227496 DOI: 10.1080/00332747.2023.2291945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
ObjectiveThe global surge in adolescent suicide necessitates the development of innovative and efficacious preventive measures. Traditionally, various approaches have been used, but with limited success. However, with the rapid advancements in artificial intelligence (AI), new possibilities have emerged. This paper reviews the potentials and challenges of integrating AI into suicide prevention strategies, focusing on adolescents. Method: This narrative review assesses the impact of AI on suicide prevention strategies, the strategies and cases of AI applications in adolescent suicide prevention, as well as the challenges faced. Through searches on the PubMed, web of science, PsycINFO, and EMBASE databases, 19 relevant articles were included in the review. Results: AI has significantly improved risk assessment and predictive modeling for identifying suicidal behavior. It has enabled the analysis of textual data through natural language processing and fostered novel intervention strategies. Although AI applications, such as chatbots and monitoring systems, show promise, they must navigate challenges like data privacy and ethical considerations. The research underscores the potential of AI to enhance future suicide prevention efforts through personalized interventions and integration with emerging technologies. Conclusion: AI possesses transformative potential for adolescent suicide prevention by offering targeted and adaptive solutions, while they also raise crucial ethical and practical considerations. Looking forward, AI can play a critical role in mitigating adolescent suicide rates, marking a new frontier in mental health care.
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50
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Zhu Q, Che P, Li M, Guo W, Ye K, Yin W, Chu D, Wang X, Li S. Artificial intelligence for segmentation and classification of lobar, lobular, and interstitial pneumonia using case-specific CT information. Quant Imaging Med Surg 2024; 14:579-591. [PMID: 38223078 PMCID: PMC10784088 DOI: 10.21037/qims-23-945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/14/2023] [Indexed: 01/16/2024]
Abstract
Background Pneumonia can be anatomically classified into lobar, lobular, and interstitial types, with each type associated with different pathogens. Utilizing artificial intelligence (AI) to determine the anatomical classifications of pneumonia and assist in refining the differential diagnosis may offer a more viable and clinically relevant solution. This study aimed to develop a multi-classification model capable of identifying the occurrence of pneumonia in patients by utilizing case-specific computed tomography (CT) information, categorizing the pneumonia type (lobar, lobular, and interstitial pneumonia), and performing segmentation of the associated lesions. Methods A total of 61 lobar pneumonia patients, 60 lobular pneumonia patients, and 60 interstitial pneumonia patients were consecutively enrolled at our local hospital from June 2020 and May 2022. All selected cases were divided into a training cohort (n=135) and an independent testing cohort (n=46). To generate the ground truth labels for the training process, manual segmentation and labeling were performed by three junior radiologists. Subsequently, the segmentations were manually reviewed and edited by a senior radiologist. AI models were developed to automatically segment the infected lung regions and classify the pneumonia. The accuracy of pneumonia lesion segmentation was analyzed and evaluated using the Dice coefficient. Receiver operating characteristic curves were plotted, and the area under the curve (AUC), accuracy, precision, sensitivity, and specificity were calculated to assess the efficacy of pneumonia classification. Results Our AI model achieved a Dice coefficient of 0.743 [95% confidence interval (CI): 0.657-0.826] for lesion segmentation in the training set and 0.723 (95% CI: 0.602-0.845) in the test set. In the test set, our model achieved an accuracy of 0.927 (95% CI: 0.876-0.978), precision of 0.889 (95% CI: 0.827-0.951), sensitivity of 0.889 (95% CI: 0.827-0.951), specificity of 0.946 (95% CI: 0.902-0.990), and AUC of 0.989 (95% CI: 0.969-1.000) for pneumonia classification. We trained the model using labels annotated by senior physicians and compared it to a model trained using labels annotated by junior physicians. The Dice coefficient of the model's segmentation improved by 0.014, increasing from 0.709 (95% CI: 0.589-0.830) to 0.723 (95% CI: 0.602-0.845), and the AUC improved by 0.042, rising from 0.947 to 0.989. Conclusions Our study presents a robust multi-task learning model with substantial promise in enhancing the segmentation and classification of pneumonia in medical imaging.
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Affiliation(s)
- Qiao Zhu
- Department of Radiology, the Third Hospital of Peking University, Beijing, China
| | - Peishuai Che
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Meijiao Li
- Department of Radiology, the Third Hospital of Peking University, Beijing, China
| | - Wei Guo
- Department of Radiology, the Third Hospital of Peking University, Beijing, China
| | - Kai Ye
- Department of Radiology, the Third Hospital of Peking University, Beijing, China
| | - Wenyu Yin
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Dongheng Chu
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiaohua Wang
- Department of Radiology, the Third Hospital of Peking University, Beijing, China
| | - Shufang Li
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
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