Published: Oct. 11, 2024
Language: Английский
Advances in medical technologies and clinical practice book series, Journal Year: 2023, Volume and Issue: unknown, P. 16 - 35
Published: Oct. 18, 2023
This narrative analysis research investigates the use of artificial intelligence (AI) in precision medicine. It focuses specifically on how AI technology may be used to improve medical practice and patient outcomes. The combination with medicine has potential revolutionize health care. Precision is a type healthcare that considers an individual's genetic, environmental, behavioural characteristics. resource-based view (RBV) theoretical framework this study give lens through which explore many components required incorporating for These include technology, resource acquisition, utilization, heterogeneity, complementarity. attempts provide light possible benefits, future consequences complete analysis.
Language: Английский
Citations
7
PLoS ONE, Journal Year: 2024, Volume and Issue: 19(5), P. e0298286 - e0298286
Published: May 14, 2024
Precision medicine endeavors to personalize treatments, considering individual variations in patient responses based on factors like genetic mutations, age, and diet. Integrating this approach dynamically, bioelectronics equipped with real-time sensing intelligent actuation present a promising avenue. Devices such as ion pumps hold potential for precise therapeutic drug delivery, pivotal aspect of effective precision medicine. However, implementing bioelectronic devices encounters formidable challenges. Variability device performance due fabrication inconsistencies operational limitations, including voltage saturation, presents significant hurdles. To address this, closed-loop control adaptive capabilities explicit handling saturation becomes imperative. Our research introduces an enhanced sliding mode controller capable managing adept at satisfactory actions amidst model uncertainties. evaluate the controller’s effectiveness, we conducted silico experiments using extended mathematical proton pump. Subsequently, compared our developed classical Proportional Integral Derivative (PID) machine learning (ML)–based controllers. Furthermore, vitro assessed efficacy various reference signals controlled Fluoxetine delivery. These showcased consistent across diverse input signals, maintaining current value near relative error less than 7% all trials. findings underscore challenges implementation, offering reliable delivery strategies within realm
Language: Английский
Citations
2
International Journal of Surgery, Journal Year: 2024, Volume and Issue: 110(11), P. 7142 - 7149
Published: Aug. 8, 2024
Risk stratification for patients undergoing coronary artery bypass surgery (CABG) left main (LMCA) disease is essential informed decision-making. This study explored the potential of machine learning (ML) methods to identify key risk factors associated with mortality in this patient group.
Language: Английский
Citations
2
Deleted Journal, Journal Year: 2024, Volume and Issue: 20(2s), P. 768 - 776
Published: April 4, 2024
The current paper is aimed at examining the use of machine learning approaches for lung cancer detection and classification using medical imaging data. In order to create model, we collected a comprehensive dataset 2400 images different stages healthy pictures. These data were preprocessed, several feature extraction considered, namely Histogram Oriented Gradients , Local Binary Patterns . addition, attempted deep representations determine their usefulness in this case. Moreover, these features used four ML models, Convolutional Neural Network ResNet-18, VGG-19, most suitable one. To evaluate general performance all characteristic points taken into account, such as precision, recall, F1 score, accuracy, confusion matrices. results primary analysis indicate that accuracy our proposed model was highest, 96.86%. other places by architectures, which also demonstrate high level performance. general, may conclude findings show it possible algorithms improve quality clinical decisions make process more accurate. At same time, able provide evaluation thorough each model. This serve basis subsequent improvements changes would allow enhancing diagnostics training advanced models.
Language: Английский
Citations
1
Royal Society Open Science, Journal Year: 2024, Volume and Issue: 11(7)
Published: July 1, 2024
Finding the optimal treatment strategy to accelerate wound healing is of utmost importance, but it presents a formidable challenge owing intrinsic nonlinear nature process. We propose an adaptive closed-loop control framework that incorporates deep learning, and reinforcement learning healing. By adaptively linear representation dynamics using interactively training agent for tracking signal derived from this without need intricate mathematical modelling, our approach has not only successfully reduced time by 45.56% compared one any treatment, also demonstrates advantages offering safer more economical strategy. The proposed methodology showcases significant potential expediting effectively integrating perception, predictive modelling control, eliminating models.
Language: Английский
Citations
1
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown
Published: Aug. 31, 2023
Abstract Precision medicine tailors treatment in a way that accounts for variations patient response. Treatment strategies can be determined based on factors such as genetic mutations, age, and diet. Another of implementing precision dynamic fashion is through bioelectronics equipped with real-time sensing intelligent actuation. Bioelectronic devices ion pumps utilized to deliver therapeutic drugs. To able perform medicine, medical need drugs high precision. For this, closed-loop control required change the strategy new information about response progression biological system received. this end, sliding mode controller given its ability satisfactory actions when there model uncertainty. The used an experiment goal delivering pre-determined dosage fluoxetine throughout period time.
Language: Английский
Citations
1
Published: Oct. 11, 2023
The present research uses a dataset of 2034 images to conduct detailed evaluation machine learning models for the purpose identifying brain tumours in MRI scans. Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forest (RF), and Logistic Regression (LR) were four alternative that thoroughly examined based on their performance indicators. Network was shown be most effective model, with high accuracy 97.5%, great precision, recall, F1 scores. This demonstrates how deep learning, namely CNNs, can used automate improve tumour identification medical imaging. study also underlines SVM RF models' durability adaptability, which demonstrated exceptional metrics thus acceptable use real-world healthcare applications. Despite having substantially lower scores, model significantly aids diagnosis. Finally, this importance business potential early detection, would patient care treatment outcomes. It emphasises field image analysis is always evolving improving, meaning advances detection management major disorders.
Language: Английский
Citations
1
Therapeutic Drug Monitoring, Journal Year: 2024, Volume and Issue: unknown, P. 355 - 371
Published: Jan. 1, 2024
The sequencing of the human genome more than 2 decades ago marked a historic achievement that brought about revolutionary changes in pharmaceutical industry and our comprehension genetic influences drug development. combination genomic technologies artificial intelligence has opened new avenues for discovery It facilitated identification potential targets, improved understanding response variability, enhanced prediction toxicity. Additionally, these advancements have paved way personalized medicine approaches, where treatments can be tailored to an individual's profile. A DNA microarray, also known as gene or chip, cDNA array, biochip, consists features attached solid support, such glass, plastic, film, silicon is very effective tool medicine. This chapter describes advances various microarray technology pharmacogenomics testing.
Language: Английский
Citations
0
2022 International Conference on Inventive Computation Technologies (ICICT), Journal Year: 2024, Volume and Issue: unknown, P. 88 - 93
Published: April 24, 2024
This paper investigates the suitability of advanced deep learning models for precise diagnosis lung cancer from MRI images. Recurrent neural networks (RNN), K-Nearest Neighbors (KNN), ResNet50, and convolutional (CNN) were all carefully evaluated to determine their unique contributions. The CNN showed off its good performance capacity recognize intricate patterns in images, achieving an accuracy 92.3%. KNN demonstrated competitive results, demonstrating adaptability non-parametric methods medical image classification. Remarkably, ResNet50 fared extremely well, exhibiting a remarkable 94.8% verifying value residual differentiating between features. RNNs gave analysis temporal dimension contributed 89.5% accuracy. Information confusion matrices containing comprehensive classification results was useful refining model. Spatial representations expected cell locations effectiveness by giving doctors visual cues targeted interventions. Comparisons with literature show that are line recent developments analysis. Because assessment different architectures, which provides fresh perspectives advance field detection technologies, this work is invaluable resource future research.
Language: Английский
Citations
0
Published: May 3, 2024
Language: Английский
Citations
0