Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review

JMIR mhealth and uhealth, Journal Year: 2023, Volume and Issue: 12, P. e44406 - e44406

Published: Aug. 18, 2023

In the modern world, mobile apps are essential for human advancement, and pandemic control is no exception. The use of technology detection diagnosis COVID-19 has been subject numerous investigations, although thorough analysis prevention conducted using apps, creating a gap.

Language: Английский

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Artificial intelligence in hepatocellular carcinoma diagnosis: a comprehensive review of current literature

Journal of Gastroenterology and Hepatology, Journal Year: 2024, Volume and Issue: 39(10), P. 1994 - 2005

Published: June 23, 2024

Hepatocellular carcinoma (HCC) diagnosis mainly relies on its pathognomonic radiological profile, obviating the need for biopsy. The project of incorporating artificial intelligence (AI) techniques in HCC aims to improve performance image recognition. Herein, we thoroughly analyze and evaluate proposed AI models field diagnosis.

Language: Английский

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Artificial intelligence applications for immunology laboratory: image analysis and classification study of IIF photos

Immunologic Research, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 6, 2024

Language: Английский

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Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data

BMC Medical Informatics and Decision Making, Journal Year: 2024, Volume and Issue: 24(1)

Published: Feb. 14, 2024

Abstract Timely and accurate referral of end-stage heart failure patients for advanced therapies, including transplants mechanical circulatory support, plays an important role in improving patient outcomes saving costs. However, the decision-making process is complex, nuanced, time-consuming, requiring cardiologists with specialized expertise training transplantation. In this study, we propose two logistic tensor regression-based models to predict warranting evaluation therapies using irregularly spaced sequential electronic health records at population individual levels. The clinical features were collected previous visit predictions made very beginning subsequent visit. Patient-wise ten-fold cross-validation experiments performed. Standard LTR achieved average F1 score 0.708, AUC 0.903, AUPRC 0.836. Personalized obtained 0.670, 0.869 0.839. not only outperformed all other machine learning which they compared but also improved performance robustness via weight transfer. scores support vector machine, random forest, Naive Bayes are by 8.87%, 7.24%, 11.38%, respectively. can evaluate importance associated therapy referral. five most medical codes, chronic kidney disease, hypotension, pulmonary mitral regurgitation, atherosclerotic reviewed validated literature cardiologists. Our proposed effectively utilize EHRs potential necessity while explaining comorbidities events. information learned from trained model could offer further insight into risk factors contributing progression both

Language: Английский

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An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 18, 2024

Abstract Appendicitis, an infection and inflammation of the appendix is a prevalent condition in children that requires immediate treatment. Rupture may lead to several complications, such as peritonitis sepsis. Appendicitis medically diagnosed using urine, blood, imaging tests. In recent times, Artificial Intelligence machine learning have been boon for medicine. Hence, supervised techniques utilized this research diagnose appendicitis pediatric patients. Six heterogeneous searching used perform hyperparameter tuning optimize predictions. These are Bayesian Optimization, Hybrid Bat Algorithm, Self-adaptive Firefly Grid Search, Randomized Search. Further, nine classification metrics were study. The Algorithm technique performed best among above algorithms, with accuracy 94% customized APPSTACK model. Five explainable artificial intelligence tested interpret results made by classifiers. According explainers, length stay, means vermiform detected on ultrasonography, white blood cells, diameter most crucial markers detecting appendicitis. proposed system can be hospitals early/quick diagnosis validate obtained other diagnostic modalities.

Language: Английский

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Convolutional Neural Network to Classify Infrared Thermal Images of Fractured Wrists in Pediatrics

Healthcare, Journal Year: 2024, Volume and Issue: 12(10), P. 994 - 994

Published: May 11, 2024

Convolutional neural network (CNN) models were devised and evaluated to classify infrared thermal (IRT) images of pediatric wrist fractures. The recorded from 19 participants with a fracture 21 without (sprain). injury diagnosis was by X-ray radiography. For each participant, 299 IRT their wrists recorded. These generated 11,960 (40 × images). image, the region interest (ROI) selected fast Fourier transformed (FFT) obtain magnitude frequency spectrum. spectrum resized 100 pixels its center as this represented main components. Image augmentations rotation, translation shearing applied spectra assist CNN generalization during training. had 34 layers associated convolution, batch normalization, rectified linear unit, maximum pooling SoftMax classification. ratio for training test 70:30, respectively. effects augmentation dropout on performance explored. Wrist identification sensitivity accuracy 88% 76%, respectively, achieved. model able identify fractures; however, larger sample size would improve accuracy.

Language: Английский

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Machine Learning Model Construction and Testing: Anticipating Cancer Incidence and Mortality

Diseases, Journal Year: 2024, Volume and Issue: 12(7), P. 139 - 139

Published: June 30, 2024

In recent years, the escalating environmental challenges have contributed to a rising incidence of cancer. The precise anticipation cancer and mortality rates has emerged as pivotal focus in scientific inquiry, exerting profound impact on formulation public health policies. This investigation adopts pioneering machine learning framework address this critical issue, utilizing dataset encompassing 72,591 comprehensive records that include essential variables such age, case count, population size, race, gender, site, year diagnosis. Diverse algorithms, including decision trees, random forests, logistic regression, support vector machines, neural networks, were employed study. ensuing analysis revealed testing accuracies 62.17%, 61.92%, 54.53%, 55.72%, 62.30% for respective models. state-of-the-art model not only enhances our understanding dynamics but also equips researchers policymakers with capability making meticulous projections concerning forthcoming rates. Considering sustainability, application advanced emphasizes importance judiciously extensive intricate databases. By doing so, it facilitates more sustainable approach healthcare planning, allowing informed decision-making takes into account long-term ecological societal impacts cancer-related integrative perspective underscores broader commitment practices both research policy formulation.

Language: Английский

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SpectroCVT-Net: A convolutional vision transformer architecture and channel attention for classifying Alzheimer’s disease using spectrograms

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 181, P. 109022 - 109022

Published: Aug. 23, 2024

Language: Английский

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Comparative analysis of the performance of supervised learning algorithms for photovoltaic system fault diagnosis

Science and Technology for Energy Transition, Journal Year: 2024, Volume and Issue: 79, P. 27 - 27

Published: Jan. 1, 2024

New trends were introduced in using PhotoVoltaic (PV) energy which are mostly attributable to new laws internationally having a goal decrease the usage of fossil fuels. The PV systems efficiency is impacted significantly by environmental factors and different faults occurrence. These if they not rapidly identified fixed may cause dangerous consequences. A lot methods have been literature detect that occur system such as Current-Voltage (I-V) curve measurements, atmospheric models statistical methods. In this paper, various machine learning techniques particular supervised used for array failure diagnosis. main target identification categorization several shadowing, degradation, open circuit short great impact on performance. results showed technique’s high ability fault diagnosis capability. K-Nearest Neighbor (KNN) technique best prediction It achieves accuracy 99.2% 99.7% Area Under Curve-Receiver Operating Curve (AUC-ROC) score. This shows its superiority over other Decision Tree, Naïve Bayes, Logistic Regression.

Language: Английский

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The impact of artificial intelligence in the diagnosis and management of acoustic neuroma: A systematic review

Technology and Health Care, Journal Year: 2024, Volume and Issue: 32(6), P. 3801 - 3813

Published: Aug. 2, 2024

Schwann cell sheaths are the source of benign, slowly expanding tumours known as acoustic neuromas (AN). The diagnostic and treatment approaches for AN must be patient-centered, taking into account unique factors preferences.

Language: Английский

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