A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development Applications DOI Creative Commons
Matthew Wiens,

Alissa Verone‐Boyle,

Nick Henscheid

et al.

Clinical and Translational Science, Journal Year: 2025, Volume and Issue: 18(3)

Published: March 1, 2025

ABSTRACT Approaches to artificial intelligence and machine learning (AI/ML) continue advance in the field of drug development. A sound understanding underlying concepts guiding principles AI/ML implementation is a prerequisite identifying which approach most appropriate based on context. This tutorial focuses popular eXtreme gradient boosting (XGBoost) algorithm for classification regression simple clinical trial‐like datasets. Emphasis placed relating code implementation. In doing so, aim reader gain knowledge about become better versed with how implement functions relevant development questions. turn, this will provide practical ML experience can be applied algorithms problems beyond scope tutorial.

Language: Английский

A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer DOI

Seyed Masoud HaghighiKian,

Ahmad Shirinzadeh-Dastgiri,

Mohammad Vakili-Ojarood

et al.

Indian Journal of Surgical Oncology, Journal Year: 2024, Volume and Issue: 16(1), P. 257 - 278

Published: Sept. 5, 2024

Language: Английский

Citations

6
The utilization of artificial intelligence in enhancing 3D/4D ultrasound analysis of fetal facial profiles DOI Creative Commons
Muhammad Adrianes Bachnas, Wiku Andonotopo, Julian Dewantiningrum

et al.

Journal of Perinatal Medicine, Journal Year: 2024, Volume and Issue: 52(9), P. 899 - 913

Published: Oct. 9, 2024

Abstract Artificial intelligence (AI) has emerged as a transformative technology in the field of healthcare, offering significant advancements various medical disciplines, including obstetrics. The integration artificial into 3D/4D ultrasound analysis fetal facial profiles presents numerous benefits. By leveraging machine learning and deep algorithms, AI can assist accurate efficient interpretation complex data, enabling healthcare providers to make more informed decisions deliver better prenatal care. One such innovation that significantly improved is imaging. In conclusion, data for offers benefits, accuracy, consistency, efficiency diagnosis

Language: Английский

Citations

6
Minimal residual disease as a target for liquid biopsy in patients with solid tumours DOI
Klaus Pantel, Catherine Alix‐Panabières

Nature Reviews Clinical Oncology, Journal Year: 2024, Volume and Issue: 22(1), P. 65 - 77

Published: Nov. 28, 2024

Language: Английский

Citations

6
Neuroimage analysis using artificial intelligence approaches: a systematic review DOI
Eric Jacob Bacon, Dianning He,

N'bognon Angèle D'avilla Achi

et al.

Medical & Biological Engineering & Computing, Journal Year: 2024, Volume and Issue: 62(9), P. 2599 - 2627

Published: April 26, 2024

Language: Английский

Citations

5
A retrospective evaluation of the potential of ChatGPT in the accurate diagnosis of acute stroke DOI Creative Commons
Beyza Nur Kuzan, İsmail Meşe, Servan Yaşar

et al.

Diagnostic and Interventional Radiology, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 2, 2024

Stroke is a neurological emergency requiring rapid, accurate diagnosis to prevent severe consequences. Early crucial for reducing morbidity and mortality. Artificial intelligence (AI) support tools, such as Chat Generative Pre-trained Transformer (ChatGPT), offer rapid diagnostic advantages. This study assesses ChatGPT's accuracy in interpreting diffusion-weighted imaging (DWI) acute stroke diagnosis.

Language: Английский

Citations

5
Artificial Intelligence Detection of Cervical Spine Fractures Using Convolutional Neural Network Models DOI Creative Commons
Wongthawat Liawrungrueang, Inbo Han, Watcharaporn Cholamjiak

et al.

Neurospine, Journal Year: 2024, Volume and Issue: 21(3), P. 833 - 841

Published: Sept. 27, 2024

To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, study might potentially lead to improved patient outcomes clinical decision-making.

Language: Английский

Citations

5
Evaluation of Convolutional Neural Networks (CNNs) in Identifying Retinal Conditions Through Classification of Optical Coherence Tomography (OCT) Images DOI Open Access

Rohin R. Teegavarapu,

Harshal A. Sanghvi,

Triya Belani

et al.

Cureus, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

Introduction Diabetic retinopathy (DR) is a leading cause of blindness globally, emphasizing the urgent need for efficient diagnostic tools. Machine learning, particularly convolutional neural networks (CNNs), has shown promise in automating diagnosis retinal conditions with high accuracy. This study evaluates two CNN models, VGG16 and InceptionV3, classifying optical coherence tomography (OCT) images into four categories: normal, choroidal neovascularization, diabetic macular edema (DME), drusen. Methods Using 83,000 OCT across categories, CNNs were trained tested via Python-based libraries, including TensorFlow Keras. Metrics such as accuracy, sensitivity, specificity analyzed confusion matrices performance graphs. Comparisons dataset sizes evaluated impact on model accuracy tools deployed JupyterLab. Results InceptionV3 achieved between 85% 95%, peaking at 94% outperforming (92%). Larger datasets improved sensitivity by 7% all highest normal drusen classifications. like positively correlated size. Conclusions The confirms CNNs' potential diagnostics, achieving classification Limitations included reliance grayscale computational intensity, which hindered finer distinctions. Future work should integrate data augmentation, patient-specific variables, lightweight architectures to optimize clinical use, reducing costs improving outcomes.

Language: Английский

Citations

0
Application of artificial intelligence technologies in cardiovascular disease detection and management authors DOI Open Access
Г. Г. Кутелев, S. A. Parfenov, K. V. Sapozhnikov

et al.

Translational Medicine, Journal Year: 2025, Volume and Issue: 11(6), P. 562 - 576

Published: Jan. 26, 2025

Cardiovascular diseases (CVD) remain the leading cause of death worldwide, including in Russian Federation. Early detection and continuous monitoring are crucial to reduce mortality improve patient outcomes. This article examines use artificial intelligence technologies treatment cardiovascular diseases, emphasizing their potential for development field cardiology. A comprehensive literature search was conducted using, focusing on studies which used diagnose, treat, monitor diseases. The review includes an analysis various methods, machine learning neural networks, effectiveness detecting heart rhythm disorders using wireless sensors wearable devices. highlights promising solutions developed both internationally Federation, provides practical recommendations implementation. By addressing existing research gaps offering directions future, aims understanding application cardiology, ultimately contributes improved care

Language: Английский

Citations

0
Artificial Intelligence in Fetal Growth Restriction Management: A Narrative Review DOI Creative Commons
Ugo Maria Pierucci, Gabriele Tonni, Glória Pelizzo

et al.

Journal of Clinical Ultrasound, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 29, 2025

ABSTRACT This narrative review examines the integration of Artificial Intelligence (AI) in prenatal care, particularly managing pregnancies complicated by Fetal Growth Restriction (FGR). AI provides a transformative approach to diagnosing and monitoring FGR leveraging advanced machine‐learning algorithms extensive data analysis. Automated fetal biometry using has demonstrated significant precision identifying structures, while predictive models analyzing Doppler indices maternal characteristics improve reliability adverse outcome predictions. enabled early detection stratification risk, facilitating targeted strategies individualized delivery plans, potentially improving neonatal outcomes. For instance, studies have shown enhancements detecting placental insufficiency‐related abnormalities when tools are integrated with traditional ultrasound techniques. also explores challenges such as algorithm bias, ethical considerations, standardization, underscoring importance global accessibility regulatory frameworks ensure equitable implementation. The potential revolutionize care highlights urgent need for further clinical validation interdisciplinary collaboration.

Language: Английский

Citations

0
Identification of Cardiovascular Disease Populations in Chinese Communities Based on Machine Learning DOI

炎 谈

Advances in Clinical Medicine, Journal Year: 2025, Volume and Issue: 15(01), P. 2059 - 2069

Published: Jan. 1, 2025

Language: Английский

Citations

0