Machine Learning Approaches in Virtual Biopsy: A Review of Recent Developments and Applications DOI
Ajaz Shah,

Vishwam Modi,

Yogesh Kumar

et al.

Published: Oct. 24, 2024

Language: Английский

A Comprehensive Analysis of Artificial Intelligence Techniques for the Prediction and Prognosis of Lifestyle Diseases DOI
Krishna Modi, Ishbir Singh, Yogesh Kumar

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(8), P. 4733 - 4756

Published: June 24, 2023

Language: Английский

Citations

18
Prediction of Fetal Brain and Heart Abnormalties using Artificial Intelligence Algorithms: A Review DOI Open Access

Ashish Shiwlani

American Journal of Biomedical Science & Research, Journal Year: 2024, Volume and Issue: 22(3), P. 456 - 466

Published: May 9, 2024

Anything that deviates from the norm or what is anticipated considered an anomaly. Unusual unexpected circumstances in fetus development during pregnancy are referred to as fetal anomalies [1,2].

Language: Английский

Citations

7
A Comprehensive Study on Deep Learning Models for the Detection of Ovarian Cancer and Glomerular Kidney Disease using Histopathological Images DOI

S J K Jagadeesh Kumar,

G. Prabu Kanna,

D. Prem Raja

et al.

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: June 1, 2024

Language: Английский

Citations

4
Bridging Gaps in Artificial Intelligence Adoption for Maternal-Fetal and Obstetric Care: Unveiling Transformative Capabilities and Challenges DOI

Kalyan Tadepalli,

Abhijit Das, Tanushree Meena

et al.

Computer Methods and Programs in Biomedicine, Journal Year: 2025, Volume and Issue: 263, P. 108682 - 108682

Published: Feb. 23, 2025

Language: Английский

Citations

0
A comprehensive review of artificial intelligence - based algorithm towards fetal facial anomalies detection (2013–2024) DOI Creative Commons
N. Sriraam,

Babu Chinta,

Suresh Seshadri

et al.

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(5)

Published: March 1, 2025

Language: Английский

Citations

0
A Review of Artificial Intelligence-Based Down Syndrome Detection Techniques DOI Creative Commons
Mohd Abu Talib Shaikh,

Hazim Saleh Al-Rawashdeh,

Abdul Rahaman Wahab Sait

et al.

Life, Journal Year: 2025, Volume and Issue: 15(3), P. 390 - 390

Published: March 1, 2025

Background: Down syndrome (DS) is one of the most prevalent chromosomal abnormalities affecting global healthcare. Recent advances in artificial intelligence (AI) and machine learning (ML) have enhanced DS diagnostic accuracy. However, there a lack thorough evaluations analyzing overall impact effectiveness AI-based approaches. Objectives: This review intends to identify methodologies technologies used AI-driven diagnostics. It evaluates performance AI models terms standard evaluation metrics, highlighting their strengths limitations. Methodology: In order ensure transparency rigor, authors followed preferred reporting items for systematic reviews meta-analyses (PRISMA) guidelines. They extracted 1175 articles from major academic databases. By leveraging inclusion exclusion criteria, final set 25 was selected. Outcomes: The findings revealed significant advancements AI-powered diagnostics across diverse data modalities. modalities, including facial images, ultrasound scans, genetic data, demonstrated strong potential early diagnosis. Despite these advancements, this outlined limitations Small imbalanced datasets reduce generalizability models. present actionable strategies enhance clinical adoptions

Language: Английский

Citations

0
Diagnosis and Management of Sexually Transmitted Infections Using Artificial Intelligence Applications Among Key and General Populations in Sub-Saharan Africa: A Systematic Review and Meta-Analysis DOI Creative Commons
Claris Siyamayambo, Edith Phalane, Nancy Phaswana‐Mafuya

et al.

Algorithms, Journal Year: 2025, Volume and Issue: 18(3), P. 151 - 151

Published: March 7, 2025

The Fourth Industrial Revolution (4IR) has significantly impacted healthcare, including sexually transmitted infection (STI) management in Sub-Saharan Africa (SSA), particularly among key populations (KPs) with limited access to health services. This review investigates 4IR technologies, artificial intelligence (AI) and machine learning (ML), that assist diagnosing, treating, managing STIs across SSA. By leveraging affordable accessible solutions, tools support KPs who are disproportionately affected by STIs. Following systematic guidelines using Covidence, this study examined 20 relevant studies conducted SSA countries, Ethiopia, South Africa, Zimbabwe emerging as the most researched nations. All reviewed used secondary data favored supervised ML models, random forest XGBoost frequently demonstrating high performance. These tracking services, predicting risks of STI/HIV, developing models for community HIV clusters. While AI enhanced accuracy diagnostics efficiency management, several challenges persist, ethical concerns, issues quality, a lack expertise implementation. There few real-world applications or pilot projects Notably, primarily focus on development, validation, technical evaluation methods rather than their practical application As result, actual impact these approaches point care remains unclear. highlights effectiveness various through detection, diagnosis, treatment, monitoring. strengthens knowledge technologies Understanding potential improve sexual outcomes, address gaps STI surpass limitations traditional syndromic approaches.

Language: Английский

Citations

0
Machine Learning Based Approaches for Livestock Symptoms and Diseases Prediction and Classification DOI
Priya Bhardwaj,

S J K Jagadeesh Kumar,

G. Prabu Kanna

et al.

Published: May 9, 2024

Language: Английский

Citations

3
A novel deep learning-based approach for prediction of neonatal respiratory disorders from chest X-ray images DOI
Ayşe Yildirim, Murat Canayaz

Journal of Applied Biomedicine, Journal Year: 2023, Volume and Issue: 43(4), P. 635 - 655

Published: Sept. 5, 2023

Language: Английский

Citations

7
Machine Learning-Based Approaches for the Prognosis and Prediction of Multiple Diseases DOI
Priya Bhardwaj, Yogesh Kumar, Shakti Mishra

et al.

Published: Jan. 24, 2024

The rapid progress in machine learning techniques has significantly transformed healthcare which enables the simultaneous and accurate detection of multiple diseases. This paper delves into application diverse algorithms for multi-disease by using a comprehensive dataset focuses on three diseases i.e. diabetes, gonorrhoea, typhoid. been meticulously pre-processed graphically visualized to discern patterns represent against emotional states/urges critical feelings. Subsequently, range classifiers includes logistic regression, Adaboost, random forest, support vector machine, CatBoost, Light Gradient Boosting Classifier, Naïve Bayes, XGBoost, KNN, Decision Tree, are trained this dataset. Their performance across these different classes is rigorously evaluated various parameters such as accuracy, F1 score, recall, precision. During execution, Adaboost emerged top performer, achieving an impressive accuracy 94.37% maintaining precision, score 0.94, indicates its robustness detection.

Language: Английский

Citations

2