A systematic review and research recommendations on artificial intelligence for automated cervical cancer detection DOI Creative Commons
Smith K. Khare, Victoria Blanes‐Vidal, Berit Bargum Booth

et al.

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2024, Volume and Issue: 14(6)

Published: July 15, 2024

Abstract Early diagnosis of abnormal cervical cells enhances the chance prompt treatment for cancer (CrC). Artificial intelligence (AI)‐assisted decision support systems detecting are developed because manual identification needs trained healthcare professionals, and can be difficult, time‐consuming, error‐prone. The purpose this study is to present a comprehensive review AI technologies used pre‐cancerous lesions cancer. includes studies where was applied Pap Smear test (cytological test), colposcopy, sociodemographic data other risk factors, histopathological analyses, magnetic resonance imaging‐, computed tomography‐, positron emission tomography‐scan‐based imaging modalities. We performed searches on Web Science, Medline, Scopus, Inspec. preferred reporting items systematic reviews meta‐analysis guidelines were search, screen, analyze articles. primary search resulted in identifying 9745 followed strict inclusion exclusion criteria, which include windows last decade, journal articles, machine/deep learning‐based methods. A total 58 have been included further analysis after identification, screening, eligibility evaluation. Our shows that deep learning models techniques, whereas machine data. convolutional neural network‐based features yielded representative characteristics CrC. also highlights need generating new easily accessible diverse datasets develop versatile CrC detection. model explainability uncertainty quantification increase trust clinicians stakeholders decision‐making automated detection models. suggests privacy concerns adaptability crucial deployment hence, federated meta‐learning should explored. This article categorized under: Fundamental Concepts Data Knowledge > Explainable Technologies Machine Learning Classification

Language: Английский

Predictive Analytics in Clinical Psychology DOI

A Bhanu Prasad

Advances in psychology, mental health, and behavioral studies (APMHBS) book series, Journal Year: 2025, Volume and Issue: unknown, P. 313 - 332

Published: Jan. 3, 2025

Predictive analytics, powered by advancements in machine learning (ML), is reshaping the landscape of clinical psychology and mental health care. This paper explores transformative potential ML algorithms early diagnosis, personalized treatment planning, predictive risk assessments for disorders. By analysing complex datasets, including behavioural, genetic, environmental variables, models provide unprecedented accuracy identifying patterns factors associated with conditions such as depression, anxiety, bipolar disorder, schizophrenia. The study highlights integration natural language processing (NLP) patient interactions, wearable technologies real-time monitoring, reinforcement adaptive therapeutic interventions. concludes emphasizing a collaborative approach involving clinicians, data scientists, policymakers to ensure equitable effective implementation.

Language: Английский

Citations

1
A bibliometric analysis of technology in sustainable healthcare: Emerging trends and future directions DOI Creative Commons
Isaac Kofi Nti, Adebayo Felix Adekoya, Benjamin Asubam Weyori

et al.

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 8, P. 100292 - 100292

Published: July 25, 2023

Technology application in healthcare is a recent field devoted to sustainability the industry. However, research this sector has grown at rapid pace. While expansion been advantageous for discipline, it also made more difficult grasp its extent. As result, answering questions such as most important emerging trends technology sustainable research, critical breakthrough papers, influence of these and productive leading researchers have become challenging. Finally, understanding intellectual framework knowledge base on Sustainable Healthcare (TSH) difficult. This study attempted address issues by presenting an overview work TSH and, doing so, answer some previously listed problems. The PRISMA model, along with science mapping review process using bibliometric analysis tools VOSviewer Python, was employed analyze published works indexed Scopus database over span 24 years. Although had progressing rapidly before COVID-19 pandemic, current accelerated shift past four years may be attributed pandemic itself well advancements technologies artificial intelligence, machine learning, Internet Things. We discuss themes, prolific authors, institutions, journals, relationships among TSH. we present challenges prospects research. findings our would helpful working healthcare.

Language: Английский

Citations

21
Recent Advances of Biosensors for Detection of Multiple Antibiotics DOI Creative Commons
Ning Lu, Juntao Chen, Zhikang Rao

et al.

Biosensors, Journal Year: 2023, Volume and Issue: 13(9), P. 850 - 850

Published: Aug. 26, 2023

The abuse of antibiotics has caused a serious threat to human life and health. It is urgent develop sensors that can detect multiple quickly efficiently. Biosensors are widely used in the field antibiotic detection because their high specificity. Advanced artificial intelligence/machine learning algorithms have allowed for remarkable achievements image analysis face recognition, but not yet been biosensors. Herein, this paper reviews biosensors simultaneous based on different mechanisms biorecognition elements recent years, compares analyzes characteristics specific applications. In particular, review summarizes some AI/ML with excellent performance detection, which provide platform intelligence terminal apps portability. Furthermore, gives short antibiotics.

Language: Английский

Citations

20
Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023 DOI
Mahboobeh Jafari,

Delaram Sadeghi,

Afshin Shoeibi

et al.

Applied Intelligence, Journal Year: 2023, Volume and Issue: 54(1), P. 35 - 79

Published: Dec. 5, 2023

Language: Английский

Citations

19
ADHD-AID: Aiding Tool for Detecting Children’s Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection DOI Creative Commons
Omneya Attallah

Biomimetics, Journal Year: 2024, Volume and Issue: 9(3), P. 188 - 188

Published: March 20, 2024

The severe effects of attention deficit hyperactivity disorder (ADHD) among adolescents can be prevented by timely identification and prompt therapeutic intervention. Traditional diagnostic techniques are complicated time-consuming because they subjective-based assessments. Machine learning (ML) automate this process prevent the limitations manual evaluation. However, most ML-based models extract few features from a single domain. Furthermore, studies have not examined effective electrode placement on skull, which affects process, while others employed feature selection approaches to reduce space dimension consequently complexity training models. This study presents an tool for automatically identifying ADHD entitled "ADHD-AID". present uses several multi-resolution analysis including variational mode decomposition, discrete wavelet transform, empirical decomposition. ADHD-AID extracts thirty time time-frequency domains identify ADHD, nonlinear features, band-power entropy-based statistical features. also looks at best EEG detecting ADHD. Additionally, it into location combinations that significant impact accuracy. variety methods choose those greatest influence diagnosis reducing classification's time. results show has provided scores accuracy, sensitivity, specificity, F1-score, Mathew correlation coefficients 0.991, 0.989, 0.992, 0.982, respectively, in with 10-fold cross-validation. Also, area under curve reached 0.9958. ADHD-AID's significantly higher than all earlier detection adolescents. These notable trustworthy findings support use such automated as means assistance doctors youngsters.

Language: Английский

Citations

8
Review on the Use of Brain Computer Interface Rehabilitation Methods for Treating Mental and Neurological Conditions DOI Creative Commons
Vladimir Khorev, Semen Kurkin, Artem Badarin

et al.

Journal of Integrative Neuroscience, Journal Year: 2024, Volume and Issue: 23(7)

Published: July 5, 2024

This review provides a comprehensive examination of recent developments in both neurofeedback and brain-computer interface (BCI) within the medical field rehabilitation. By analyzing comparing results obtained with various tools techniques, we aim to offer systematic understanding BCI applications concerning different modalities input data utilized. Our primary objective is address existing gap area meta-reviews, which more outlook on field, allowing for assessment current landscape scope BCI. main methodologies include meta-analysis, search queries employing relevant keywords, network-based approach. We are dedicated delivering an unbiased evaluation studies, elucidating vectors research development this field. encompasses diverse range applications, incorporating use interfaces rehabilitation treatment diagnoses, including those related affective spectrum disorders. encompassing wide variety cases, perspective utilization treatments across contexts. The structured organized presentation information, complemented by accompanying visualizations diagrams, renders valuable resource scientists researchers engaged domains biofeedback interfaces.

Language: Английский

Citations

8
Artificial Intelligence for Enhancing Special Education for K-12: A Decade of Trends, Themes, and Global Insights (2013–2023) DOI
Yuqin Yang,

L Chen,

Wenmeng He

et al.

International Journal of Artificial Intelligence in Education, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 19, 2024

Language: Английский

Citations

8
A novel approach to identify the brain regions that best classify ADHD by means of EEG and deep learning DOI Creative Commons
Javier Sanchís, Sandra García-Ponsoda, Miguel A. Teruel

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(4), P. e26028 - e26028

Published: Feb. 1, 2024

Attention-Deficit Hyperactivity Disorder (ADHD) is one of the most widespread neurodevelopmental disorders diagnosed in childhood. ADHD by following guidelines Diagnostic and Statistical Manual Mental Disorders, Fifth Edition (DSM-5). According to DSM-5, has not yet identified a specific cause, thus researchers continue investigate this field. Therefore, primary objective work present study find subset channels or brain regions that best classify vs Typically Developing children means Electroencephalograms (EEG).

Language: Английский

Citations

7
Quantum machine learning for drowsiness detection with EEG signals DOI
Isis Didier Lins, Lavínia Maria Mendes Araújo, Caio Bezerra Souto Maior

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 186, P. 1197 - 1213

Published: April 15, 2024

Language: Английский

Citations

7
A Hybrid Deep Spatiotemporal Attention‐Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals DOI
Niloufar Delfan, M R Shahsavari, Sadiq Hussain

et al.

International Journal of Imaging Systems and Technology, Journal Year: 2024, Volume and Issue: 34(4)

Published: June 21, 2024

ABSTRACT Parkinson's disease (PD), a severe and progressive neurological illness, affects millions of individuals worldwide. For effective treatment management PD, an accurate early diagnosis is crucial. This study presents deep learning‐based model for the PD using resting state electroencephalogram (EEG) signal. The objective to develop automated that can extract complex hidden nonlinear features from EEG demonstrate its generalizability on unseen data. designed hybrid model, consisting convolutional neural network (CNN), bidirectional gated recurrent unit (Bi‐GRU), attention mechanism. proposed method evaluated three public datasets (UC San Diego, PRED‐CT, University Iowa [UI] dataset), with one dataset used training other two evaluation. demonstrated remarkable performance, attaining high accuracy scores 99.4%, 84%, 73.2% UC UI datasets, respectively. These results justify effectiveness robustness across diverse highlighting potential versatile applications in data analysis prediction tasks. Our spatiotemporal attention‐based has been developed 10‐fold cross‐validation (CV) Diego CV leave‐one‐out (LOOCV) strategies PRED‐CT datasets. indicate detection system robust. prototype be neurodegenerative diseases such as Alzheimer's disease, Huntington's so forth.

Language: Английский

Citations

7