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Vol - 1, No. 1, July - September 2025

Artificial Intelligence for Detecting Coronavirus (COVID-19): An Extended Review

Authors:

Mohammed Hussein Khalil, Ank Gabor

Abstract:

Rapid, accurate detection of coronavirus disease 2019 (COVID-19) has been essential for epidemic control. While reverse-transcription polymerase chain reaction (RT-PCR) remains the reference standard, constraints in turnaround time, access, and sensitivity under certain conditions have motivated the use of Artificial Intelligence (AI) to assist screening and diagnosis from complementary data sources: medical images (chest radiographs and computed tomography), respiratory audio (cough/voice/breath), wearable and consumer-device signals, and clinical/electronic health record (EHR) data. This review synthesizes key datasets, model families, validation strategies, and performance trends, and highlights persistent pitfalls including dataset bias, information leakage, lack of external validation, and explainability gaps. We conclude with a roadmap for robust, clinically useful AI systems: rigorous study design, standardized reporting, multicenter external testing, prospective evaluation, human factors integration, and governance for safety, privacy, and equity.

Keywords:

COVID-19, SARS‑CoV‑2, artificial intelligence, machine learning, deep learning, chest X‑ray, CT, cough, voice, wearables, EHR, diagnosis, screening, triage

Refference:

1. Bai, H. X., et al. “Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia on Chest CT.” Radiology, vol. 296, no. 3, 2020, pp. E156–65. 10.1148/radiol.2020201491.
2. Brown, C., J. Chauhan, A. Grammenos, et al. “Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data.” IEEE Trans Audio Speech Lang Process., vol. 29, 2021, pp. 2010–23. 10.48550/arXiv.2006.05919
3. Cohen, J. P., P. Morrison, and L. Dao. “COVID-19 Image Data Collection.” arXiv, 2020, arXiv:2003.11597.
4. de la Iglesia Vayá, M., et al. “BIMCV COVID-19+: A Large Annotated Dataset of Chest Radiograph Images and Clinical Data.” medRxiv, 2020. 10.48550/arXiv.2006.01174
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6. Harmon, S. A., et al. “Artificial Intelligence for the Detection of COVID-19 Pneumonia on Chest CT Using Multinational Datasets.” Nat Commun, vol. 11, 2020, p. 4080. doi:10.1038/s41467-020-17971-2.
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8. Laguarta, Jordi, Ferran Hueto, and Brian Subirana. “COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings.” IEEE Open Journal of Engineering in Medicine and Biology, vol. 1, 2020, pp. 275–81. 10.1109/OJEMB.2020.3026928
9. Li, L., et al. “Artificial Intelligence Distinguishes COVID-19 from Community-Acquired Pneumonia on Chest CT.” Radiology, vol. 296, no. 2, 2020, pp. E65–71. 10.1148/radiol.2020200905.
10. Lundberg, Scott M., and Su-In Lee. “A Unified Approach to Interpreting Model Predictions.” Advances in Neural Information Processing Systems (NeurIPS), 2017. https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf
11. Mishra, T., et al. “Pre-Symptomatic Detection of COVID-19 from Smartwatch Data.” Nature Biomedical Engineering, vol. 4, 2020, pp. 1208–10. 10.1038/s41551-020-00640-6
12. Morozov, S. P., et al. “MosMedData: Chest CT Scans with COVID-19 Related Findings Dataset.” medRxiv, 2020. 10.1101/2020.05.20.20100362
13. Ozturk, T., et al. “Automated Detection of COVID-19 Cases Using Deep Neural Networks with X-Ray Images.” Computers in Biology and Medicine, vol. 121, 2020, p. 103792. 10.1016/j.compbiomed.2020.103792
14. Orlandic, L., T. Teijeiro, and D. Atienza. “The COUGHVID Crowdsourcing Dataset.” Scientific Data, vol. 8, 2021, p. 156. 10.1038/s41597-021-00937-4
15. Quer, G., et al. “Wearable Sensor Data and Self-Reported Symptoms for COVID-19 Detection.” Nature Medicine, vol. 27, 2021, pp. 73–77. 10.1038/s41591-020-1123-x
16. Radin, J. M., et al. “Harnessing Wearable Device Data to Improve COVID-19 Detection.” Scientific Reports, vol. 10, 2020, p. 13773.
17. Rajpurkar, P., et al. “CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning.” arXiv, 2017, arXiv:1711.05225.
18. Roberts, M., et al. “Common Pitfalls and Recommendations for Using ML to Detect and Prognosticate for COVID-19 Using Chest Radiographs and CT Scans.” Nature Machine Intelligence, vol. 3, 2021, pp. 199–217. 10.1038/s42256-021-00307-0
19. RSNA/ACR. RICORD – The RSNA International COVID-19 Open Radiology Database, 2021. 10.1148/radiol.2021203957
20. Selvaraju, R. R., et al. “Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization.” Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017. 10.1109/ICCV.2017.74
21. Shorten, C., and T. M. Khoshgoftaar. “A Survey on Image Data Augmentation for Deep Learning.” Journal of Big Data, vol. 6, 2019, p. 60. 10.1186/s40537-019-0197-0
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25. Yan, L., et al. “An Interpretable Mortality Prediction Model for COVID-19 Patients.” Nature Machine Intelligence, vol. 2, 2020, pp. 283–88. 10.1038/s42256-020-0180-7

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COMPETENCY-BASED TEACHING IN IMPROVING NEONATAL RESUSCITATION KNOWLEDGE AMONG NURSING INTERNS: A QUASI-EXPERIMENTAL STUDY

Authors:

Bani Prasanna Behera

Abstract:

Neonatal mortality remains a global challenge, with approximately 2.4 million neonatal deaths occurring annually. The first minutes of life are critical, and timely, effective neonatal resuscitation can prevent up to 30% of intrapartum-related deaths. Despite its proven benefits, gaps in competency persist among nursing graduates, often due to reliance on didactic teaching methods that fail to build procedural confidence. This quasi-experimental study evaluated the effectiveness of a Competency-Based Teaching (CBT) programme in improving neonatal resuscitation knowledge and confidence among nursing interns in a tertiary care setting in Odisha, India. Sixty nursing interns were selected through purposive sampling and allocated equally to intervention and control groups. The intervention was designed in alignment with updated Neonatal Resuscitation Program (NRP) guidelines and comprised interactive lectures, skill demonstrations, hands-on manikin practice, scenario-based simulations, and formative feedback. Pre- and post-test assessments using a structured, validated questionnaire and self-confidence scale revealed a significant increase in mean knowledge scores in the intervention group (from 14.5 ± 3.2 to 22.8 ± 2.5, p < 0.001), whereas no significant improvement was seen in the control group (p > 0.05). The findings confirm that structured CBT significantly enhances both cognitive and procedural competencies in neonatal resuscitation. These results underscore the need for embedding CBT approaches within internship curricula to bridge the gap between theory and clinical practice.

Keywords:

Competency-based teaching, neonatal resuscitation, nursing interns, quasi-experimental study, skill development, nursing education, NRP

Refference:

I. World Health Organization. ‘Newborn Mortality.’ WHO, 2024.
https://www.who.int/news-room/fact-sheets/detail/newborn-mortality.
II. Mohamed, S. M., et al. ‘Effect of Digital Detox Program on Electronic Screen Syndrome Among Preparatory School Students.’ Nursing Open, vol. 10, no. 4, 2023, pp. 2222–2228. https://doi.org/10.1002/nop2.1472.
III. American Academy of Pediatrics. ‘Neonatal Resuscitation Program, 8th Edition.’ American Academy of Pediatrics, 2022.
IV. Rani, P. L., and G. M. Buvaneswari. ‘Digital Detoxification Among Late Adolescence—Need of the Hour.’ International Journal of Health Sciences (IJHS), 2022, pp. 6560–6572. https://doi.org/10.53730/ijhs.v6ns1.6402.
V. Ramadhan, R. N., et al. ‘Impacts of Digital Social Media Detox for Mental Health: A Systematic Review and Meta-Analysis.’ Narra Journal, vol. 4, no. 2, 2024, p. e786. https://doi.org/10.52225/narra.v4i2.786.
VI. American Heart Association. ‘Highlights of the 2020 American Heart Association Guidelines for CPR and ECC.’ 2020.
VII. Wyckoff, M. H., et al. ‘Part 13: Neonatal Resuscitation: 2020 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care.’ Circulation, vol. 142, no. 16_suppl_2, 2020, pp. S524–S550.
VIII. Weiner, G. M., et al. ‘Textbook of Neonatal Resuscitation (NRP), 8th Edition.’ American Academy of Pediatrics, 2021.
IX. Patel, D., et al. ‘Competency-Based Simulation Training in Neonatal Resuscitation: A Randomized Controlled Trial.’ Journal of Neonatal Nursing, vol. 28, no. 2, 2022, pp. 110–118.
X. Singh, A., et al. ‘Effectiveness of Structured Teaching Program on Neonatal Resuscitation Among Nursing Students.’ Indian Journal of Pediatrics, vol. 89, 2022, pp. 234–239.

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Design and Optimization of a Wireless Antenna-Based Glucose Sensor for Non-Invasive Monitoring

Authors:

Omar Dayyeni, Omer Saad Abdulqader Abdulwahab

Abstract:

This study presents the design, simulation, and experimental validation of a wireless glucose sensor based on a microstrip patch antenna operating in the 2.4 GHz ISM band. The sensor detects glucose concentrations by observing dielectric property variations in aqueous solutions. A thorough literature review highlights recent advances in wireless and RF glucose monitoring. Our results demonstrate high sensitivity, good linearity, and excellent potential for wearable glucose monitoring. We did parametric studies in this paper about glucose sensitivity by antenna.

Keywords:

Wireless glucose sensor, Glucose concentrations, Dielectric property, Aqueous solutions

Refference:

1. Carr, A. R., Y. J. Chan, and N. F. Reuel. “Contact-Free, Passive, Electromagnetic Resonant Sensors for Enclosed Biomedical Applications: A Perspective on Opportunities and Challenges.” ACS Sensors, vol. 8, no. 3, 2023, pp. 943–55. 10.1021/acssensors.2c02552.
2. CST Studio Suite 2023. Dassault Systèmes, 2023.
3. Fang, Zhongyuan, et al. “A Review of Emerging Electromagnetic-Acoustic Sensing Techniques for Healthcare Monitoring.” IEEE Transactions on Biomedical Circuits and Systems, vol. 16, no. 6, 2022, pp. 1075–94. 10.1109/TBCAS.2022.3226290.
4. Kim, J., et al. “Flexible Wireless Glucose Monitoring System Using Patch Antenna Sensors.” IEEE Sensors Journal, vol. 19, no. 13, 2019.
5. Raj, S., et al. “An Electromagnetic Band Gap-Based Complementary Split Ring Resonator Loaded Patch Antenna for Glucose Level Measurement.” IEEE Sensors Journal, vol. 21, no. 20, 2021, pp. 22679–87. 10.1109/JSEN.2021.3107462.
6. Wang, G., et al. “Dielectric Characterization of Glucose Solutions for Non-Invasive Glucose Monitoring with RF Sensors.” Sensors, vol. 20, no. 3, 2020.

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Case Study on Complexities of Adult ADHD

Authors:

Anwesha Ghara

Abstract:

This case study looks at the challenges faced by a 27-year-old woman diagnosed with adult ADHD after struggling for 13 years with dysthymia and generalized anxiety disorder. The patient showed symptoms such as inattention, restlessness, impulsivity, and emotional instability, which often blurred the lines with mood and anxiety disorder symptoms, making diagnosis tricky. Assessment tools like the Adult ADHD Self-Report Scale (ASRS), Hamilton Anxiety Scale (HAM-A), and Bender Gestalt Test (BGT) were used to clarify the diagnosis. After a structured six-month treatment plan that included medication and Mandala Art Therapy, the patient showed significant improvements in attention, emotional stability, and anxiety management. This case emphasizes the difficulties in diagnosing adult ADHD, the importance of thorough assessment, and the benefits of combined therapeutic methods. It also highlights the need for greater awareness and holistic treatment options for individuals with coexisting psychiatric issues.

Keywords:

Adult-ADHD, Mandala Art therapy, integrated therapy, anxiety disorders

Refference:

1. Kessler, R. C., Adler, L., Barkley, R., Biederman, J., Conners, C. K., Demler, O., Faraone, S. V., Greenhill, L. L., Howes, M. J., Secnik, K., Spencer, T., Ustun, T. B., Walters, E. E., & Zaslavsky, A. M. (2006). The Prevalence and Correlates of Adult ADHD in the United States: Results From the National Comorbidity Survey Replication. American Journal of Psychiatry, 163(4), 716–723. https://doi.org/10.1176/ajp.2006.163.4.716
2. Nimmo-Smith, V., Merwood, A., Hank, D., Panagiotidi, M., Asherson, P., & Sonuga-Barke, E. (2020). Non-pharmacological interventions for adult ADHD: a systematic review. Psychological Medicine, 50(4), 529–541. https://doi.org/10.1017/S0033291720000069
3. Faraone, S. V., Biederman, J., & Mick, E. (2006). The age-dependent decline of attention deficit hyperactivity disorder: A meta-analysis of follow-up studies. Psychological Medicine, 36(2), 159–165. https://doi.org/10.1017/S003329170500471X
4. Young, S., Asherson, P., Lloyd, T., Absoud, M., Arif, M., Colley, W., & Das, D. (2021). Failure of healthcare provision for attention-deficit/hyperactivity disorder in the UK: A consensus statement. BMJ Evidence-Based Medicine, 26(6), 161–164. https://doi.org/10.1136/bmjebm-2020-111507

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Harnessing AI and Big Data for Nursing Research: Opportunities, Challenges, and Ethics

Authors:

Sinjit Mukherjee

Abstract:

Artificial Intelligence (AI) and Big Data represent two of the most influential technological innovations in modern healthcare. Their application in nursing research goes beyond simple data analysis, offering predictive modeling, real-time clinical decision support, and advanced patient care optimization. Nursing, being the backbone of healthcare delivery, produces vast amounts of structured and unstructured data such as patient records, nurse–patient interactions, monitoring device outputs, and administrative logs. AI algorithms can detect hidden patterns, while Big Data platforms ensure scalability and efficiency in handling millions of records. Together, they provide new opportunities for nursing students, educators, and practitioners to enhance patient safety, reduce workload, and improve evidence-based practice. This study critically explores the roles, benefits, methodologies, and challenges of AI and Big Data in the domain of nursing research, while also presenting examples, calculations, and graphs to illustrate their impact on healthcare outcomes.

Keywords:

Artificial Intelligence, Big Data, Nursing Research, Predictive Analytics, Machine Learning, Patient Care, Healthcare Technology

Refference:

1. Topaz, Maxim, and Lisiane Pruinelli. “Big Data and Nursing: Implications for the Future.” Journal of Nursing Scholarship, vol. 52, no. 3, 2020, pp. 263–71. Wiley. 10.1111/jnu.12557.
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4. McBride, Susan, Mari Tietze, and Charlotte Robichaux. “Big Data Analytics and Nursing Research: Current Trends and Future Directions.” Computers, Informatics, Nursing, vol. 40, no. 1, 2022, pp. 8–14. Wolters Kluwer. 10.1097/CIN.0000000000000807.
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