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All Journal Indonesian Journal of Business and Entrepreneurship (IJBE) Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Journal Of Nursing Practice Medica Hospitalia Magna Neurologica Business, Entrepreneurship, and Management Journal
Retnaningsih
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Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Health Professions Immunology & microbiology Medicine & Pharmacology Neuroscience Nursing Public Health

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Journal : Magna Neurologica

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A 22-Years-Old Male with Tuberculoma of the Brain and Spinal Cord with Miliary Tuberculosis Simamora, Rosinondang Deolita; Retnaningsih; Pasmanasari, Elta Diah; Muhartomo, Hexanto
Magna Neurologica Vol. 3 No. 1 (2025): January
Publisher : Department of Neurology Faculty of Medicine Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/magnaneurologica.v3i1.1782

Abstract

Background: Tuberculosis (TB) remains a significant health issue in Indonesia. Central nervous system (CNS) tuberculoma is one of the extrapulmonary TB diseases and accounts for approximately 1% of all cases. The emergence of this disease is primarily associated with a weakened immune system. However, several other factors, such as comorbidities, a history of inadequate TB treatment, and poor nutrition, also play a role in the development of tuberculoma. Case: A 22-year-old male complained of weakness in all four limbs for the past month, accompanied by tingling and numbness from both feet up to the T10-11 dermatome level. The patient has a history of seizures from one year ago, interrupted treatment for military tuberculosis, and malnutrition. An MRI of the head and whole spine with contrast revealed tuberculomas. The patient was treated with medication, including intravenous dexamethasone 5 mg every 8 hours, oral phenytoin 200 mg every 24 hours, and anti-tuberculosis therapy. Discussion: Tuberculoma in the central nervous system is rare, especially multiple tuberculomas co-occurring in the brain and spinal cord. MRI is a sensitive tool for diagnosing tuberculomas, characterized by the presence of a target sign. The combination of corticosteroids, antiepileptic drugs, and an entire course of anti-tuberculosis medications aims to address both the immediate neurological symptoms and the underlying infection. Conclusion: TB can present as lesions in the brain and spinal cord, requiring the ability to correlate clinical manifestations and radiological features to establish a diagnosis and necessitating adequate therapy.
Automated Prediction of Large Vessel Occlusion Using Artificial Intelligence in Non-Contrast Computed Tomography: A Systematic Review and Meta-Analysis Umam, Hamid Faqih; Mustofa, Aila; David Noor Umam; Shabrina Nur Zidny; Dyah Pranani; Retnaningsih
Magna Neurologica Vol. 3 No. 2 (2025): July
Publisher : Department of Neurology Faculty of Medicine Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/magnaneurologica.v3i2.1704

Abstract

Background: Acute ischemic stroke due to large vessel occlusion (LVO) requires rapid identification. Reducing the time to diagnosis and treatment of stroke patients is an important goal to improve clinical outcomes. Non-contrast computed tomography (NCCT) is widely used in clinical practice for suspected stroke patients. Automated analysis using artificial intelligence in NCCT may be a solution to accelerate the early detection of LVO. Objective: To determine the accuracy of artificial intelligence in NCCT to predict LVO. Methods: A systematic literature search was conducted based on the PRISMA flow chart in four databases (PubMed, ProQuest, ScienceDirect, Cochrane Library) until June 2024. Data extraction was performed to evaluate the accuracy of predicting LVO. Quality assessment was performed using QUADAS-2. All data were analyzed using Review Manager 5.4 and MetaDTA 2.0. Results: Five studies involving 4.862 patients were enrolled. The quality of all the studies was high and had a low risk of bias. All studies used different software. Artificial intelligence in NCCT had fairly good accuracy with a sensitivity and specificity of 0.83 (95% CI; 0.78-0.87) and 0.73 (95% CI; 0.52-0.87). NCCT plus clinical status (NIHSS, stroke onset) in two studies slightly improved overall accuracy with a sensitivity and specificity of 0.85 (95% CI; 0.80-0.89) and 0.74 (95% CI; 0.54-0.88). Two studies reported that machine learning took less than two minutes. Conclusion: Artificial intelligence in NCCT was reasonably accurate and took a short time to predict LVO. There are still opportunities for machine learning to improve performance. Further research is still needed.
Co-Authors Andhitara, Yovita Ardhini, Rahmi Arlia Ayu Damayanti Ayu Imamatun Nisa Azzara, Rana Chyntia Baarid Luqman Hamidi David Noor Umam Daynuri Dodik Tugasworo Dwi Febryanto Dyah Pranani DYAN ARINTOWATI Erliza Noor Fitria Handayani Gautama Sastra Waskita Harianto, Erlangga Pradipta Hexanto Muhartomo, Hexanto Injesus, Elioting Bich Jeffri Nagasastra Khairiyah Kamilah Kurnianto, Aditya Machin, Abdulloh Mahendra, Muhammad Chosasih Megawati Simanjuntak Mohammad Fathul Qorib Mustofa, Aila Pasmanasari, Elta Diah Rahmana, Adhitya Saputra, Fandawa Sawal Sartono, Sawal Shabrina Nur Zidny Simamora, Rosinondang Deolita Suhendi Umam, Hamid Faqih Yandri Zatalini, Halimah