Claim Missing Document
Check
Articles

Found 2 Documents
Search

Analysis of axial T2 TSE images using deep learning reconstruction in MRI of brain tumors Muzdalifah, Nadifah Pratiwi; Utami, Hernastiti Sedya; Hidayat, Fathur Rachman; Wibowo, Kusnanto Mukti; Jadmika, Muhammad Riefki; Samudra, Alan
Science Midwifery Vol 13 No 1 (2025): April: Health Sciences and related fields
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/midwifery.v13i1.1867

Abstract

Magnetic Resonance Imaging (MRI) Brain examinations often encounter uncooperative patients, necessitating rapid scanning techniques that yield optimal results. To address this challenge, advanced technologies such as deep learning can be leveraged to accelerate scan time, reduce noise, and enhance image precision. This study aims to evaluate the disparity in MRI Brain image quality with and without deep learning in tumor cases to achieve superior diagnostic imaging. Employing a quantitative experimental approach, this research analyzed a sample of 30 patients collected from January to February 2025. Three Radiologist Specialists assessed the images using a questionnaire based on the Visual Grading Analysis (VGA) method. The obtained responses were statistically examined through Cohen’s Kappa consistency test and Wilcoxon Signed-Rank Test. Findings revealed a statistically significant difference in image information between deep learning-assisted and conventional MRI scans. In T2 TSE sequences, deep learning reconstruction demonstrated superior anatomical visualization of the Gray Matter, White Matter, Lateral Ventricles, Basal Ganglia, and Parafalx Cerebri. However, in brain tumor pathology visualization, conventional MRI exhibited sharper and more distinct tumor delineation. Although deep learning-enhanced T2 TSE sequences reduced scan duration and improved overall image quality, they provided suboptimal diagnostic information in tumor cases.
Program Literasi Komunitas bagi Pelajar Pulau Soop: Upaya Meningkatkan Akses Pendidikan di Daerah Terpencil Lukman Hakim; Wibowo, Ugung Dwi Ario; Purwanti, Kusuma; Yudistira, Mohammed Rheyhan; Muzdalifah, Nadifah Pratiwi; Achmad, Shafiyah Mirza; Arsyad, Rahmatullah bin; Baho, Irowe Irno
Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Vol. 9 No. 1 (2025): Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35326/pkm.v9i1.6580

Abstract

Barat Daya, yang menghadapi keterbatasan akses pendidikan, bahan bacaan, dan tenaga pendidik. Kegiatan dilakukan oleh mahasiswa Kuliah Kerja Nyata (KKN) 3T kolaborasi Universitas Muhammadiyah Purwokerto dan Universitas Muhammadiyah Sorong. Program kerja yang dilaksanakan meliputi pengoperasian kembali Rumah Pintar sebagai pusat kegiatan literasi, pelaksanaan bimbingan belajar, serta pemberian bantuan belajar secara insidental untuk membantu siswa dalam mengerjakan tugas sekolah. Program ini dilaksanakan selama Agustus 2024 dengan melibatkan sekitar 70 siswa sekolah dasar dan 30–40 siswa sekolah menengah pertama. Pendekatan kualitatif digunakan dalam pelaksanaan program dengan teknik pengumpulan data berupa observasi, wawancara, angket, dan dokumentasi. Hasil kegiatan menunjukkan adanya peningkatan kemampuan anak dalam mengenali huruf dan angka, membaca suku kata sederhana, menulis nama dengan ejaan benar, serta memperbaiki penggunaan tanda baca dan huruf kapital. Kegiatan read aloud dan pendampingan membaca secara rutin berhasil menumbuhkan minat baca, meningkatkan daya imajinasi, dan membangun kepercayaan diri anak dalam memahami bacaan. Program ini menunjukkan bahwa literasi berbasis komunitas dapat menjadi alternatif efektif dalam mendukung peningkatan kualitas pendidikan di daerah terpencil.