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Jurnal Imejing Diagnostik
Published by Poltekkes Kemenkes Semarang
ISSN : 2356301X     EISSN : 26217457     DOI : -
Core Subject : Health,
Health Professions Medicine & Pharmacology
Jurnal Imejing Diagnostik (JImeD) memuat tulisan ilmiah dalam bidang radiologi berupa hasil penelitian dan non penelitian (konseptual). Jurnal Imejing Diagnostik (JImeD) terbit 2 kali dalam satu tahun yaitu pada bulan Januari dan Juli oleh Jurusan Teknik Radiodiagnostik dan Radioterapi, Politeknik Kesehatan Kemenkes Semarang. Jurnal Imejing Diagnostik (JImeD) memuat artikel ilmiah dalam bidang radiologi, meliputi : radiografi konvensional, digital radiografi, CT scan, MRI, kedokteran nuklir, radioterapi dan ilmu lainnya yang berkaitan dengan radiologi.
Arjuna Subject : -
Articles 294 Documents
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Reproduksibilitas Biomarker Radiomics pada PET Molecular imaging: A Systematic Literature Review ADITYA PRATAMA PUTRA
Jurnal Imejing Diagnostik (JImeD) Vol. 12 No. 2 (2026): JULY 2026
Publisher : Poltekkes Kemenkes Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31983/jimed.v12i2.15316

Abstract

Background: Radiomics has emerged as a promising quantitative approach in PET molecular imaging; however, its clinical translation remains limited by concerns regarding feature reproducibility under varying technical conditions. This systematic literature review aimed to identify and synthesize the main technical factors influencing the reproducibility of PET radiomic biomarkers. Methods: A systematic literature Review was conducted using the Scopus database with the search strategy TITLE-ABS-KEY (radiomics AND (PET OR PET/CT) AND (reproducibility OR "test-retest")). Fifteen eligible studies were included and narratively synthesized according to major technical domains: segmentation, motion and acquisition/reconstruction, resampling and discretization, scanner variability and harmonization, and phantom-based quality assurance. Results: The findings consistently indicate that segmentation strategy, respiratory motion, reconstruction parameters, voxel resampling, intensity discretization, and inter-scanner variability are major determinants of PET radiomic feature variability. Shape-based features and selected texture features, particularly gray-level co-occurrence matrix (GLCM) features, generally demonstrated higher repeatability than intensity-based and more complex texture features. While harmonization techniques, including AI-based approaches, and standardized phantom-based quality assurance protocols show promise for improving reproducibility, their generalizability remains insufficiently validated. Conclusions: PET radiomic biomarker reproducibility is highly sensitive to methodological variability across the imaging pipeline. Standardized acquisition protocols, robust feature selection, harmonization strategies, and routine phantom-based quality assurance are essential prerequisites before reliable clinical implementation. Further multicenter validation studies are needed to establish reproducible and clinically transferable PET radiomic biomarkers.
Kecerdasan Buatan dalam Transformasi Interpretasi Radiologi: Tinjauan Sistematis tentang Akurasi Diagnostik, Efisiensi Pembacaan, dan Interaksi Manusia–AI DADANG NUGROHO; Yusuf Alam Romadhon; Yuli Kusumawati
Jurnal Imejing Diagnostik (JImeD) Vol. 12 No. 2 (2026): JULY 2026
Publisher : Poltekkes Kemenkes Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31983/jimed.v12i2.15346

Abstract

Background: Artificial Intelligence (AI) has fundamentally transformed the paradigm of radiological image interpretation. Deep learning capabilities offer opportunities for improved diagnostic accuracy, reduced reading time, and minimized inter-reader variability. However, comprehensive evidence regarding AI’s impact across imaging modalities still requires systematic synthesis, particularly concerning which readers benefit most and under what conditions. This review aimed to synthesize scientific evidence on AI’s impact on diagnostic accuracy, interpretation time, diagnostic confidence, and the factors shaping human–AI interaction in radiology interpretation. Methods: A systematic review was conducted following the PRISMA 2020 guidelines. Searches were performed on PubMed/MEDLINE, Scopus, Google Scholar, and IEEE Xplore for studies published between 2021 and 2025. Of 847 identified records, 8 studies met the inclusion criteria as controlled empirical studies, comprising multi-reader, prospective observational, comparative retrospective, and experimental designs. Methodological quality and risk of bias were appraised using the QUADAS-2 tool. Results: The eight included studies covered mammography, chest radiography, computed tomography, magnetic resonance imaging, angiography, and musculoskeletal radiography, encompassing more than 100,000 image readings. AI consistently improved diagnostic sensitivity, with increases ranging from 11.4% to 23.0% across studies, shortened reading time for normal cases, and enhanced diagnostic confidence among readers. Junior radiologists and non-radiologist clinicians consistently benefited the most, suggesting AI functions as a diagnostic equalizer. However, the same group also showed greater susceptibility to automation bias, and AI performance was conditional on model accuracy, explanation design, and technical reliability across subpopulations. Conclusions: AI consistently improves radiological diagnostic performance without compromising reader productivity, with the magnitude of benefit conditional on model accuracy, explanation design, and reader expertise. Implementation in the Indonesian health system should prioritize local validation, AI literacy training for health workers, and integration into JKN/BPJS-based teleradiology models to address the chronic shortage and maldistribution of radiologists. Keywords: Artificial Intelligence; Deep Learning; Diagnostic Accuracy; Medical Image Interpretation; Radiology
Kajian Keselamatan Paparan Radiasi Penggunaan Dental Sinar-X di Rumah Sakit Akademis Kota Makassar Muh Rusli
Jurnal Imejing Diagnostik (JImeD) Vol. 12 No. 2 (2026): JULY 2026
Publisher : Poltekkes Kemenkes Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31983/jimed.v12i2.15440

Abstract

Background: This study was designed to evaluate radiation-safety conditions related to exposure from dental X-ray procedures at the Radiology Unit of Akademis Hospital Makassar. In routine clinical practice, variations in distance, exposure time, and positioning can influence the amount of scattered radiation received by surrounding areas. Given the importance of maintaining exposure levels within recommended safety limits, an assessment of dose accuracy at different measurement points was carried out. Methods: Radiation measurements were conducted at a fixed tube potential of 70 kV and a tube current of 9 mA. Dose readings were taken at four distances (1 m, 2 m, 3 m, and 4 m) and under four exposure-time settings (0.15 s, 0.19 s, 0.23 s, and 0.27 s). Measurements were also performed from four directions relative to the X-ray source—front, left, right, and rear. All data were recorded using standardized dosimetric procedures to determine which combination yielded optimal dose accuracy. Results: The analysis showed that the most favorable dose accuracy was obtained at a distance of 3 meters with an exposure time of 0.19 seconds, measured from the posterior direction. Under these conditions, the recorded dose was 0.87 µSv/h, representing the most stable and reliable value among all tested variations. Conclusions: The findings indicate that maintaining a distance of at least 3 meters, combined with appropriate exposure-time selection, can significantly enhance dose accuracy and improve radiation-safety conditions in dental X-ray environments. These results highlight the importance of optimizing positioning and exposure parameters to support safer radiographic practice. Keywords: Dental X-ray; radiation safety, dose accuracy, exposure parameters, scattered radiation; dosimetry; distance variation
Analisa Kemampuan Deep Learning Untuk Deteksi Penyakit Paru Obstruktif Kronik (PPOK) Menggunakan Citra CXR Di RSP Ario Wirawan Salatiga Lilik Lestari; Susilo Susilo; Rudi Setiawan
Jurnal Imejing Diagnostik (JImeD) Vol. 12 No. 2 (2026): JULY 2026
Publisher : Poltekkes Kemenkes Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31983/jimed.v12i2.15569

Abstract

Background: Chronic Obstructive Pulmonary Disease (COPD) represents a monumental global public health challenge, characterized by progressively worsening airflow limitation, high mortality, and substantial morbidity rates. Early and accurate detection plays a pivotal role in managing patient deterioration and improving quality of life. Traditional diagnosis in primary and secondary care heavily relies on expert interpretation of chest X-rays to detect subtle signs of hyperinflation and rule out comorbidities. However, this manual process is notoriously time-consuming, prone to inter-observer variability, and subjective. Consequently, this study aims to develop, optimize, and rigorously evaluate an automated computational detection system for COPD using a tailored Convolutional Neural Network (CNN) architecture based on standard digital X-ray imaging. Methods: A quantitative, experimental computational approach was utilized with a dataset consisting of 276 chest radiograph images. The dataset was partitioned into 143 training images (100 Normal, 43 COPD) and 133 testing images (100 Normal, 33 COPD), deliberately maintaining a class imbalance to reflect real-world clinical prevalence. The CNN architecture was systematically evaluated across multiple hyperparameters, specifically training epochs and learning rates, to identify the absolute optimal model configuration for feature extraction and classification. Model performance was comprehensively measured using accuracy, sensitivity, and specificity metrics derived from confusion matrices. Results: The empirical results demonstrated that the deep learning model achieved its highest testing accuracy of 98.5% at epoch 20 when paired with a learning rate of 0.1. At this optimal convergence state, the model demonstrated exceptional discriminatory power, yielding a sensitivity of 0.98 and a flawless specificity of 1 for the normal class. Conversely, for the critical COPD class, it achieved a sensitivity of 1 (zero false negatives) and a specificity of 0.98. Conclusions: In conclusion, the implemented and optimized CNN architecture provides a highly accurate, robust, and rapid computational tool for COPD screening. With its perfect sensitivity for detecting pathological features, this system holds significant potential for integration as a clinical decision support system, particularly assisting clinicians in rural, high-volume, or under-resourced hospital environments.

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