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Analisis Pemanfaatan Program Automasi AI-Rad Companion Brain MR untuk Morfometri Otak pada Penyakit Neurodegeneratif Demensia Salis Nurbaiti; Bambang Satoto; Bagus Abimanyu; Diyah Fatmasari; Nanang Sulaksono
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.v12i2.14978

Abstract

Background: : Neurodegenerative diseases such as dementia are characterized by progressive atrophy of the hippocampal structure. Accurate evaluation of brain morphometry is crucial for early diagnosis and monitoring disease progression. Currently, atrophy assessment is performed using the manual Medial Temporal Atrophy (MTA) Score, but this is subjective and depends on the assessor's experience. Advances in artificial intelligence (AI) technology, such as the AI-Rad Companion (AIRC) Brain MR, enable more objective, automated volumetric measurements. This study to analyze the level of correlation between the brain atrophy measurements using MTA Score method and the results of automatic volumetric measurements using the AIRC Brain MR program, as well as to assess the validity of the use of AI in brain morphometry in patients with dementia. Methods: This research was quasi-experimental. The sample was 32 dementia patients who underwent T1-W MPRAGE brain MRI examination at RSPON Dr. Mahardjono. MRI images were assessed visually using the MTA Score (0–4) and measured automatically with AIRC Brain MR. Data were analyzed using the Spearman Rank correlation test to determine the relationship between the MTA score and AI-derived hippocampal volume. Results: The results showed an average MTA score of 3 on both sides, indicating moderate to severe atrophy. The average hippocampal volume measured by AIRC was 3.0 ml (left) and 2.8 ml (right). The Spearman Rank test showed a very strong negative correlation between the MTA score and hippocampal volume (r = −0.898; p-value = 0.000). This means that the more severe the brain atrophy according to visual assessment, the smaller the hippocampal volume calculated automatically by AI. Conclusions: There was a very strong negative correlation between the manual MTA Score method and the automated volumetric measurements of AI-Rad Companion Brain MR in detecting brain atrophy in dementia patients. These results suggest that AIRC can be used as an objective validation tool in the evaluation of brain morphometry in neurodegenerative diseases. The use of AI technology is expected to improve the efficiency, accuracy, and consistency of brain atrophy diagnosis in MRI examinations.
Development of an Orthanc Pacs-Based Radiology Information System (RIS) at The Ilyas Tarakan Type D Navy Hospital Nofa Rosalina; Bambang Satoto; Rasyid Rasyid; Alfisna Fajru Rohmah
Jurnal Health Sains Vol. 7 No. 8 (2026): Journal Health Sains
Publisher : Syntax Corporation Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/jhs.v7i8.2872

Abstract

Digital transformation in radiology management is urgently needed to improve accuracy, efficiency, and compliance with medical data retention standards. However, many type C and D hospitals in Indonesia still rely on paper- and film-based workflows because commercial Picture Archiving and Communication System (PACS) solutions are costly. This research aimed to design, develop, and evaluate an integrated Radiology Information System (RIS) based on the open-source Orthanc PACS at Ilyas Tarakan Naval Hospital, a type D hospital, to address radiology data fragmentation, limited Digital Imaging and Communications in Medicine (DICOM) storage on modality consoles, and paper-based reporting workflows. A Research and Development (R&D) approach was employed using the Rapid Application Development (RAD) model, consisting of a preliminary study, system development, expert validation through black-box testing, and limited pilot testing using a pre-test/post-test design involving ten respondents. The RIS was developed using Laravel and Bootstrap and integrated with Orthanc PACS through a Modality Worklist (MWL) mechanism. System performance was analyzed using normality, homogeneity, and Mann–Whitney statistical tests. Expert validation showed 100% validity across functionality, performance, user interface, security, compatibility, error handling, and usability aspects, while the integration aspect could not be evaluated due to hospital policy constraints. Pilot testing demonstrated a significant improvement in usability across all seven measured dimensions, with a Mann–Whitney test result of p = 0.000 (p < 0.05). The Orthanc-based RIS-PACS provided an effective and low-cost model for digitizing radiology services in budget-constrained type D hospitals and is recommended for gradual integration with the hospital information system.