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All Journal Jurnal Imejing Diagnostik
Nanang Sulaksono
(SCOPUS ID: 57194067331), Dept. of Radiology, Poltekkes Kemenkes Semarang
Health Professions Medicine & Pharmacology

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Studi Kasus Pemeriksaan MSCT Urografi Multiphase dengan Klinis Tumor Ginjal Ratna Tri Rahayu; Nanang Sulaksono; Andrey Nino Kurniawan
Jurnal Imejing Diagnostik (JImeD) Vol 9, No 2: JULY 2023
Publisher : Poltekkes Kemenkes Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31983/jimed.v9i2.10402

Abstract

Background: The procedure for examining multi-slice computed tomography (MSCT) multiphase urography with clinical kidney tumors in the Radiology Installation of the Banyumas General Hospital differs from the literature in the use of scans in the corticomedullary phase and the post-contrast scanning phase. This study aims to determine the procedure for MSCT Urography Multiphase examination with clinical kidney tumors, to find out the reasons for using a scan of the corticomedullary phase area from the diaphragm to the pubic symphysis and to find out the reasons for using four phases of post-contrast scanning.Methods: This type of research is qualitative research with a case study approach. Data was collected at the radiology installation at the Banyumas Hospital from January to May 2023, the study respondents consisted of three radiographers, one radiologist, one sending doctor and one radiology nurse. Methods of data collection by observation, in-depth interviews, documentation. Data analysis was carried out through the stages of data collection, data reduction, data presentation and drawing conclusions.Results: MSCT Urography Multiphase examination procedure with clinical kidney tumors in the radiology installation of Banyumas Hospital includes, the patient's supine feet first position, scanning area from the diaphragm to the symphysis pubis, using 50 ml of contrast media with 40 ml of saline flush. Scanning technique by taking the pre-contrast phase, corticomedullary phase, nephrography phase, equilibrium phase and delay phase. Scan the area of the corticomedullary phase from the diaphragm to the symphysis pubis to to simplify the scanning process because the tool protocol has already made full abdominal scan area settings and to evaluate the pattern of abdominal organ enhancement if there is a metastatic urothelial lesion other than the kidney. The use of four phases of post-contrast scanning is because the goals and functions of each scanning phase are different so that the maximum diagnostic information is obtained.Conclusions: The MSCT Urography Multiphase examination procedure with clinical kidney tumors in the radiology installation of Banyumas can provide optimal diagnostic information. 
Penerapan Artificial Intelligence dalam Mendeteksi Batu Ginjal secara Otomatis pada Citra CT Scan Nanang Sulaksono; Ary Kurniawati
Jurnal Imejing Diagnostik (JImeD) Vol 10, No 1: JANUARY 2024
Publisher : Poltekkes Kemenkes Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31983/jimed.v10i1.11072

Abstract

Background: Kidney stones are a clinical condition with the presence of stones along the urinary tract of varying sizes. The aim of this research is the need for a system to automatically detect kidney stones so that it can help radiologists in diagnosing kidney stones accurately, effectively and efficiently, and patients can immediately undergo further action to cure kidney stones.Methods: The difference in research carried out by researchers is the use of artificial intelligence which uses deep learning with a convolutional neural network (CNN) algorithm. This research uses images obtained from CT scan results from public data (Kaggle) and primary hospital data. The number of images used in the Augmentation training data was 2338 normal images and 2390 kidney stone images. The augmentation testing data used 540 normal images and 446 kidney stone images. The research also involved experts, namely radiology specialists, in determining images with abnormal and normal stone tones.Results: research obtained from CT Scan images of kidney stones with augmentation and original using public data/Kaggle images, obtained using augmentation obtained a high accuracy value of 99.69%. Meanwhile, in testing data using primary/hospital data images, augmented data obtained accuracy values that were still low at 45.43% and 45.23%, respectively.Conclusions: The use of deep learning with the CNN model in training data augmentation obtained high accuracy values, however in testing data using hospital CT scan images the accuracy value was still low, but it was able to recognize images of kidney stones, so it could help in automatically diagnosing kidney stones. For future work could involve refining the model to handle variations in hospital data or exploring additional features to improve generalizability.
Co-Authors Andrey Nino Kurniawan Ary Kurniawati Ratna Tri Rahayu