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All Journal Jurnal Administrasi Kesehatan Indonesia MEDISAINS Proceedings of the International Conference on Applied Science and Health Global Health Management Journal Journal of Applied Health Management and Technology MAHESA : Malahayati Health Student Journal
Donny Kristanto Mulyantoro
Balai Litbang GAKI
Dentistry Education Health Professions Medicine & Pharmacology Nursing Public Health

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The design of radiology viewing box using charger system and potentiometer Diartama, Anak Agung Aris; Suswaty, Susy; Priantoro, Win; Sugiyanto; Sudiyono; Anwar, M. Choiroel; Latifah, Leny; Santjaka, Aris; Amri, Faisal; Mulyantoro, Donny Kristanto
GHMJ (Global Health Management Journal) Vol. 1 No. 1 (2017)
Publisher : Yayasan Aliansi Cendekiawan Indonesia Thailand (Indonesian Scholars' Alliance)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Background: In the process of work to gain the maximum results, a radiologist needs a viewing box tool to read radiographs. Therefore, the authors want to develop a viewing box tool, which in general the work if this tool resembles the factory manufactured tool. The viewing tool box made can adjust the intensity of the light produced. It uses batteries as a charger system, so that the viewing box can be used anywhere, especially areas that have not been reached by electricity.Aims: This study aimed to create a viewing box tool by using a potentiometer system and charger system.Methods: This study used applied research method by creating and using the design of viewing box tool by using a potentiometer and charger system. Using a Lux meter, the tool's feasibility and the quality of potentiometer system were assessed by 15 respondents consisting of five radiologists and 10 radiographers who should fulfill the questionnaire form.Results: The results of the questionnaire showed that 100% radiologist gave an A (excellent) and expressed that the viewing box tool created could be used properly and 90% radiographers provided an A (excellent) and expressed that the viewing box tool created could be used properly, while 10% radiographer gave a value of B (moderate).Conclusion: The proposed viewing box tool could be used properly and obtained optimal results as a tool in reading radiographs. Potentiometer system contained in the viewing box was very helpful in reading radiographs. Keywords: Viewing box, Radiology, Potentiometer, Charger Submitted: 3 May 2017, Accepted: 22 June 2017 DOI: https://doi.org/10.35898/ghmj-1196
BENEFITS OF STEEPING BLACK TEA AS A NEGATIVE CONTRAST MEDIUM ON CT UROGRAPHY EXAMINATION Yudha, Sagita; Hadisaputro, Suharyo Hadisaputro; Ardiyanto, Jeffri; Mulyantoro, Donny Kristanto; Masrochah, Siti
Journal of Applied Health Management and Technology Vol. 2 No. 2 (2020): April 2020
Publisher : Postgraduate Program , Poltekkes Kemenkes Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31983/jahmt.v2i2.5697

Abstract

The use of water as a contrast medium requires large amounts of water to fill the lumen of the Urinary Tractus and more water is reabsorbed by the body than is secreted into urine. Steeping Black tea contains Caffeine which is able to increase blood flow in the kidneys thus inhibiting the process of absorption of Na, Ca and Mg causing stimulation of the kidneys to increase the amount of urine production. The purpose of this study is to prove that drinking black tea can increase urine production as a negative contrast medium to see differences in the distension and density of the Urinary Tract on CT Urography examination. This type of research uses True Experimental with Pretest-Posttest Control Group Design research design. Patients selected by Simple Random Sampling. Analysis: Paired t test and Independent t test. The results of the study of the use of 600 ml steeping Black Tea as a negative contrast medium on CT Urography examination did not show the difference in mean difference between the left renal Pelvis p value 0.956, Left UVJ 0.640, Right UVJ 0.935 while on the right renal Pelvis p value 0.001 showed differences in mean difference between the left renal Pelvis p value 0.956, Left UVJ 0.640, Right UVJ 0.935 while on the right renal Pelvis p value 0.001 intervention and control group. Hasil pengukuran p value  densitas Vesika urinaria sebesar 0,678. Conclusion: Black tea can be used as a negative contrast medium on CT Urographic examination but when compared with mineral water it does not show a significant difference.
TECHNICAL APPLICATION OF DENOISING KALMAN FILTER FOR ARTIFACT REDUCTION IN MRI ANATOMIC IMAGE INFORMATION Puspitaningtyas, Dyah Ayu; Mulyantoro, Donny Kristanto; Sudiyono, Sudiyono
Journal of Applied Health Management and Technology Vol. 3 No. 1 (2021): January 2021
Publisher : Postgraduate Program , Poltekkes Kemenkes Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31983/jahmt.v1i1.6571

Abstract

Fatsups and BLADE sequences are used to reduce artifacts and clarify anatomical images. Basedon theoretical studies, the STIR, BLADE sequences, the addition or subtraction of parameters, and theaddition of artificial intelligence used still have a weakness, namely increasing the scanning time to belonger. Another technique that can be used and sequences and parameters on MRI is the denoisingKalman filter technique in the Matlab (Matrix Laboratory) program. The denoising technique is appliedafter the scanning process. Denoising will not increase the MRI scanning time. This systematic reviewaims to know the technical application of denoising Kalman filter for reduction artifacts on MRIexamination. The search was done using google scholar, WILEY, IEE Explore, SPRINGER,PERPUSNAS, and Scopus in English with 2004-2020 articles period. The keywords are MRI artifact,reducing artifacts, and the Kalman filter algorithm. A review of 4 articles of filter Kalman intervention onMRI Brain, MRI Abdomen, and MR Cardiac shows that the Kalman filter is good enough to reduceartifacts and improve anatomical information. The Kallman filter could reduce flow artifacts, improveimage quality and clarify anatomical images on MRI.
Evaluasi Efektivitas Model U-Net untuk Segmentasi Citra Renal Scintigraphy pada Penilaian Fungsi Ginjal Gitawiarsa, I Putu Pande Wahyu; Guruh, Bambang; Dartini, Dartini; Mulyantoro, Donny Kristanto; Masrochah, Siti; Wibowo, Gatot Murti
MAHESA : Malahayati Health Student Journal Vol 6, No 4 (2026): Volume 6 Nomor 4 (2026)
Publisher : Universitas Malahayati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/mahesa.v6i4.22074

Abstract

ABSTRACT Renal scintigraphy is a nuclear medicine procedure commonly used to quantitatively assess kidney function and monitor various clinical conditions. Image analysis requires segmentation of the kidney’s region of interest (ROI), which is typically performed manually by experienced operators. This manual approach is time-consuming and prone to inter-observer variability. This study develops and evaluates a Convolutional Neural Network (CNN) U-Net model to perform automated ROI segmentation of the kidneys in Tc-99m DTPA–based renal scintigraphy images. The image dataset underwent preprocessing, normalization, and data augmentation, and was then split into training, validation, and testing sets. Model performance was evaluated using the Dice Coefficient on both validation and testing datasets. The results showed an average Dice Coefficient of 0.900 on the validation set and 0.889 on thetesting set. Frame-by-frame analysis demonstrated stable model performance across all acquisition phases, with Dice Coefficient values ≥ 0.87. These findings demonstrate that the U-Net model can accurately and consistently segment kidney ROIs, and has the potential to be integrated into clinical decision-support systems to enhance the efficiency and consistency of renal scintigraphy interpretation. Keywords: U-Net, Medical Image Segmentation, Renal Scintigraphy, Nuclear Medicine, Deep Learning.  ABSTRAK Renal scintigraphy merupakan prosedur kedokteran nuklir yang umum digunakan untuk menilai fungsi ginjal secara kuantitatif dan memantau berbagai kondisi klinis. Proses analisis citra memerlukan segmentasi region of interest (ROI) ginjal, yang umumnya dilakukan secara manual oleh operator berpengalaman. Metode manual ini memakan waktu dan rentan terhadap variabilitas antar- pengamat. Penelitian ini mengembangkan dan mengevaluasi model Convolutional Neural Network (CNN) U-Net untuk melakukan segmentasi otomatis ROI ginjal pada citra renal scintigraphy berbasis radiofarmaka Tc-99m DTPA. Dataset citra yang digunakan melalui proses pra-pemrosesan, normalisasi, dan data augmentation, kemudian dibagi menjadi data latih, validasi, dan uji. Evaluasi kinerja model menggunakan metrik Dice Coefficient pada dataset validasi dan uji. Hasil menunjukkan nilai rata-rata Dice Coefficient sebesar 0,900 pada data validasi dan 0,889 pada data uji. Analisis per frame menunjukkan stabilitas performa model di seluruh faseperekaman, dengan Dice Coefficient ≥0,87. Temuan ini membuktikan bahwa model U-Net mampu melakukan segmentasi ROI ginjal secara akurat dan konsisten, serta berpotensi diintegrasikan dalam sistem pendukung keputusan klinis untuk meningkatkan efisiensi dan konsistensi interpretasi citra renal scintigraphy. Kata Kunci: U-Net, Segmentasi Citra Medis, Renal Scintigraphy, Kedokteran Nuklir, Deep Learning.
Co-Authors Agung Dwi Laksono Aldinda, Tharra Widadari Anak Agung Aris Diartama Anwar, M. Choerul Arif Budiman Aris Santjaka Dartini Dartini, Dartini Diartama, Anak Agung Aris Faisal Amri Gatot Murti Wibowo, Gatot Murti Gitawiarsa, I Putu Pande Wahyu Guruh, Bambang Hadisaputro, Suharyo Hadisaputro Istiqomah, Nursita Jeffri Ardiyanto Khairunnisa, Marizka Kholifah Kholifah Kurnianingsih Kurnianingsih, Kurnianingsih Kusumawardani, Hastin Dyah Laksonowati, Setyo Mahanani Leny Latifah M. Choiroel Anwar M.Pd S.T. S.Pd. I Gde Wawan Sudatha . Mahalul Azam Melyana Nurul Widyawati Muhamad Arif Musoddaq Ni Putu Rita Jeniyanthi NURUL HIDAYAH Priantoro, Win Priantoro, Win Puspitaningtyas, Dyah Ayu Rachmalina Soerachman Rini Indrati, Rini Runjati Sagita Yudha Samarang Siti Masrochah Sri Sumarni Sudiyono Sudiyono Sudiyono SUGIYANTO Sugiyanto - Supriyana Supriyana, Supriyana Susy Suswaty Ta'adi, Ta'adi Taufiq Hidayat