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All Journal Jurnal Imejing Diagnostik Journal of Science Technology (JoSTec) Media Ilmiah Kesehatan Indonesia Lontara Journal Of Health Science And Technology
Mirfauddin, Mirfauddin
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Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science Health Professions Medicine & Pharmacology Nursing Public Health Veterinary

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Use Deep Learning for Processing Automation Image DR in Detecting Pneumothorax Mirfauddin, Mirfauddin
Journal of Science Technology (JoSTec) Vol. 6 No. 2 (2024): Journal of Science Technology (JoSTec)
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/jostec.v6i2.1297

Abstract

Pneumothorax is condition medical serious thing that happened consequence accumulation air in the pleural cavity, which can cause the collapse lungs and potentially threaten soul If No quick handled. A quick and accurate diagnosis is essential. For determine action proper medical. Digital radiography (DR) is one of the method the most common imaging used in detect pneumothorax. However, the limitations in manual interpretation by manpower medical can cause misdiagnosis or​ delay in handling. Study This propose approach based on Deep Learning, especially Convolutional Neural Networks (CNN), for automation processing DR image in detect pneumothorax. The model used utilise ResNet-50 and DenseNet-121 architectures with transfer learning techniques for increase accuracy classification. The data used originate from the ChestX-ray14 and SIIM-ACR Pneumothorax Challenge datasets that have been annotated by experts radiology. Research result show that the CNN model was developed reach level accuracy of 92%, with a precision of 90%, a recall of 93%, and an F1-score of 91%. In addition, the technique Grad-CAM visualization is used For increase interpretability of the model with highlight important areas in the image that becomes base decision classification. Implementation of this model No only increase efficiency of pneumothorax diagnosis but can also reduce burden Work power medical as well as increase quality service health . With promising results , research​ This open opportunity For development more carry on in application of AI in the field radiology.
Tinjauan Radioterapi Kanker Serviks: Mengatasi Tantangan Pelayanan Kesehatan Indonesia Mirfauddin, Mirfauddin; Nurbeti, Nurbeti; Harun, Herlinda Mahdania
Lontara Journal of Health Science and Technology Vol. 4 No. 2 (2023): Ilmu dan Teknologi Kesehatan
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Politeknik Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53861/lontarariset.v4i2.395

Abstract

Cervical cancer ranks among the leading causes of death worldwide, with Indonesia experiencing a significant impact, ranking second after breast cancer in the country and eighth in Southeast Asia. The disease affects approximately 500,000 Indonesian women annually, resulting in over 50% are death. Various methods have been developed to treat cancer, one of which is by using radiation therapy or radiotherapy. Based on the International Agency for Research on Cancer (IARC), of the 10.9 million people diagnosed with cancer worldwide each year, around 50% require radiotherapy. The use of radiation for cancer therapy has not been widely used and is still limited in Indonesia. The purpose of this paper is to describe the basic concepts of radiotherapy and radiotherapy techniques that are appropriate in treating cervical cancer. The research method used was a literature search in the form of theory and research data related to the basic concept of radiation and its use in cancer therapy/radiotherapy, especially in cervical cancer. The results of a literature search found that the main goal of radiotherapy is to maximize the therapeutic ratio between Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP). Where the radiation dose received by the tumor is as maximal as possible from the dose prescription determined by a clinical doctor, while the dose that affects healthy organs around the tumor is as minimal as possible. There are 2 methods used in radiotherapy treatment, namely internal radiotherapy (Brachytherapy) and external radiotherapy. The use of appropriate methods for the treatment of cervical cancer is expected to help patients in their treatment and reduce the number of deaths from cervical cancer.
Analisis Dosis Radiasi Pada Kelenjar Tiroid Selama Pemeriksaan Dental Panoramik Di Instalasi Radiologi RSKDGM Provinsi Sulawesi Selatan Darmawan, Zulkifli Tri; Pradana, Alfa; Mirfauddin, Mirfauddin
Jurnal Imejing Diagnostik (JImeD) Vol 11, No 2 (2025): JULY 2025
Publisher : Poltekkes Kemenkes Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31983/jimed.v11i2.13240

Abstract

Background: The thyroid gland is highly sensitive to radiation and can easily be exposed during panoramic dental imaging. Although BAPETEN sets the annual public dose limit at 1 mSv, and ICRP 103 suggests a specific limit of 0.04 mGy per year for the thyroid, the use of personal protective equipment (PPE) is still often neglected in daily practice. This study aimed to assess how much radiation the thyroid receives during panoramic dental examinations without PPE.Methods: This study used a descriptive quantitative approach and was conducted at the Radiology Department of the Regional Dental and Oral Hospital, South Sulawesi. Ten patients undergoing panoramic radiography were selected. The thyroid radiation dose was calculated using five formulas: exposure dose, scatter dose, absorbed dose, equivalent dose, and effective dose. Technical factors such as tube voltage, current, exposure time, and distance to the thyroid were also recorded.Results: The absorbed dose to the thyroid gland obtained from 10 patients ranged from 0.0139 mGy to 0.0239 mGy, with a mean value of 0.0165 mGy and a standard deviation of 0.0035 mGy. These results indicate a relatively narrow distribution despite variations in patient body size and exposure parameters. All measured doses remained well below the annual organ dose limit of 0.04 mGy, as recommended by the International Commission on Radiological Protection (ICRP). However, existing literature has highlighted that even low-dose radiation exposures, when repeated and unshielded, may increase the stochastic risk of developing thyroid cancer—particularly in younger or genetically predisposed individuals. While the current findings confirm that radiation exposure during panoramic dental examinations is within acceptable limits, they also underscore the need for precautionary measures. The results support the practical implementation of the ALARA (As Low As Reasonably Achievable) principle, especially through the consistent use of thyroid shields during panoramic imaging, as a critical component of evidence-based radiation protection strategies.Conclusions: Panoramic dental imaging without thyroid shielding still produces radiation doses that are within acceptable limits. However, it is strongly recommended that PPE be used consistently, along with adherence to the ALARA principle, to ensure maximum patient safety.
ANALISIS MANFAAT DAN TANTANGAN PENERAPAN PICTURE ARCHIVING AND COMMUNICATION SYSTEM (PACS) DI INSTALASI RADIOLOGI Darmawan, Zulkifli Tri; Angraeni, Dian; Mirfauddin, Mirfauddin; Rakhmansyah, A.AR.; Rusli, Muh; Wulandari, Andi Nur Intan; Syuhada, Firdha Adlia; Normawati, Sitti; Musdalifah, Indah
Media Ilmiah Kesehatan Indonesia Vol. 3 No. 3 (2025): SEPTEMBER
Publisher : Pakis Journal Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58184/miki.v3i3.745

Abstract

Penelitian ini bertujuan untuk menganalisis manfaat dan tantangan penerapan sistem Picture Archiving and Communication System (PACS) di Instalasi Radiologi Rumah Sakit Otak dan Jantung Pertamina Royal Biringkanaya. PACS merupakan sistem digital yang menggantikan metode penyimpanan berbasis film konvensional guna meningkatkan efisiensi pelayanan radiologi. Pendekatan yang digunakan adalah deskriptif kualitatif, dengan teknik pengumpulan data melalui kuesioner, wawancara, observasi langsung, dan dokumentasi. Partisipan dalam penelitian ini terdiri dari enam orang radiografer dan satu orang dokter radiologi. Hasil penelitian menunjukkan bahwa PACS memberikan kontribusi signifikan terhadap peningkatan pelayanan. Berdasarkan hasil kuesioner, 62% radiografer menyatakan sangat setuju dan 38% setuju terhadap manfaat PACS. Respons dokter menunjukkan 80% sangat setuju, 10% setuju, dan 10% tidak setuju. Hasil wawancara memperkuat data kuantitatif tersebut, terutama dalam hal kemudahan akses gambar, kecepatan pelaporan, dan efisiensi alur kerja. Meski demikian, tantangan seperti gangguan jaringan dan perlunya pelatihan berkelanjutan masih ditemui. Hasil observasi menunjukkan bahwa infrastruktur PACS seperti komputer dan monitor sudah memadai, serta pelatihan yang diberikan umumnya cukup. Namun, responden menyarankan adanya pembaruan sistem dan pemeliharaan berkala. Secara keseluruhan, PACS terbukti meningkatkan kualitas pelayanan radiologi tetapi optimalisasi sistem masih diperlukan melalui peningkatan infrastruktur dan kapasitas pengguna.
Analisis Dosis Radiasi Pada Kelenjar Tiroid Selama Pemeriksaan Dental Panoramik Di Instalasi Radiologi RSKDGM Provinsi Sulawesi Selatan Darmawan, Zulkifli Tri; Pradana, Alfa; Mirfauddin, Mirfauddin
Jurnal Imejing Diagnostik (JImeD) Vol. 11 No. 2 (2025): JULY 2025
Publisher : Poltekkes Kemenkes Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31983/jimed.v11i2.13240

Abstract

Background: The thyroid gland is highly sensitive to radiation and can easily be exposed during panoramic dental imaging. Although BAPETEN sets the annual public dose limit at 1 mSv, and ICRP 103 suggests a specific limit of 0.04 mGy per year for the thyroid, the use of personal protective equipment (PPE) is still often neglected in daily practice. This study aimed to assess how much radiation the thyroid receives during panoramic dental examinations without PPE.Methods: This study used a descriptive quantitative approach and was conducted at the Radiology Department of the Regional Dental and Oral Hospital, South Sulawesi. Ten patients undergoing panoramic radiography were selected. The thyroid radiation dose was calculated using five formulas: exposure dose, scatter dose, absorbed dose, equivalent dose, and effective dose. Technical factors such as tube voltage, current, exposure time, and distance to the thyroid were also recorded.Results: The absorbed dose to the thyroid gland obtained from 10 patients ranged from 0.0139 mGy to 0.0239 mGy, with a mean value of 0.0165 mGy and a standard deviation of 0.0035 mGy. These results indicate a relatively narrow distribution despite variations in patient body size and exposure parameters. All measured doses remained well below the annual organ dose limit of 0.04 mGy, as recommended by the International Commission on Radiological Protection (ICRP). However, existing literature has highlighted that even low-dose radiation exposures, when repeated and unshielded, may increase the stochastic risk of developing thyroid cancer—particularly in younger or genetically predisposed individuals. While the current findings confirm that radiation exposure during panoramic dental examinations is within acceptable limits, they also underscore the need for precautionary measures. The results support the practical implementation of the ALARA (As Low As Reasonably Achievable) principle, especially through the consistent use of thyroid shields during panoramic imaging, as a critical component of evidence-based radiation protection strategies.Conclusions: Panoramic dental imaging without thyroid shielding still produces radiation doses that are within acceptable limits. However, it is strongly recommended that PPE be used consistently, along with adherence to the ALARA principle, to ensure maximum patient safety.
Use Deep Learning for Processing Automation Image DR in Detecting Pneumothorax Mirfauddin, Mirfauddin
Journal of Science Technology (JoSTec) Vol. 6 No. 2 (2024): Journal of Science Technology (JoSTec)
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/jostec.v6i2.1297

Abstract

Pneumothorax is condition medical serious thing that happened consequence accumulation air in the pleural cavity, which can cause the collapse lungs and potentially threaten soul If No quick handled. A quick and accurate diagnosis is essential. For determine action proper medical. Digital radiography (DR) is one of the method the most common imaging used in detect pneumothorax. However, the limitations in manual interpretation by manpower medical can cause misdiagnosis or​ delay in handling. Study This propose approach based on Deep Learning, especially Convolutional Neural Networks (CNN), for automation processing DR image in detect pneumothorax. The model used utilise ResNet-50 and DenseNet-121 architectures with transfer learning techniques for increase accuracy classification. The data used originate from the ChestX-ray14 and SIIM-ACR Pneumothorax Challenge datasets that have been annotated by experts radiology. Research result show that the CNN model was developed reach level accuracy of 92%, with a precision of 90%, a recall of 93%, and an F1-score of 91%. In addition, the technique Grad-CAM visualization is used For increase interpretability of the model with highlight important areas in the image that becomes base decision classification. Implementation of this model No only increase efficiency of pneumothorax diagnosis but can also reduce burden Work power medical as well as increase quality service health . With promising results , research​ This open opportunity For development more carry on in application of AI in the field radiology.
Co-Authors Angraeni, Dian Darmawan, Zulkifli Tri Harun, Herlinda Mahdania Muh Rusli Musdalifah, Indah Nurbeti, Nurbeti Pradana, Alfa Rakhmansyah, A.AR. Syuhada, Firdha Adlia Wulandari, Andi Nur Intan