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"This book is an ideal introduction to the use of radiology in imaging diseases of the liver, gallbladder and biliary system, pancreas, spleen, and gastrointestinal tract. Each of the ten chapters is devoted to a particular organ and contains ten illustrated case reports drawn from clinical practice. Common clinical situations and indications for imaging are reviewed, and clear descriptions are provided of the various imaging techniques that will assist in resolving diagnostic and therapeutic dilemmas. "

"This book offers a practical approach to the world of diagnostic ultrasound. It has been structured in a reader-friendly, case-based format that makes it easy and enjoyable to learn the basics of the applications and interpretation of ultrasound. Each case includes illustrations, descriptions of the imaging findings, and technical details and serves to identify the essential imaging features of the pathology under consideration, thus assisting the reader in the diagnosis of similar cases. The book is divided into 17 short chapters that review the most important areas of ultrasound application and also document the latest advances in the use of contrast and interventional ultrasound. The authors treat every topic from a “how to do it” perspective with the aim of imparting their wide experience in use of the technique. "

"This introduction to genitourinary and pelvic radiology is a further volume in the learning imaging series. Written in a case-based format, the book is subdivided into ten chapters, kidney, adrenal gland, urinary bladder, collecting system and urethra, prostate and seminal vesicles, scrotum, obstetrics, uterus, cervix and vagina, adnexa and retroperitoneum. Genitourinary radiology has undergone a tremendous change owing to advances in ultrasound, CT and MRI that have redefined our understanding of genitourinary and pelvic pathology. Each chapter includes an introduction and ten case studies with illustrations and comments from anatomical, physiopathological and radiological standpoints and with bibliographic recommendations."

"The chest X-ray (CXR) or chest radiograph remains the most commonly ordered imaging study in medicine, yet paradoxically is often the most complex to learn, recall, and master effective and accurate interpretation. The chest radiograph includes all thoracic anatomy and provides a high yield, given the low cost and single source. This guide presents a structured lexicon for use by readers to reproducibly describe radiographic abnormalities of the chest detected on plain film CXRs. The lexicon is designed to provide readers with clinically significant differentiation of abnormalities detected. The content is structured to relate specific combinations of distinct radiographic findings to classes/groupings of pathological etiologies of those findings. Recognizing the individual findings and identifying their combination or lack of combination with other individual findings allows readers to create effective differential diagnoses that can then be further evaluated using other imaging procedures and/or non-radiographic clinical information. The book includes hundreds of images, including radiographs, CTs, graphics, and analogous models to help teach otherwise complex processes and radiographic principles."

"This book constitutes the refereed proceedings of the International Workshop CCAAI 2012, held in Nice, France, in October 2012. The book includes 31 papers which were carefully reviewed and selected from 37 submissions. All of the accepted papers were revised by incorporating of the reviewers’ comments and re-submitted by the authors to be included in this proceedings volume. The papers are organized into topical sections on colon and other gastrointestinal tract; and liver, kidney, and other organs."

"ABSTRACTIn order to set up a more effective mechanism for revitalizing the Hakka language, the Legislative Yuan passed the amendment of Hakka Basic Act on December 29th, 2017. The revised version of Act institutes Hakka language as the local and teaching language in those "major Hakka cultural areas" (Hakka villages). After examine the revision process of Hakka Basic Act, this paper finds that the revised Act follows the "Local Government Act" and established "Hakka cultural regional cooperation organizations" instead of adopting the idea of "Hakka cultural self-ruling body" advocated by Hakka Affairs Council. This result from both of Ministry of Interior and Hakka Affairs Council tend to build the cultural self-ruling mechanism under the framework of administrative regions, which is according to the article 24-1 of "Local Government Act". For providing references to domestic policy analysis, this paper, which employs the institution of Belgium as a case study, explores the autonomous area of language family and regional self-ruling body in Belgium by the approach of public policy theory and literature review. Moreover, this paper tried to propose a designation of "Hakka language community", which is based on the experience of ethnic self-ruling body in Belgium."

"Vital sign merupakan parameter fisiologis yang penting dalam melihat adanya gangguan pada tubuh seseorang. Maka dari itu kebutuhan peralatan dalam pemeriksaan vital sign sangat tinggi. Saat ini vital sign dapat diketahui dengan cara pemeriksaan non-contact. Pemeriksaan vital sign dengan non-contact dapat menggunakan Photoplethysmography (PPG). Saat ini PPG sendiri telah banyak dikembangkan agar dapat membaca keseluruhan vital sign seperti detak jantung, tekanan darah, dan juga konsenstrasi oksigen di dalam darah (SpO2). Pada penelitian ini dirancang pengembangan PPG dengan bantuan pencitraan dalam membaca vital sign. Dataset yang digunakan pada penelitian ini adalah dataset yang berasal dari pengukuran langsung yang telah dirancang agar dapat diproses menjadi sinyal Imaging Photoplethysmography (IPPG) yang baik. Dataset terdiri dari 13 orang laki-laki dan 17 orang perempuan. Dataset yang didapatkan akan dibagi menjadi beberapa scene yang kemudian diproses dalam metode yang diusungkan yaitu Discrete Fourier Transform (DFT) dan Deep Learning yaitu Convolutional Neural Network (CNN). Hasil penelitian ini berupa nilai RMSE dan MAE dimana saat penggunaan DFT menghasilkan masing masing 3,39 dan 1,38 dan dengan metode CNN arsitektur PhysNet menghasilkan 8,2151 dan 2,5976 untuk detak jantung, 3,3311 dan 1,0534 untuk tekanan darah, serta 3,6044 dan 1,1398 untuk SpO2.

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Vital sign is an important physiological parameter in seeing a disturbance in a person's body. Therefore the need for equipment in vital sign examination is very high. Currently vital signs can be identified with non-contact examination. Examination of vital signs with non-contact can use Photoplethysmography (PPG). Currently PPG itself has been developed a lot so that it can read all vital signs such as heart rate, blood pressure, and also the concentration of oxygen in the blood (SpO2). In this study, the development of PPG was designed with the help of imaging in reading vital signs. The dataset used in this study is a dataset derived from direct measurements that have been designed to be processed into a good Imaging Photoplethysmography (IPPG) signal. The dataset consists of 13 men and 17 women. The dataset obtained will be divided into several scenes which are then processed using the proposed method, namely the Discrete Fourier Transform (DFT) and Deep Learning, namely the Convolutional Neural Network (CNN). The results of this study are RMSE and MAE values where when using the DFT they produce 3.39 and 1.38 respectively and with the PhysNet architecture CNN method they produce 8.2151 and 2.5976 for heart rate, 3.3311 and 1.0534 for blood pressure , and 3.6044 and 1.1398 for SpO2."

"Penggunaan kecerdasan buatan berbasis Deep Learning untuk mendukung prediksi dan pengambilan keputusan sangat populer di banyak bidang. Salah satu bidang tersebut adalah di sektor kesehatan, terutama dalam pengobatan kanker. Banyak ahli onkologi radiasi dan fisikawan medis sedang melakukan penelitian yang menjanjikan dalam histologi dan stadium kanker, prediksi hasil, segmentasi otomatis, perencanaan perawatan, dan jaminan kualitas. Penelitian ini merupakan studi pendahuluan pengembangan dan perbandingan model deep learning yang berfungsi sebagai alat konversi dari nilai piksel citra Electronic Portal Imaging Device (EPID) ke dosis. Data diambil dari dua bidang radioterapi dengan teknik yang berbeda, yang pertama dosimetri transit pada Varian Unique 6MV foton dan dosimetri non-transit pada Varian Halcyon. Selanjutnya karena data yang tersedia hanya sedikit, data tersebut direproduksi dengan teknik augmentasi sehingga data tersebut cukup untuk menjadi data latih pada berbagai model deep learning, hasilnya divalidasi menggunakan indeks gamma 3%/3mm terhadap citra dosis hasil perencanaan dari TPS. Beberapa model deep learning telah berhasil dibuat yang dapat mengubah nilai piksel EPID menjadi distribusi dosis. Pada dosimetri transit telah berhasil dibuat model Convolutional Neural Network (CNN) dengan 6 layer dengan hasil validasi terbaik mencapai 92,40% ± 28,14%. sedangkan pada dosimetri non-transit, model terbaik mencapai tingkat kelulusan gamma indeks rata-rata 90,07 ± 4,96%. Validasi lebih lanjut dalam banyak kasus dan perbaikan perlu dilakukan untuk meningkatkan akurasi kemiripan dengan citra acuan dengan mempertimbangkan karakteristik yang terkandung dalam gambar EPID dan jumlah dataset.

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The use of deep learning to support prediction and decision making is very popular in many areas. Many radiations oncologist and medical physicists are conducting promising research in cancer histology and staging, outcome prediction, automated segmentation, treatment planning, and quality assurance. This research is a preliminary study of the development and comparison of deep learning model that work as a conversion tool from the pixel value of Electronic Portal Imaging Device (EPID) images to dose. Data were taken from two radiotherapy plane with different techniques, the first was transit dosimetry on the Varian Unique 6MV Photon and the second non-transit dosimetry on the Varian Halcyon. Furthermore, due to limited of data source, the data was reproduced by augmentation techniques so that the data was sufficient to become training data on various deep learning models, the results were validated using a gamma index of 3%/3mm compared to the planned dose image from TPS. Several deep learning models has been successfully created that can convert the EPID pixel value into a dose distribution. In transit dosimetry, a Convolutional Neural Network (CNN) model with 6 layers has been successfully created with the best results from the validation reaching 92.40% ± 28.14%. while in non-transit dosimetry, the best model achieves an average gamma passing rate of 90.07 ± 4.96%. Further validation in many cases and improvements need to be made to increase the accuracy of similarity by considering the characteristics contained in the EPID image and the number of datasets."

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Gangguan pendengaran pada umumnya dapat terjadi sejak lahir (tuli kongenital) atau di kemudian hari (tuli didapat). Kedua grup memiliki perbedaan karakteristik yang berdampak pada proses pengobatannya. Oleh karena itu, mengetahui kedua jenis penyakit tersebut terlebih dahulu sebelum melanjutkan ke tindakan selanjutnya adalah sangat penting. Namun, saat ini di Indonesia masih belum ada program skrining yang berjalan untuk mendeteksi gangguan pendengaran pada anak sejak dini. Menanggapi hal tersebut, penelitian ini bertujuan menganalisis data Diffusion Tensor Imaging (DTI) dari kedua jenis pasien tuli untuk dilanjutkan ke proses klasifikasi dan clustering supaya didapat model yang dapat membedakan kedua kondisi tersebut. Pengembangan model dilakukan melalui proses hyperparameter tuning serta percobaan terhadap dataset dengan dan tanpa fitur usia. Selanjutnya, diterapkan juga percobaan terhadap ada atau tidaknya data validasi terpisah. Performa model dianalisis berdasarkan beberapa metrik evaluasi seperti akurasi, presisi, spesifisitas, recall, confusion matrix, skor F1, area under the ROC curve (AUC-ROC), precision-recall curve, dan silhouette score. Hasil analisis secara keseluruhan menunjukkan bahwa performa model menggunakan fitur usia lebih baik, yaitu pada model klasifikasi diperoleh spesifisitas 89.89%, skor F1 91.93%, dan AUC-ROC 88.61%, dan pada model clustering diperoleh nilai silhouette sebesar 0.8524. Analisis tanpa fitur usia menunjukkan bahwa kedua kelompok dapat diklasifikasi, namun tidak berdasarkan kondisi maturasinya, sedangkan hasil clustering menunjukkan pengelompokkan kelas yang berbeda dari klasifikasi. Penelitian ini berpotensi untuk dikembangkan lebih lanjut, terutama jika kedua kelas memiliki rasio dataset yang seimbang.

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In general, hearing disorders can occur since birth (congenital hearing loss) or later in life (acquired hearing loss). Both group has different characteristics that affected the treatment process. Therefore, knowing both types of diseases beforehand before proceeding to further actions is crucial. However, currently in Indonesia, there are no any functional screening programs to detect hearing disorders on children from early ages. In response to this, this study aims to analyze Diffusion Tensor Imaging (DTI) data from both types of deaf patients to proceed to the classification and clustering processes to obtain a model that can differentiate between the two conditions. Model development is conducted through hyperparameter tuning and experimentation with datasets with and without age features. Additionally, we will experiment with the presence or absence of separate validation data. The model's performance is analyzed based on several evaluation metrics such as accuracy, precision, specificity, recall, confusion matrix, F1 score, area under the ROC curve (AUC-ROC), precision-recall curve, and silhouette score. The overall analysis results show that the model performance using age features is better, namely in the classification model, specificity of 89.89%, F1 score of 91.93%, and AUC-ROC of 88.61% are obtained. Meanwhile, in the clustering model, a silhouette score of 0.8524 is obtained. The analysis without age features indicates that both groups can be classified, but not based on their maturation conditions, while the clustering results show different grouping of classes from the classification. This research has the potential for further development, particularly if both classes have a balanced dataset ratio and age data distributed evenly.

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"Recurrent abdominal pain is one of the most common symptoms found in children. Description of abdominal pain is important in determining the etiologic cause. Organic pain must be ruled out first before suspecting psychogenic cause of pain. However; Children and infant are likely having difficulties in describing abdominal pain. Referred pain may lead to misdiagnosis. Alarm symptoms of abdominal pain are important indices and must be recognized. Careful and complete anamnesis and physical examination play critical role in management approach of recurrent abdominal pain in children and determine whether medical therapy only or combination with surgical intervention is considered necessary."