Hasil Pencarian  ::  Simpan CSV :: Kembali

"Encompassing the entire spectrum of breast imaging and diagnostics, this acclaimed text provides a systematic and pragmatic guide for all clinicians involved in diagnosing breast disease. The new third edition has been fully updated to include advances in mammography, ultrasound, breast MRI, percutaneous interventions, and emerging technologies, with pros and cons and evidence-based approaches throughout. Special features of the third edition: Coverage of the field, with comprehensive sections on examination procedures and technical requirements; histologic, clinical, and radiologic appearance of a wide range of breast pathologies; results of international screening studies; and much more. Nearly 1,200 clear radiographic images showing normal findings, benign and malignant disorders, and post-traumatic, post-surgical, and post-therapeutic changes to the breast. Innovations in digital mammography, tomosynthesis, and computer assisted detection (CAD); new chapters on imaging of implants, lesions of uncertain malignant potential, developing technologies; and more. A systematic, highly reproducible methodology for detection, diagnosis, and assessment of findings. Easy-to-follow flowcharts for the diagnostic work-up of both typical and atypical cases. Written by world-renowned authorities with decades of clinical experience, this book provides a brilliant orientation to the multimodality diagnostic approach and therapeutic significance of breast imaging findings. It is an essential reference and board review for radiologists, residents and fellows, gynecologists, oncologists, surgeons, technologists, and any other interdisciplinary specialist working to improve outcomes in breast disease"

"D1 Indonesia. Rab1es masih dianggap sebaga1 penyaklt zoonosis yang palmg umum. Hal mi bukan karena JUmla/J kasus kemallan tetap1 jumlah kasus manus1a yang digig1t oleh hewan yang tennfeks1 virus rabies a tau yang d1cunga1. Sebag1an besar kasus rabies manusia yang disebabkan oleh gigitan anjing, selain g1g1tan kucing dan kera. Jika rab1es dapat d1h1/angkan dan anjlng, rabies pada kucmg dan kera juga bisa dihilangkan sebagai rabies spontan di dua binatang langka Rabies d1sebabkan oleh v1rus RNA dari spesies Rhabdovmdae, dan menyerang sistem saraf pusat. Hal ini /Jampir selalu fatal }ika profilaks1s pasca paparan tidak d1berikan sebelum timbulnya ge]ala parah pada orang tidak divaksinasi. Diagnosis didasarkan pada se)arah kontak dekat dengan air liur yang terinfeks1 (melalui g1gitan atau goresan) dan pengembangan tanda-tanda dan ge)ala Geja/a tahap awal adalah demam, malaise, diikut1 oleh agitas1. perilaku nonnal, kecemasan halusmas1 meluas ke de/mum, hipersal1vas1, penyakit anjing gila, aerophobia, gejala neuro/ogis seperli kejang fanng, kelumpuhan. keJang. dan akhimya kematian U)llaboratorium untuk mendeteksi virus rabies dalam air liur dapat dilakukan oleh Reverse transknps1 dukuli oleh Polymerase Chain Reaction (RTIPCR) dan iso/asi v1rus dalam kultur jaringan. Biopsi kulit akar ram but d1 tengkuk dipe1 iksa untuk antigen rabies di saraf kulit di dasar folikel ram but o/eh immunofluoresence pewamaan. Perlakuan setelah terkena adalah pembersihan lesi, pemilahan intradennal anti-rabies imunisasi untuk mempercepat respons imun. anti-rab1es serum, menghentikan proses infeksi, intravena ribavirin, dan interferon a/fa intraventricular, konsentrasi tingg1 pada mfus ketamm untuk menghambat replikasi virus rabies. Akhimya, vaksinasi adalah pencegahan terba1k"

"Latar Belakang : Deteksi infark pada populasi sindroma koroner akut non elevasi segmen ST (SKA-NEST) pada praktik klinis sulit dan menyebabkan kegagalan stratifikasi risiko yang tepat. Pemeriksaan enzim jantung tidak tersedia secara luas, memiliki waktu tunggu yang lama, dan membutuhkan biaya yang tidak sedikit.Tujuan : Mengetahui akurasi dasar dan akurasi paska training kecerdasan buatan Learning Intelligent for Effective Sonography (LIFES) dalam mendeteksi infark miokard pada populasi SKA-NEST berdasarkan gambaran ekokardiografiMetode : Penelitian ini merupakan studi diagnostik yang mengevaluasi kemampuan kecerdasan buatan berbasis deep learning LIFES dalam mendeteksi infark miokard pada pasien SKA-NEST di RSJPDHK pada tahun 2019-2023 berdasarkan gambaran ekokardiografi. Dilakukan transfer learning menggunakan dataset penelitian dan cross validation untuk mengetahui tingkat akurasi model baru paska transfer learning.Hasil : Sebanyak 721 subjek memenuhi kriteria inklusi dan eksklusi dari tahun 2019-2023. 310 diantaranya adalah pasien infark miokard non elevasi segmen ST (IMA-NEST). Sebanyak 67,8 % subjek adalah laki-laki dengan median usia 61 tahun. Median waktu dilakukan ekokardiografi dari admisi adalah tiga hari. Terdapat perbedaan signifikan pada beberapa parameter ekokardiografi pada kelompok infark vs non infark berupa median FEVKi 53% vs 63 % (p < 0,001), median LVEDD 48,8 mm vs 44,6 mm (p < 0,001), median rerata E/E’ 12,0 vs 9,8 (p < 0,001) dan median LAVI 30 ml/m2 vs 26 ml/m2 (p < 0,001). Performa diagnostik LIFES terhadap infark didapatkan paling baik pada tampilan PLAX dengan sensitivitas 88,7 % dan spesifisitas 20,4 % AUC 0,55 pada LIFES fase 2 model 1. Paska transfer learning, model LIFES-MI menghasilkan akurasi terbaik pada tampilan A4C dengan sensitivitas 41,3 % dan spesifisitas 83,7% AUC 0,61.Kesimpulan Model kecerdasan buatan LIFES fase 2 model 1 memiliki sensitivitas yang baik untuk deteksi infark miokard, sedangkan model LIFES-MI memiliki spesifisitas yang baik dalam mendeteksi infark miokard berdasarkan gambaran ekokardiografi pada populasi SKA-NEST.

---

Background: Detecting myocardial infarction in the non-ST segment elevation acute coronary syndrome (NSTEACS) population in clinical practice is challenging and leads to failure in appropriate risk stratification. Cardiac enzyme assays are not widely available, have long waiting times, and incur significant costs.

Objective: To determine the baseline accuracy and post-training accuracy of the Learning Intelligent for Effective Sonography (LIFES) artificial intelligence in detecting myocardial infarction in the NSTEACS population based on echocardiographic findings.

Method: This study is a diagnostic study that evaluates the ability of deep learning-based artificial intelligence LIFES in detecting myocardial infarction in NSTEACS patients at RSJPDHK from 2019 to 2023 based on echocardiographic videos.. Transfer learning was performed using the research dataset and cross-validation to determine the accuracy level of the new model post-transfer learning.

Results: A total of 721 subjects met the inclusion and exclusion criteria from 2019 to 2023. Among them, 310 were non-ST segment elevation myocardial infarction (NSTEMI) patients. 67.8% of the subjects were male with a median age of 61 years. The median time from admission to echocardiography was three days. There were significant differences in several echocardiographic parameters between the infarct and non-infarct groups, including median EF% 53% vs 63% (p < 0.001), median LVEDD 48.8 mm vs 44.6 mm (p < 0.001), median mean E/E' 12.0 vs 9.8 (p < 0.001), and median LAVI 30 ml/m2 vs 26 ml/m2 (p < 0.001). LIFES diagnostic performance for infarction was best achieved in the PLAX view with sensitivity of 88.7% and specificity of 20.4%, AUC 0.55 in LIFES phase 2 model 1. Post-transfer learning, the LIFES-MI model produced the best accuracy in the A4C view with sensitivity of 41.3% and specificity of 83.7%, AUC 0.61.

Conclusion: The Learning Intelligent for Effective Sonography (LIFES) phase 2 model 1 has good sensitivity for detecting myocardial infarction, while the LIFES-MI model has good specificity in detecting myocardial infarction based on echocardiographic findings in the NSTEACS population."