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All Journal CRJIM (Clinical and Research Journal in Internal Medicine) Jurnal Ilmiah Computer Science
Tahir, Anoosha
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Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Nursing Public Health Other

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BI-RADS Category Prediction from Mammography Images and Mammography Radiology Reports Using Deep Learning: A Systematic Review Shiwlani, Ashish; Ahmad, Ahsan; Umar, Muhammad; Dharejo, Nasrullah; Tahir, Anoosha; Shiwlani, Sheena
Jurnal Ilmiah Computer Science Vol. 3 No. 1 (2024): Volume 3 Number 1 July 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i1.31

Abstract

Women's health and mortality are significantly threatened by breast cancer, underscoring the importance of timely detection and treatment. Mammograms are an extremely useful and trustworthy diagnostic tool for early detection and screening of breast cancer. Mammograms based CADe systems have helped doctors in predicting BI-RADS categories and make better decisions and have somewhat reduced diagnostic errors. As deep learning algorithms advance, deep learning-based CADe systems become a practical means of resolving these problems and greatly improving the accuracy. The purpose of this review is to discuss the current techniques that have been developed for BI-RADS category classification in the fields of deep learning and convolutional neural networks. Additionally, the paper demonstrates the progression of models introduced in the past ten years. It also discusses the shortcomings of models proposed in the literature for the prediction of BI-RADS categories from mammography radiology reports and mammography images, in addition to summarizing the current challenges. Lastly, it proposes a novel multi-modal approach to predict the BI-RADS categories from radiology reports and mammography images.
Hepatocellular Carcinoma Prediction in HCV Patients using Machine Learning and Deep Learning Techniques Saeed, Fiza; Shiwlani, Ashish; Umar, Muhammad; Jahangir, Zeib; Tahir, Anoosha; Shiwlani, Sheena
Jurnal Ilmiah Computer Science Vol. 3 No. 2 (2025): Volume 3 Number 2 January 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i2.48

Abstract

Hepatitis C virus is the root cause of 78% of hepato-cellular carcinoma cases. Hepatocellular carcinoma (HCC) represents one of the primary causes of liver cancer mortality and incidence. Clinical prediction of HCC in patients suffering with hepatitis C virus infection (HCV) is challenging due to the diagnostic gold standard, liver biopsy, which is an invasive technique with several limitations. Artificial intelligence (AI) technology is being used in clinical research at a larger rate in recent years, and the field of HCC diagnosis is no exception. Several advanced and light-weight machine learning algorithms combined with less invasive blood tests have promising diagnostic potential to diagnose HCC from HCV. Deep learning algorithms are regarded as best methods for handling and processing complex, unstructured and raw data from various modalities, ranging from routine clinical variables i.e., from EMRs and laboratories to high-resolution medical images. This paper offers a thorough analysis of the most current research that has used machine learning and deep learning to diagnose, prognosticate, treat, and predict HCC risk in patients suffering with HCV.
Bee Sting Rhabdomyolysis: A Clinical Case Study Investigating Elevated Procalcitonin Levels Amidst Biochemical Muscle Injury Anwar, Zara; Khakwani, Muhammad Khan; Tahir, Anoosha
Clinical and Research Journal in Internal Medicine Vol. 6 No. 1 (2025): Volume 6 No 1, May 2025
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.crjim.2025.006.01.12

Abstract

Bee stings, although a nuisance, seem easy to deal with, causing only mild symptoms in the majority of people, but severe systemic reactions, like anaphylaxis or rhabdomyolysis, can rarely happen. Here is an unusual case report of a 75-year-old man with over a hundred bee stings, which resulted in elevated creatinine phosphokinase (CPK) levels, acute kidney injury (AKI), and increased markers of inflammation like C-reactive protein (CRP), white cell count and procalcitonin levels (PCT). Here, we highlight particular attention to procalcitonin, a marker sensitively used to differentiate between bacterial infections and other causes of inflammation. It was significantly elevated to 350 times above its normal upper range despite the patient not exhibiting any signs or symptoms of the disease. The patient made a complete recovery with supportive care, and all his biochemical markers of muscle injury and inflammation and AKI improved to baseline. Here, the authors highlight that clinicians should be aware that PCT can be spuriously elevated in rhabdomyolysis and does not automatically equate to a bacterial infection, re-iterating the need for reasonable antimicrobial use in this situation.
Co-Authors Ahmad, Ahsan Anwar, Zara Dharejo, Nasrullah Jahangir, Zeib Khakwani, Muhammad Khan Muhammad Umar Saeed, Fiza Shiwlani, Ashish Shiwlani, Sheena