Missing data are a universal data quality problem in many domains, leading to misleading analysis and inaccurate decisions. Much research has been done to investigate the different mechanisms of missing data and the proper techniques in handling various data types. In the last decade, machine learning has been utilized to replace conventional methods to address the problem of missing values more efficiently. By studying and analyzing recently proposed methods using machine learning approaches, vital adoptions in accuracy, performance, and time consumed can be highlighted. This study aimed to help data analysts and researchers address the limitations of machine learning imputation methods by conducting a systematic literature review to provide a comprehensive overview of using such methods to impute missing values. Novel proposed machine learning approaches used for data imputation are analyzed and summarized to assist researchers in selecting a proper machine learning method based on several factors and settings. The review was performed on research studies published between 2016 and 2021 on adopting machine learning to impute missing values, focusing on their strengths and limitations. A total of 684 research articles from various scientific databases were analyzed using search engines, and 94 of them were selected as primary studies. Finally, several recommendations were given to guide future researchers in applying machine learning to impute missing values.INDEX TERMS Systematic literature review, data imputation, data mining, missingness, data preprocessing, data quality.
We reported a rare case of a spontaneous rupture of an intratumoral pseudoaneurysm in a giant renal angiomyolipoma of a 52-year-old lady. The initial presentation was a sudden onset of right hypochondriac pain, nausea, and vomiting. CT scan revealed large heterogenous exophytic enhancing mass with mixed solid and fat density within, arising from the right kidney likely represent a giant right renal angiomyolipoma. There is associated right perinephric hematoma and active bleeding within the mass. No features suggestive of tuberous sclerosis. Subsequent right renal angiogram revealed a pseudoaneurysm of an inferior segmental right renal artery and emergency embolization was done with successful obliteration of the aneurysmal sac and devascularization of the mass.
Bronchial artery embolization was first performed in 1973 by Remy et al with widespread acceptance since then. Multi-detector computed tomography (MDCT) CT angiography (CTA) is currently the gold standard imaging modality used to identify the site and cause of bleeding in patient presented with haemoptysis. Bronchial artery anatomies and precise location can be obtained by scrutinizing CTA prior to interventional procedures. CTA has the advantage of not only can preclude the need of digital subtraction angiography (DSA) in inappropriate cases, but also can shorten the intervention procedure timing. We present a case of false negative bronchial artery caliber seen on MDCT which was abnormal in DSA.
Background: Benign Prostatic Hyperplasia (BPH) is common in aging men with worldwide prevalence at 20-62%, while Malaysian prevalence was 39.3% (2001) and increased at 8% per decade. In surgical treatment of BPH, Trans-Urethral Resection of Prostate (TURP) remains the gold standard. Other surgical options would mostly also require general anesthesia (GA). Therefore, more Local-Anaesthesia (LA) based options should be made available for patients who are not fit or unwilling to be under GA. Those currently available LA-based procedure has shown promising results including prostatic stents and trans-urethral lifts, but have drawbacks due to being expensive, not widely available, less suitable in median lobe enlargement or may cause complications including migration, overgrowth of prostatic tissue or foreign-body related complications which may require GA for their treatment. Prostatic Artery Embolization (PAE), initially an LA-based emergency treatment option for persistent life-threatening hematuria from a bleeding BPH, now has been proven to be a safe elective treatment. In Malaysia this novel technique was first reported in 2017 for treatment of post TURP intractable hematuria. Methods and Material: We retrospectively evaluated all 13 catheter-dependent BPH patients in two tertiary urology centres treated with PAE from April 2019 until December 2021 to assess post-treatment efficacy. Results: One patient failed removal of catheter within 3 months post-procedure but 12 out of 13 patients safely got their catheter removed within 1-3 months and resulted in significant IPSS improvement. Conclusion: PAE is a safe and effective treatment option for BPH patients of the Malaysian population but needs prospective evaluation.
Introduction: AI-based techniques can be used to localize and measure the intracerebral haemorrhage (ICH) in computed tomography (CT). This study aims to develop an automated detection algorithm with higher sensitivity in ICH evaluation in comparison to the conventional method. This indirectly influences the patient’s prognosis by reducing the risk of delay or misdiagnosis. Methods: Selected 50 CT brain images with primary ICH were used for three different measurement approaches including the conventional Kothari method (Conventional), AI-based method (A.I.), and manually marking by the radiologist, which is the ground truth (G.T.). In the automated system, a convolutional neural network (CNN) is used to localize the ICH, followed by a thresholding technique to segment the ICH, and finally, the measurements are computed. The segmentation performance is measured using Dice similarity coefficient. The automated ICH measurements are compared against the ground truth (A.I. vs G.T.). Concurrently, the ICH measurements calculated using the conventional method are also compared against the ground truth (Conventional vs G.T). The t-test analysis is performed between the sum squared error (SSE) of ICH measurements from the automated-ground truth and the conventional-ground truth. Results: The mean volumetric Dice similarity coefficient for the automated segmentation algorithm when tested against the ground truth, is 0.859±0.135. The t-test analysis of the SSE between conventional-ground truth (median=5.45, SD=3.96) and automated-ground truth (median=0.73, SD=0.78) achieved p-value < 0.001 (p=5.10E-9). Conclusion: The automated AI-based algorithm significantly improved the ICH surface area measurement from the CT brain with higher accuracy and efficiency in comparison to the conventional method.
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