arXiv subjects

Zhi Zhen Qin

Publications and source records attributed to Zhi Zhen Qin.

3 recordsLinked to original sources

arXiv2024

Assessment of scattered and leakage radiation from ultra-portable digital chest X-ray systems: An independent study

Ultraportable X-ray devices are ideal for TB screening in resource-limited settings. Unfortunately, guidelines on the radiation safety of these devices are lacking. The aim of the study was to determine the radiation dose by scattered and leakage radiation of ultraportable X-ray devices to provide a basis for these guidelines. Radiation dose measurements were performed with four ultraportable X-ray devices that meet the WHO-IAEA criteria. An anthropomorphic thorax phantom was positioned in posterior-anterior orientation in a clinically representative X-ray setup. X-ray exams were acquired with the following scan parameters: 90kV, 2.5mAs and maximum mAs, 1m and 1.8m source-to-skin distance. The entrance skin dose was measured at the center of the phantom. The scattered radiation dose was measured at 1m from the phantom as a function of scatter angle. Leakage radiation was measured at 0.5m from the X-ray tube with collimators closed and covered with additional lead. From the scatter measurements, the 'safe distance bunny' was determined, which was the minimum distance to the phantom to stay below the international dose limit to the public (1mSv per year) at a given workload. The longest distance (related to highest scatter dose) was observed behind the edges of the detector and back towards the X-ray tube, whereas the shortest distance (related to lowest scatter dose) was observed to the sides of the phantom. For the radiographer position, the total radiation dose by scattered and leakage radiation was determined in various scenarios. In most cases, the total radiation dose of ultraportable X-ray devices can be kept below 1mSv per year by employing basic radiation safety rules:1. Reduce time in the field, 2. Increase distance to the X-ray source, and 3. Use shielding measures (lead apron). Ultraportable X-ray devices can be safely used for TB screening when using adequate precautions.

physics.med-ph↗

arXiv2021

Can artificial intelligence (AI) be used to accurately detect tuberculosis (TB) from chest X-rays? An evaluation of five AI products for TB screening and triaging in a high TB burden setting

Artificial intelligence (AI) products can be trained to recognize tuberculosis (TB)-related abnormalities on chest radiographs. Various AI products are available commercially, yet there is lack of evidence on how their performance compared with each other and with radiologists. We evaluated five AI software products for screening and triaging TB using a large dataset that had not been used to train any commercial AI products. Individuals (>=15 years old) presenting to three TB screening centers in Dhaka, Bangladesh, were recruited consecutively. All CXR were read independently by a group of three Bangladeshi registered radiologists and five commercial AI products: CAD4TB (v7), InferReadDR (v2), Lunit INSIGHT CXR (v4.9.0), JF CXR-1 (v2), and qXR (v3). All five AI products significantly outperformed the Bangladeshi radiologists. The areas under the receiver operating characteristic curve are qXR: 90.81% (95% CI:90.33-91.29%), CAD4TB: 90.34% (95% CI:89.81-90.87), Lunit INSIGHT CXR: 88.61% (95% CI:88.03%-89.20%), InferReadDR: 84.90% (95% CI: 84.27-85.54%) and JF CXR-1: 84.89% (95% CI:84.26-85.53%). Only qXR met the TPP with 74.3% specificity at 90% sensitivity. Five AI algorithms can reduce the number of Xpert tests required by 50%, while maintaining a sensitivity above 90%. All AI algorithms performed worse among the older age and people with prior TB history. AI products can be highly accurate and useful screening and triage tools for TB detection in high burden regions and outperform human readers.

eess.IV↗

arXiv2017

One-sample aggregate data meta-analysis of medians

An aggregate data meta-analysis is a statistical method that pools the summary statistics of several selected studies to estimate the outcome of interest. When considering a continuous outcome, typically each study must report the same measure of the outcome variable and its spread (e.g., the sample mean and its standard error). However, some studies may instead report the median along with various measures of spread. Recently, the task of incorporating medians in meta-analysis has been achieved by estimating the sample mean and its standard error from each study that reports a median in order to meta-analyze the means. In this paper, we propose two alternative approaches to meta-analyze data that instead rely on medians. We systematically compare these approaches via simulation study to each other and to methods that transform the study-specific medians and spread into sample means and their standard errors. We demonstrate that the proposed median-based approaches perform better than the transformation-based approaches, especially when applied to skewed data and data with high inter-study variance. In addition, when meta-analyzing data that consists of medians, we show that the median-based approaches perform considerably better than or comparably to the best-case scenario for a transformation approach: conducting a meta-analysis using the actual sample mean and standard error of the mean of each study. Finally, we illustrate these approaches in a meta-analysis of patient delay in tuberculosis diagnosis.

stat.ME↗