Computed tomography (CT), fluoroscopy, and radiography ("conventional X-ray" including mammography) all use ionizing radiation to generate images of the body. There are many types - or modalities - of medical imaging procedures, each of which uses different technologies and techniques. Medical imaging has led to improvements in the diagnosis and treatment of numerous medical conditions in children and adults. Industry Guidance - Documents of Interest.Medical device requirements for manufacturers of x-ray imaging devices.Electronic Product Radiation Control (EPRC) requirements for manufacturers and assemblers.Regulations and guidelines pertaining to imaging facilities and personnel.Information for the referring physician.Principles of radiation protection: justification and optimization.Questions to ask your health care provider.So the straight line of the equation y = 2x + 6 meets the y-axis at (0,6). Therefore, we can find the intersection point of y-axis and y = 2x + 6 by simply putting the value of x as 0 and finding the value of y. y = 2(0)+6 = 0 + 6 = 6. Question 3: For a linear equation y = 2x + 6, find the point where the straight line meets y-axis on the graph.Īnswer: On y-axis, the x-coordinate of the point is 0. After extending the straight line, we see that this line intersects the x-axis at point (5,0). Now, we can join both points with a straight line when we have plotted both points. Also, find out the point where the straight line going through these points meets the x-axis.Īnswer: For (3,2), as we can see, the x-coordinate point is 3, and the y-coordinate point is 2. If the given points are (3,2) and (2,3), then plot these two points on the X- and Y-axis. Question 2: Two different points are to be plotted on a graph. (0, 1) (4, 0) (7, 7) (−5, 0) (−4, 4) (0, −5) (8, 0) (6, 0)Īnswer: Since the coordinates lying on x-axis have their y coordinate zero (0), the following points will lie on x-axis: Question 1: Which of the following points lie on the x-axis? Now, the y-coordinate of B(3,4) is 4, so we will go 4 spaces up from this point.Īnd thus we have plotted our point B(3,4) on the graph using the axes. So we will start from the origin and move 3 units to the right on x-axis. Let us learn how to plot a point on the graph by using the X- and Y-axis.įor example: Let’s try to plot the point B(3,4) on the graph. The origin is where the two axes intersect and is written as (0,0). Here, x represents the location of the point with respect to the x-axis and y represents the location of the point with respect to the y-axis. The x-axis is also called the abscissa and the y-axis is called the ordinate.Īny point on the coordinate plane can be located or represented using these two axes in the form of an ordered pair of the form ( x,y). These two axes intersect perpendicularly to form the coordinate plane. The x-axis is a horizontal number line and the y-axis is a vertical number line. The x and y-axis are two important lines of the coordinate plane. Representing a Linear Equation on X- and Y-AxisĪn axis in mathematics is defined as a line that is used to make or mark measurements.
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A fundamental concern associated with automatically or semi-automatically eliminating candidate records is that important studies may be missed, compromising the comprehensiveness of the review and potentially the validity of its conclusions. Mounting interest in the use of ML tools to expedite title and abstract screening has been accompanied by skepticism and distrust by review teams and end users of reviews, and adoption has been slow. The relevance predictions produced by ML tools can also be leveraged by review teams to semi-automate title and abstract screening by eliminating records predicted to be irrelevant. By prioritizing relevant records, such tools provide reviewers with the opportunity to identify relevant studies earlier and move forward with subsequent review tasks (e.g., data extraction, risk of bias appraisal) sooner. Freely-available, off-the-shelf tools like Abstrackr, RobotAnalyst, and Rayyan allow review teams without ML expertise and/or limited resources to create efficiencies during title and abstract screening. Machine learning (ML) tools provide the potential to expedite title and abstract screening by predicting and prioritizing the relevance of candidate records.Īt the time of writing the SR Tool Box, an online repository of software tools that support and/or expedite evidence synthesis processes, referenced 37 tools aimed at supporting title and abstract screening. The process requires substantial effort and time to return a relatively small body of relevant studies. Often, two reviewers screen through the records retrieved, first by title and abstract and then by full text, to identify those that are relevant. To avoid missing relevant studies, rigorously conducted evidence syntheses typically include comprehensive searches of multiple sources. Systematic evidence syntheses provide the foundation of informed decision-making however, the large and growing body of primary studies makes it difficult to complete them efficiently and keep them up-to-date. Many missed records would be identified via other means. Using Abstrackr to assist one of two reviewers in systematic reviews saves time with little risk of missing relevant records. The median (range) proportion missed records for both approaches was 6 (0–22)%. For the rapid reviews, the fully and semi-automated approaches saved time (median (range) 9 (2–18) hours and 3 (1–10) hours, respectively), but less so than for the systematic reviews. The cited references search identified 59% ( n = 10/17) of the records missed. Resultsįor systematic reviews, the semi-automated, dual independent screening approach provided the best balance of time savings (median (range) 20 (3–82) hours) and reliability (median (range) proportion missed records, 1 (0–14)%). We performed cited reference searches to determine if missed studies would be identified via reference list scanning. We calculated the proportion missed, workload savings, and time savings compared to single-reviewer and dual independent screening by human reviewers. We subjected 11 systematic reviews and six rapid reviews to four retrospective screening simulations (automated and semi-automated approaches to single-reviewer and dual independent screening) in Abstrackr, a freely-available machine learning software. We investigated the feasibility of using a machine learning tool’s relevance predictions to expedite title and abstract screening. |