Errors in radiographic image annotation can lead to incorrect clinical interpretations and disastrous consequences. The research aims to examine the practice of radiographers’ use of left-right image annotation in film screen radiography and after the installation of computed radiography in a tertiary hospital in Africa. The study outcome indicated that the standard error was that of the anatomical side marker. Research’s main points include that Radiologists should be thoroughly trained, especially those who are expected to produce an image that is complete and accurate to avoid patients from unnecessary suffering. Another point is that almost half of the radiographs surveyed have complete left-right image annotations, which have reduced errors in film-screen radiography (FSR). The study results revealed some radiographers fail to recognize appropriate left-right image annotation consistently due to inadequate training or failure to adhere to good practices.
The strength of the article is that it used a descriptive research design. It surveyed radiographers practice of left-right image annotation in film-screen radiography after installing computed radiography in a tertiary hospital in Africa. The study’s results can be used as a basis for further research since most physicians are unaware of left-right image annotation and radiographic artifacts. Therefore, this research has implications for the training of radiographers since they are in charge of the radiographic record.
The main objective of this article is to investigate how physicians annotate left and right in computed radiography (CR); however, the authors did not address the issue of radiographic artifacts. They only focused on the technical issue of patient positioning. According to Adejoh et al. (2020), the onset of radiographic artifacts takes time from imaging, such as intravenous contrast injection, film removal, or patient movement. Most radiographic artifacts are caused by improper patient positioning, which can be rectified through radiological knowledge and experience. Radiographers are expected to be able to diagnose diseases with relatively high accuracy. Therefore, they must be adequate trained to diagnose diseases properly and accurately.
The authors did not consider the possible effects of radiopaque markers, which cause patient misalignment and failure to annotate correctly. It led to the failure of the study to achieve its primary aim of investigating how physicians annotate left and right in computed radiography (CR) and direct digital radiography (DDR). Physicians should be trained in CR to identify and correctly annotate left and right in CR and DDR. Mention the possible causes that might have caused the study result, which was one of the primary aims of its presented study. It is impossible to get a clear answer to that question because the study mainly focuses on applying CR annotation to avoid medical negligence and any other annotation errors. Therefore, radiologists prone to this type of technology should be trained in CR and DDR to make them inappropriate practices and prevent people from using CR for other purposes.
In conclusion, it is essential for radiologists whom the patients pay to be aware of new technologies. For example, it is essential to know about new technologies such as CR and DDR and their application in radiology. The article suggested that radiographers still make wrong annotations of left and right images in CR, which could be one of the possible causes that might have caused the incorrect result. The study also suggests that there is a lack of radiologists awareness about CR and DDR, more so on how to use them properly. The poor-quality images and poor annotations of the physicians are one of the factors that could have caused wrong results in this study. Poor quality images and lack of knowledge about annotations work usually lead to wrong results.
References
Adejoh, T., Elugwu, C. H., Sidi, M., Ezugwu, E. E., Asogwa, C. O., & Okeji, M. C. (2020). An audit of radiographers’ practice of left-right image annotation in film-screen radiography and after installation of computed radiography in a tertiary hospital in Africa. Egyptian Journal of Radiology and Nuclear Medicine, 51(1), 1-7. Web.
Meena, T., & Roy, S. (2022). Bone fracture detection using deep supervised learning from radiological images: A paradigm shift. Diagnostics, 12(10), 2420.