Repeat Analysis Program As A Quality Assurance System For Radiology Management: Causal Repeat and Challenges
Rejected or repeated images analysis remains a significant challenge, particularly in digital imaging. Despite the expectation that the transition from conventional to digital systems would reduce repetition rates, the reality is that repetition rates still exceed established standards. This literat...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/85/e3sconf_icenis2023_05004.pdf |
| _version_ | 1797345469798547456 |
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| author | Rochmayanti Dwi Adi Kusworo Edi Widodo Catur |
| author_facet | Rochmayanti Dwi Adi Kusworo Edi Widodo Catur |
| author_sort | Rochmayanti Dwi |
| collection | DOAJ |
| description | Rejected or repeated images analysis remains a significant challenge, particularly in digital imaging. Despite the expectation that the transition from conventional to digital systems would reduce repetition rates, the reality is that repetition rates still exceed established standards. This literature review aims to shed light on the identification of causes and barriers in the reject/repeat program. We conducted a systematic review of this program in radiography units over several decades, examining the causes of repetition, types of examinations, and data sources used. We also described the methods employed to analyze reject/repeat instances in both conventional and digital systems. The study found that computed or digital radiography was the primary data source for image analysis. Despite the use of digital systems, repetition rates persisted, with chest radiography being the most significant contributor, accounting for over 30% of cases. Technical factors, particularly positioning errors, contributed to more than 30% of repetitions. Notably, determining the causes of rejection proved subjective. However, one study highlighted that artificial intelligence (AI) could accurately predict image rejection with a sensitivity of 93%. Thus, the incorporation of AI can greatly assist in classifying rejection causes, resulting in more efficient and streamlined radiology management |
| first_indexed | 2024-03-08T11:18:08Z |
| format | Article |
| id | doaj.art-281b934e6d264f3b8c37b9367700e768 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-08T11:18:08Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-281b934e6d264f3b8c37b9367700e7682024-01-26T10:28:08ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014480500410.1051/e3sconf/202344805004e3sconf_icenis2023_05004Repeat Analysis Program As A Quality Assurance System For Radiology Management: Causal Repeat and ChallengesRochmayanti Dwi0Adi Kusworo1Edi Widodo Catur2Doctoral Program of Information System School of Postgraduate, University DiponegoroDepartment of Physics, Faculty of Science and Mathematics, Diponegoro UniversityDepartment of Physics, Faculty of Science and Mathematics, Diponegoro UniversityRejected or repeated images analysis remains a significant challenge, particularly in digital imaging. Despite the expectation that the transition from conventional to digital systems would reduce repetition rates, the reality is that repetition rates still exceed established standards. This literature review aims to shed light on the identification of causes and barriers in the reject/repeat program. We conducted a systematic review of this program in radiography units over several decades, examining the causes of repetition, types of examinations, and data sources used. We also described the methods employed to analyze reject/repeat instances in both conventional and digital systems. The study found that computed or digital radiography was the primary data source for image analysis. Despite the use of digital systems, repetition rates persisted, with chest radiography being the most significant contributor, accounting for over 30% of cases. Technical factors, particularly positioning errors, contributed to more than 30% of repetitions. Notably, determining the causes of rejection proved subjective. However, one study highlighted that artificial intelligence (AI) could accurately predict image rejection with a sensitivity of 93%. Thus, the incorporation of AI can greatly assist in classifying rejection causes, resulting in more efficient and streamlined radiology managementhttps://www.e3s-conferences.org/articles/e3sconf/pdf/2023/85/e3sconf_icenis2023_05004.pdfreject repeat analysis programradiologymanagementai |
| spellingShingle | Rochmayanti Dwi Adi Kusworo Edi Widodo Catur Repeat Analysis Program As A Quality Assurance System For Radiology Management: Causal Repeat and Challenges E3S Web of Conferences reject repeat analysis program radiology management ai |
| title | Repeat Analysis Program As A Quality Assurance System For Radiology Management: Causal Repeat and Challenges |
| title_full | Repeat Analysis Program As A Quality Assurance System For Radiology Management: Causal Repeat and Challenges |
| title_fullStr | Repeat Analysis Program As A Quality Assurance System For Radiology Management: Causal Repeat and Challenges |
| title_full_unstemmed | Repeat Analysis Program As A Quality Assurance System For Radiology Management: Causal Repeat and Challenges |
| title_short | Repeat Analysis Program As A Quality Assurance System For Radiology Management: Causal Repeat and Challenges |
| title_sort | repeat analysis program as a quality assurance system for radiology management causal repeat and challenges |
| topic | reject repeat analysis program radiology management ai |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/85/e3sconf_icenis2023_05004.pdf |
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