首页|First Affiliated Hospital of Guangxi Medical University Researcher Describes Adv ances in Artificial Intelligence (Evaluation of alarm notification of artificial intelligence in automated analyzer detection of parasites)
First Affiliated Hospital of Guangxi Medical University Researcher Describes Adv ances in Artificial Intelligence (Evaluation of alarm notification of artificial intelligence in automated analyzer detection of parasites)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news reporting originating from Guangxi, People’s Republic of China, by NewsRx correspondents, research stated, “To evaluate the alarm notification of artificial intelligence in detecting parasites on the KU-F 40 Fully Automatic Feces Analyzer and provide a reference for clinical diagnosis in parasite diseases.” The news correspondents obtained a quote from the research from First Affiliated Hospital of Guangxi Medical University: “A total of 1030 fecal specimens from p atients in our hospital from May to June 2023 were collected, and parasite detec tion studies were conducted using the KU-F40 automated feces analyzer (normal mo de method, floating-sedimentation mode method), acid-ether sedimentation method, and direct smear microscopy method, respectively. The positive detection rate o f parasites in the 1030 fecal specimens was 22.9% (236 cases), of which the KU-F40 normal mode method had a detection rate of 16.3% (168 cases), the acid-ether sedimentation method had a detection rate of 19.0% (196 cases), and the direct smear microscopy method had a detection rate of 13.1 % (135 cases). The detection rates of the first 2 methods were hig her than those of the direct smear microscopy method, and the difference was sta tistically significant (P <.05). The detection rate of the KU-F40 floating-sediment ation mode method was 11.9% (123 cases), which was lower than that of the direct smear microscopy, and the difference was not statistically signif icant (P > .05). The sensitivity of the KU-F40 normal mode metho d, acid-ether sedimentation method, direct smear microscopy method, and the KU-F 40 floating-sedimentation mode method were 71.2%, 83.1% , 57.2%, and 52.1%, respectively, and the specificity was 94.7%, 100%, 100%, and 97.7% , respectively. The coincidence rates of the KU-F40 normal mode method was 90.78 %, with Kappa values of 0.633.”
First Affiliated Hospital of Guangxi Med ical UniversityGuangxiPeople’s Republic of ChinaAsiaArtificial Intellige nceEmerging TechnologiesMachine Learning
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