首页|Southern Medical University Reports Findings in Artificial Intelligence (Predict ion of Visual Outcome After Rhegmatogenous Retinal Detachment Surgery Using Arti ficial Intelligence Techniques)
Southern Medical University Reports Findings in Artificial Intelligence (Predict ion of Visual Outcome After Rhegmatogenous Retinal Detachment Surgery Using Arti ficial Intelligence Techniques)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Guangzhou, People ’s Republic of China, by NewsRx journalists, research stated, “This study aimed to develop artificial intelligence models for predicting postoperative functiona l outcomes in patients with rhegmatogenous retinal detachment (RRD). A retrospec tive review and data extraction were conducted on 184 patients diagnosed with RR D who underwent pars plana vitrectomy (PPV) and gas tamponade.” The news correspondents obtained a quote from the research from Southern Medical University, “The primary outcome was the best-corrected visual acuity (BCVA) at three months after the surgery. Those with a BCVA of less than 6/18 Snellen acu ity were classified into a vision impairment group. A deep learning model was de veloped using presurgical predictors, including ultra-widefield fundus images, s tructural optical coherence tomography (OCT) images of the macular region, age, gender, and preoperative BCVA. A fusion method was used to capture the interacti on between different modalities during model construction. Among the participant s, 74 (40%) still had vision impairment after the treatment. There were significant differences in age, gender, presurgical BCVA, intraocular press ure, macular detachment, and extension of retinal detachment between the vision impairment and vision non-impairment groups. The multimodal fusion model achieve d a mean area under the curve (AUC) of 0.91, with a mean accuracy of 0.86, sensi tivity of 0.94, and specificity of 0.80. Heatmaps revealed that the macular invo lvement was the most active area, as observed in both the OCT and ultra-widefiel d images. This pilot study demonstrates that artificial intelligence techniques can achieve a high AUC for predicting functional outcomes after RRD surgery, eve n with a small sample size. Machine learning methods identified The macular regi on as the most active region.”
GuangzhouPeople’s Republic of ChinaA siaArtificial IntelligenceEmerging TechnologiesEye Diseases and ConditionsHealth and MedicineMachine LearningRetinal DetachmentRetinal Diseases an d ConditionsSurgery
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