{
  "abstract": "Aims To develop prediction models for identifying cases with poor visual outcomes after surgery for primary rhegmatogenous retinal detachment (RRD).Methods All data were obtained from the Japan-Retinal Detachment Registry. Patients with visual acuity (VA) recorded at 6 months after the surgery for primary RRD were included. For identifying the cases with poor VA defined as the logarithm of the minimum angle of resolution VA≥1.0 at 6 months post-surgery, 47 clinical features were selected out of 288 features, then three different machine learning (ML) algorithms (LightGBM, XGBoost and Random Forest) and logistic regression were used to construct models. Discriminative accuracies of the constructed models were compared with the Primary Retinal detachment Outcomes (PRO) score, a previously proposed scoring system for the same purpose. We also estimated the continuous VA using the same three ML algorithms and linear regression model to detect individuals with poor postoperative VA.Results A total of 2658 patients were included. We observed that all the constructed ML models showed high discriminative accuracies (area under the receiver operating characteristic curve; AUROC: 0.876–0.901). Of the constructed models, the Random Forest model achieved the highest discriminative accuracy (AUROC: 0.901; 95% CI 0.881 to 0.921). The performance of this model appeared to be superior to the PRO score (AUROC: 0.794, 95% CI 0.744 to 0.839).Conclusion ML models predict poor visual prognosis after primary RRD surgery with high discriminative accuracy.",
  "authors": [
    {
      "affiliations": [
        "Department of Ophthalmology, Nagoya University Graduate School of Medicine, Nagoya, Japan"
      ],
      "name": "Ai Fujita Sajiki"
    },
    {
      "affiliations": [
        "Department of Ophthalmology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan"
      ],
      "name": "Kanae Fukutsu"
    },
    {
      "affiliations": [
        "Department of Ophthalmology, Kyoto Prefectural University of Medicine, Kyoto, Japan"
      ],
      "name": "Akifumi Matsumoto"
    },
    {
      "affiliations": [
        "Department of Ophthalmology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan"
      ],
      "name": "Ryoh Funatsu"
    },
    {
      "affiliations": [
        "Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan"
      ],
      "name": "Masahiro Miyake"
    },
    {
      "affiliations": [
        "Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan"
      ],
      "name": "Masato Akiyama"
    }
  ],
  "title": "Identifying patients with poor visual outcomes after primary rhegmatogenous retinal detachment surgery using machine learning",
  "uid": "82548a4d-0861-5d3c-9bc1-db69c728a95b"
}