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    Applying large language model for automated quality scoring of radiology requisitions using a standardized criteria
    (Springer, 2025) Buyuktoka, Rasit Eren; Surucu, Murat; Erekli Derinkaya, Pelin Berfin; Adibelli, Zehra Hilal; Salbas, Ali; Koc, Ali Murat; Buyuktoka, Asli Dilara
    ObjectivesTo create and test a locally adapted large language model (LLM) for automated scoring of radiology requisitions based on the reason for exam imaging reporting and data system (RI-RADS), and to evaluate its performance based on reference standards.Materials and methodsThis retrospective, double-center study included 131,683 radiology requisitions from two institutions. A bidirectional encoder representation from a transformer (BERT)-based model was trained using 101,563 requisitions from Center 1 (including 1500 synthetic examples) and externally tested on 18,887 requisitions from Center 2. The model's performance for two different classification strategies was evaluated by the reference standard created by three different radiologists. Model performance was assessed using Cohen's Kappa, accuracy, F1-score, sensitivity, and specificity with 95% confidence intervals.ResultsA total of 18,887 requisitions were evaluated for the external test set. External testing yielded a performance with an F1-score of 0.93 (95% CI: 0.912-0.943); kappa = 0.88 (95% CI: 0.871-0.884). Performance was highest in common categories RI-RADS D and X (F1 >= 0.96) and lowest for rare categories RI-RADS A and B (F1 <= 0.49). When grouped into three categories (adequate, inadequate, and unacceptable), overall model performance improved [F1-score = 0.97; (95% CI: 0.96-0.97)].ConclusionThe locally adapted BERT-based model demonstrated high performance and almost perfect agreement with radiologists in automated RI-RADS scoring, showing promise for integration into radiology workflows to improve requisition completeness and communication.Key PointsQuestionCan an LLM accurately and automatically score radiology requisitions based on standardized criteria to address the challenges of incomplete information in radiological practice?FindingsA locally adapted BERT-based model demonstrated high performance (F1-score 0.93) and almost perfect agreement with radiologists in automated RI-RADS scoring across a large, multi-institutional dataset.Clinical relevanceLLMs offer a scalable solution for automated scoring of radiology requisitions, with the potential to improve workflow in radiology. Further improvement and integration into clinical practice could enhance communication, contributing to better diagnoses and patient care.Key PointsQuestionCan an LLM accurately and automatically score radiology requisitions based on standardized criteria to address the challenges of incomplete information in radiological practice?FindingsA locally adapted BERT-based model demonstrated high performance (F1-score 0.93) and almost perfect agreement with radiologists in automated RI-RADS scoring across a large, multi-institutional dataset.Clinical relevanceLLMs offer a scalable solution for automated scoring of radiology requisitions, with the potential to improve workflow in radiology. Further improvement and integration into clinical practice could enhance communication, contributing to better diagnoses and patient care.Key PointsQuestionCan an LLM accurately and automatically score radiology requisitions based on standardized criteria to address the challenges of incomplete information in radiological practice?FindingsA locally adapted BERT-based model demonstrated high performance (F1-score 0.93) and almost perfect agreement with radiologists in automated RI-RADS scoring across a large, multi-institutional dataset.Clinical relevanceLLMs offer a scalable solution for automated scoring of radiology requisitions, with the potential to improve workflow in radiology. Further improvement and integration into clinical practice could enhance communication, contributing to better diagnoses and patient care.
  • [ X ]
    Öğe
    Convolutional neural networks can diagnose schizophrenia
    (Elsevier, 2025) Degirmenci, Murside; Surucu, Murat; Perc, Matjaz; Isler, Yalcin
    Schizophrenia is a severe mental disorder that affects how individuals think, perceive, and behave, often making accurate and timely diagnosis a significant challenge for clinicians. Traditional diagnostic approaches, such as interviews and psychological tests, have limitations in capturing the complex neurological underpinnings of the condition. In recent years, machine learning and deep learning techniques have shown promise in improving diagnostic accuracy across a variety of medical domains. However, relatively few studies have applied these methods to schizophrenia diagnosis, despite their potential. In this study, we investigate whether convolutional neural networks can effectively diagnose schizophrenia using publicly available EEG data. We achieved classification accuracies of 98.26% in subject-independent settings and 91.21% in subject-dependent settings on the test data, using a fully connected layer based on a Multi-Layer Perceptron classifier. These results appear promising when compared to the current state of the art.