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An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry
Gokhan Guney,Busra Ozgode Yigin,Necdet Guven,Yasemin Hosgoren Alici,Burcin Colak,Gamze Erzin,Gorkem Saygili 대한정신약물학회 2021 CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE Vol.19 No.2
Deep learning (DL) algorithms have achieved important successes in data analysis tasks, thanks to their capability of revealing complex patterns in data. With the advance of new sensors, data storage, and processing hardware, DL algo-rithms start dominating various fields including neuropsychiatry. There are many types of DL algorithms for different data types from survey data to functional magnetic resonance imaging scans. Because of limitations in diagnosing, esti-mating prognosis and treatment response of neuropsychiatric disorders; DL algorithms are becoming promising approaches. In this review, we aim to summarize the most common DL algorithms and their applications in neuro-psychiatry and also provide an overview to guide the researchers in choosing the proper DL architecture for their research.
The efficacy of 18F-FDG PET/CT in the preoperative evaluation of pancreatic lesions
Atilgan Tolga Akcam,Zafer Teke,Ahmet Gokhan Saritas,Abdullah Ulku,Isa Burak Guney,Ahmet Rencuzogullari 대한외과학회 2020 Annals of Surgical Treatment and Research(ASRT) Vol.98 No.4
Purpose: Since the treatment strategy for benign and malignant pancreatic lesions differ, we aimed to evaluate the clinical value of PET/CT in the diagnosis and management of pancreatic lesions. Methods: Ninety patients who had a histologically confirmed pancreatic lesion were studied. Receiver operating characteristic (ROC) curve analysis was used to investigate the ability of PET/CT to differentiate malignant lesions from benign tumors. Results: The malignant and benign groups comprised 64 and 26 patients, respectively. Despite the similarity in the size of primary tumors (P = 0.588), the mean maximum standardized uptake values (SUVmax) obtained from PET/CT imaging were significantly higher in malignant lesions (9.36 ± 5.9) than those of benign tumors (1.04 ± 2.6, P < 0.001). ROC analysis showed that the optimal SUVmax cutoff value for differentiating malignant lesions (to an accuracy of 91%; 95% confidence interval, 83%–98%) from benign tumors was 3.9 (sensitivity, 92.2%; specificity, 84.6%). Conclusion: PET/CT evaluation of pancreatic lesions confers advantages including fine assessment of malignant potential with high sensitivity and accuracy using a threshold SUVmax value of 3.9.