Tıp Fakültesi / Faculty of Medicine

Permanent URI for this collectionhttps://hdl.handle.net/11727/1403

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    AI-ASSISTED Emotion Analysis During Complementary Feeding in Infants Aged 6-11 Months
    (2023) Gulsen, Murat; Aydin, Beril; Gurer, Guliz; Yalcin, Siddika Songul; 37742418
    This study aims to explore AI-assisted emotion assessment in infants aged 6-11 months during complementary feeding using OpenFace to analyze the Actions Units (AUs) within the Facial Action Coding system. When infants (n = 98) were exposed to a diverse range of food groups; meat, cow-milk, vegetable, grain, and dessert products, favorite, and disliked food, then video recordings were analyzed for emotional responses to these food groups, including surprise, sadness, happiness, fear, anger, and disgust. Time-averaged filtering was performed for the intensity of AUs. Facial expression to different food groups were compared with neutral states by Wilcoxon Singed test. The majority of the food groups did not significantly differ from the neutral emotional state. Infants exhibited high disgust responses to meat and anger reactions to yogurt compared to neutral. Emotional responses also varied between breastfed and non-breastfed infants. Breastfed infants showed heightened negative emotions, including fear, anger, and disgust, when exposed to certain food groups while non-breastfed infants displayed lower surprise and sadness reactions to their favorite foods and desserts. Further longitudinal research is needed to gain a comprehensive understanding of infants' emotional experiences and their associations with feeding behaviors and food acceptance.
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    Strategies to improve the diagnosis and clinical treatment of dermatophyte infections
    (2023) Durdu, Murat; Ilkit, Macit; 36329574
    Introduction Significant problems are associated with the diagnosis and treatment of dermatophyte infections, which constitute the most common fungal infections of the skin. Although this is a common problem in the community, there are no adequate guidelines for the management of all forms of dermatophyte infections. Even if dermatophytes are correctly diagnosed, they sometimes exhibit poor susceptibility to several antifungal compounds. Therefore, long-term treatment may be needed, especially in immunosuppressed patients, for whom antifungal pharmacotherapy may be inconvenient owing to allergies and undesirable drug interaction-related effects. Areas covered In this review article, problems related to the diagnosis and treatment of dermatophyte infections have been discussed, and suggestions to resolve these problems have been presented. Expert opinion Pretreatment microscopic or mycological examinations should be performed for dermatophyte infections. In treatment-refractory cases, antifungal-resistant strains should be determined using antifungal susceptibility testing or via molecular methods. Natural herbal, laser, and photodynamic treatments can be used as alternative treatments in patients who cannot tolerate topical and systemic antifungal treatments.
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    Utilizing Deep Convolutional Generative Adversarial Networks for Automatic Segmentation of Gliomas: An Artificial Intelligence Study
    (2022) Aydogan Duman, Ebru; Sagiroglu, Seref; Celtikci, Pinar; Demirezen, Mustafa Umut; Borcek, Alp Ozgun; Emmez, Hakan; Celtikci, Emrah; 34542897
    AIM: To describe a deep convolutional generative adversarial networks (DCGAN) model which learns normal brain MRI from normal subjects than finds distortions such as a glioma from a test subject while performing a segmentation at the same time. MATERIAL and METHODS: MRIs of 300 healthy subjects were employed as training set. Additionally, test data were consisting anonymized T2-weigted MRIs of 27 healthy subjects and 27 HGG patients. Consecutive axial T2-weigted MRI slices of every subject were extracted and resized to 364x448 pixel resolution. The generative model produced random normal synthetic images and used these images for calculating residual loss to measure visual similarity between input MRIs and generated MRIs. RESULTS: The model correctly detected anomalies on 24 of 27 HGG patients' MRIs and marked them as abnormal. Besides, 25 of 27 healthy subjects' MRIs in the test dataset detected correctly as healthy MRI. The accuracy, precision, recall, and AUC were 0.907, 0.892, 0.923, and 0.907, respectively. CONCLUSION: Our proposed model demonstrates acceptable results can be achieved only by training with normal subject MRIs via using DCGAN model. This model is unique because it learns only from normal MRIs and it is able to find any abnormality which is different than the normal pattern.