Background: The primary aim of this study was to create a machine learning (ML) model that can predict perforated and nonperforated acute appendicitis (AAp) with high accuracy and to demonstrate the clinical interpretability of the model with explainable artificial intelligence (XAI). Method: A total of 1797 patients who underwent appendectomy with a preliminary diagnosis of AAp between May 2009 and March 2022 were included in the study. Considering the histopathological examination, the patients were divided into two groups as AAp (n = 1465) and non-AAp (NA; n = 332); the non-AAp group is also referred to as negative appendectomy. Subsequently, patients confirmed to have AAp were divided into two subgroups: nonperforated (n = 1161) and perforated AAp (n = 304). The missing values in the data set were assigned using the Random Forest method. The Boruta variable selection method was used to identify the most important variables associated with AAp and perforated AAp. The class imbalance problem in the data set was resolved by the SMOTE method. The CatBoost model was used to classify AAp and non-AAp patients and perforated and nonperforated AAp patients. The performance of the model in the holdout test set was evaluated with accuracy, F1- score, sensitivity, specificity, and area under the receiver operator curve (AUC). The SHAP method, which is one of the XAI methods, was used to interpret the model results. Results: The CatBoost model could distinguish AAp patients from non-AAp individuals with an accuracy of 88.2% (85.6–90.8%), while distinguishing perforated AAp patients from nonperforated AAp individuals with an accuracy of 92% (89.6–94.5%). According to the results of the SHAP method applied to the CatBoost model, it was observed that high total bilirubin, WBC, Netrophil, WLR, NLR, CRP, and WNR values, and low PNR, PDW, and MCV values increased the prediction of AAp biochemically. On the other hand, high CRP, Age, Total Bilirubin, PLT, RDW, WBC, MCV, WLR, NLR, and Neutrophil values, and low Lymphocyte, PDW, MPV, and PNR values were observed to increase the prediction of perforated AAp. Conclusion: For the first time in the literature, a new approach combining ML and XAI methods was tried to predict AAp and perforated AAp, and both clinical conditions were predicted with high accuracy. This new approach proved successful in showing how well which demographic and biochemical parameters could explain the current clinical situation in predicting AAp and perforated AAp.

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The study aims to evaluate the diagnostic performance of an artificial intelligence system based on deep learning for the segmentation of occlusal, proximal and cervical caries lesions on panoramic radiographs. The study included 504 anonymous panoramic radiographs obtained from the radiology archive of Inonu University Faculty of Dentistry’s Department of Oral and Maxillofacial Radiology from January 2018 to January 2020. This study proposes Dental Caries Detection Network (DCDNet) architecture for dental caries segmentation. The main difference between DCDNet and other segmentation architecture is that the last part of DCDNet contains a Multi-Predicted Output (MPO) structure. In MPO, the final feature map split into three different paths for detecting occlusal, proximal and cervical caries. Extensive experimental analyses were executed to analyze the DCDNet network architecture performance. In these comparison results, while the proposed model achieved an average F1-score of 62.79%, the highest average F1-score of 15.69% was achieved with the state-of-the-art segmentation models. These results show that the proposed artificial intelligence-based model can be one of the indispensable auxiliary tools of dentists in the diagnosis and treatment planning of carious lesions by enabling their detection in different locations with high success.

OBJECTIVE:The aim of the present study was to retrospectively evaluate the difference between the preoperative estimated volume and the actual intraoperative graft volume determined in donor right hepatectomies and to evaluate the possible effect of age, gender, and body mass index on the difference.METHODS:A total of 225 donor hepatectomies performed at the center between 2016 and 2017 were evaluated for the study. Left hepatectomies and left lateral segmentectomies were excluded from the analysis. As a result, 174 donor right hepatectomies were included in the study. Volumetric analysis was performed with dynamic hepatic computed tomography (CT), including non-contrast analysis, followed by non-ionic, contrast-enhanced arterial, portal, and hepatic-phase, thin-slice scanning. Volumetric analysis was performed based on the CT images using automatic volume calculating software.RESULTS:The mean preoperatively estimated graft volume was 800±112 g and the mean intraoperatively measured actual graft volume was 750±131 g. There was a statistically significant difference (p=0.003). Age and body mass index had a significant impact on the discrepancy between the predicted and actual graft volume, while gender did not.CONCLUSION:A thorough preoperative evaluation of the donor graft volume should be performed in order to prevent donor morbidity and mortality, as well as small-for-size and large-for-size phenomena in the implanted grafts. Physicians working in the field of transplantation should be aware of the fact that a difference of 10% between the predicted and the actual graft volume is usually encountered.

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Aim: In this study, it is aimed to extract patient-based explanations of the contribution of important features in the decision-making process (estimation) of the Random forest (RF) model, which is difficult to interpret for PCOS disease risk, with Local Interpretable Model-Agnostic Explanations (LIME). Materials and Methods: In this study, the Local Interpretable Model-Agnostic Annotations (LIME) method was applied to the “Polycystic ovary syndrome” dataset to explain the Random Forest (RF) model, which is difficult to interpret for PCOS risk factors estimation. This dataset is available at https://www.kaggle.com/prasoonkottarathil/polycystic-ovary-syndrome-pcos. Results: Accuracy, sensitivity, specificity, positive predictive value, negative predictive value and balanced accuracy obtained from the Random Forest method were 86.03%, 86.32%, 85.37%, 93.18%, 72.92% and 85.84% respectively. According to the obtained results, the observations whose results were obtained, the values of Follicle (No) L. and Follicle (No) R. in different value ranges were positively correlated with the absence of PCOS. For the observations whose absence of PCOS results were obtained, the variables RBS(mg/dl), bmi_y, fsh_lh, TSH (mIU/L), Endometrium (mm) also played a role in obtaining the results. In addition, for the observations whose results were obtained, the values of Follicle No L and Follicle No R in different value ranges were also found to be positively correlated with PCOS. In addition, beta-HCG(mIU/mL), PRG(ng/mL), RBS(mg/dl), bmi_y, Endometrium (mm), fsh_lh variables also played a role in obtaining the results for PCOS. Conclusion: When the observations obtained from the results are examined, it can be said that the Follicle (No) L. and Follicle (No) R. variables are the most effective variables on the presence or absence of PCOS. For different value ranges of these two variables, the result of PCOS or not varies. Based on this, it can be said that different values of Follicle (No) L. and Follicle (No) R. variables for PCOS status may be effective in determining the disease.

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