Deep Network-Based Comprehensive Parotid Gland Tumor Detection

Sünnetci, Kubilay Muhammed; Kaba, Esat; Çeliker, Fatma Beyazal; Alkan, Ahmet

doi:10.1016/j.acra.2023.04.028

Cited by 38 publications

(3 citation statements)

References 37 publications

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“…However, some researchers have successfully implemented automatic segmentation of the parotid gland using artificial intelligence software, achieving excellent accuracy compared to manual segmentation. 41,42 Therefore, in future studies, we will also try to perform automatic segmentation based on a deep learning architecture. Lastly, the extended time span covered by the cases and the utilization of diverse types of CT scanners introduce the possibility of influencing model construction and performance, thus requiring acknowledgment.…”

Section: Discussionmentioning

confidence: 99%

“…Thirdly, in this study, we collected ROI through manual delineation, which is time‐inefficient. However, some researchers have successfully implemented automatic segmentation of the parotid gland using artificial intelligence software, achieving excellent accuracy compared to manual segmentation 41,42 . Therefore, in future studies, we will also try to perform automatic segmentation based on a deep learning architecture.…”

Section: Discussionmentioning

confidence: 99%

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Discriminating atypical parotid carcinoma and pleomorphic adenoma utilizing extracellular volume fraction and arterial enhancement fraction derived from contrast‐enhanced CT imaging: A multicenter study

Xu,

Huang,

Yang

et al. 2024

Cancer Medicine

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ObjectivesTo investigate the added value of extracellular volume fraction (ECV) and arterial enhancement fraction (AEF) derived from enhanced CT to conventional image and clinical features for differentiating between pleomorphic adenoma (PA) and atypical parotid adenocarcinoma (PCA) pre‐operation.MethodsFrom January 2010 to October 2023, a total of 187 cases of parotid tumors were recruited, and divided into training cohort (102 PAs and 51 PCAs) and testing cohort (24 PAs and 10 atypical PCAs). Clinical and CT image features of tumor were assessed. Both enhanced CT‐derived ECV and AEF were calculated. Univariate analysis identified variables with statistically significant differences between the two subgroups in the training cohort. Multivariate logistic regression analysis with the forward variable selection method was used to build four models (clinical model, clinical model+ECV, clinical model+AEF, and combined model). Diagnostic performances were evaluated using receiver operating characteristic (ROC) curve analyses. Delong's test compared model differences, and calibration curve and decision curve analysis (DCA) assessed calibration and clinical application.ResultsAge and boundary were chosen to build clinical model, and to construct its ROC curve. Amalgamating the clinical model, ECV, and AEF to establish a combined model demonstrated superior diagnostic effectiveness compared to the clinical model in both the training and test cohorts (AUC = 0.888, 0.867). There was a significant statistical difference between the combined model and the clinical model in the training cohort (p = 0.0145).ConclusionsECV and AEF are helpful in differentiating PA and atypical PCA, and integrating clinical and CT image features can further improve the diagnostic performance.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Discriminating atypical parotid carcinoma and pleomorphic adenoma utilizing extracellular volume fraction and arterial enhancement fraction derived from contrast‐enhanced CT imaging: A multicenter study

Xu,

Huang,

Yang

et al. 2024

Cancer Medicine

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“…In this study, we developed and investigated two different types of algorithms for spondylolisthesis detection, where Faster R-CNN showed better performance than RetinaNet in spondylolisthesis detection. Faster R-CNN is a two-stage algorithm with real-time performance and higher detection accuracy, while RetinaNet is a state-of-the-art one-stage algorithm that focuses on detection speed ( Sunnetci et al, 2023 ; Xu et al, 2023 ). Considering the high accuracy requirements by clinical work, Faster R-CNN is more suitable for the detection of spondylolisthesis.…”

Section: Discussionmentioning

confidence: 99%

Deep learning system assisted detection and localization of lumbar spondylolisthesis

Zhang

Lin²,

Wang

et al. 2023

Front. Bioeng. Biotechnol.

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Objective: Explore a new deep learning (DL) object detection algorithm for clinical auxiliary diagnosis of lumbar spondylolisthesis and compare it with doctors’ evaluation to verify the effectiveness and feasibility of the DL algorithm in the diagnosis of lumbar spondylolisthesis.Methods: Lumbar lateral radiographs of 1,596 patients with lumbar spondylolisthesis from three medical institutions were collected, and senior orthopedic surgeons and radiologists jointly diagnosed and marked them to establish a database. These radiographs were randomly divided into a training set (n = 1,117), a validation set (n = 240), and a test set (n = 239) in a ratio of 0.7 : 0.15: 0.15. We trained two DL models for automatic detection of spondylolisthesis and evaluated their diagnostic performance by PR curves, areas under the curve, precision, recall, F1-score. Then we chose the model with better performance and compared its results with professionals’ evaluation.Results: A total of 1,780 annotations were marked for training (1,242), validation (263), and test (275). The Faster Region-based Convolutional Neural Network (R-CNN) showed better precision (0.935), recall (0.935), and F1-score (0.935) in the detection of spondylolisthesis, which outperformed the doctor group with precision (0.927), recall (0.892), f1-score (0.910). In addition, with the assistance of the DL model, the precision of the doctor group increased by 4.8%, the recall by 8.2%, the F1-score by 6.4%, and the average diagnosis time per plain X-ray was shortened by 7.139 s.Conclusion: The DL detection algorithm is an effective method for clinical diagnosis of lumbar spondylolisthesis. It can be used as an assistant expert to improve the accuracy of lumbar spondylolisthesis diagnosis and reduce the clinical workloads.

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Machine learning models for classifying coffee fruits detachment force

Meneses,

dos Santos Carreira,

de Almeida Moreira

et al. 2024

Agronomy Journal

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The maturation process of coffee (Coffea arabica) trees exhibits inherent variability, producing fruits at various physiological maturity stages. This variability affects the resistance between the fruit and its peduncle, posing a challenge in mechanized harvesting: non‐selective harvesting. A precise classification of coffee fruit detachment force is essential to address this challenge, ensuring coffee's quality and producer's profitability. This study assesses the efficacy of machine learning (ML) models in determining the detachment force across various coffee cultivars under drip‐irrigated and rainfed conditions. The dataset included detachment force measurements from 24 cultivars—13 drip‐irrigated and 11 rainfed—yielding 1152 data points. Variance analysis compared irrigation methods and three maturity stages: green, cherry, and dry. Detachment force was categorized into four classes based on the dataset's quartile distribution. The ML models utilized were random forest (RF), support vector machine (SVM), K‐nearest neighbors, and artificial neural networks. The SVM model was notably effective in classifying detachment force for rainfed cultivars, with a Matthews correlation coefficient (MCC) of 0.78. In contrast, the RF model was particularly adept for drip‐irrigated cultivars, with an MCC of 0.75. The highest classification accuracies were recorded for the extreme force classes I and IV, with precision values of 0.93 and 0.8, respectively, while classes II and III had lower precision at 0.57 and 0.69. Implementing these ML models for detachment force classification has been beneficial, improving decision‐making in mechanized harvesting systems.

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Deep Network-Based Comprehensive Parotid Gland Tumor Detection

Cited by 38 publications

References 37 publications

Discriminating atypical parotid carcinoma and pleomorphic adenoma utilizing extracellular volume fraction and arterial enhancement fraction derived from contrast‐enhanced CT imaging: A multicenter study

Discriminating atypical parotid carcinoma and pleomorphic adenoma utilizing extracellular volume fraction and arterial enhancement fraction derived from contrast‐enhanced CT imaging: A multicenter study

Deep learning system assisted detection and localization of lumbar spondylolisthesis

Machine learning models for classifying coffee fruits detachment force

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