Response to Letters to the Editor regarding the editorial “Artificial intelligence, machine learning, and the human interface in medicine: is there a sweet spot for oral and maxillofacial radiology?”

2019 ◽  
Vol 128 (4) ◽  
pp. 445
Author(s):  
Aditya Tadinada
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Sweet Spot ◽  
Maxillofacial Radiology ◽  
Letters To The Editor ◽  
Human Interface ◽  
Oral And Maxillofacial Radiology

scholarly journals A U-Net Approach to Apical Lesion Segmentation on Panoramic Radiographs

2022 ◽  
Vol 2022 ◽  
pp. 1-7
Author(s):  
Ibrahim S. Bayrakdar ◽  
Kaan Orhan ◽  
Özer Çelik ◽  
Elif Bilgir ◽  
Hande Sağlam ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Maxillofacial Radiology ◽  
Lesion Segmentation ◽  
Data Set ◽  
Panoramic Radiographs ◽  
Periapical Lesions ◽  
Oral And Maxillofacial Radiology ◽  
Apical Lesions ◽  
Clinical Problems

The purpose of the paper was the assessment of the success of an artificial intelligence (AI) algorithm formed on a deep-convolutional neural network (D-CNN) model for the segmentation of apical lesions on dental panoramic radiographs. A total of 470 anonymized panoramic radiographs were used to progress the D-CNN AI model based on the U-Net algorithm (CranioCatch, Eskisehir, Turkey) for the segmentation of apical lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Eskisehir Osmangazi University. A U-Net implemented with PyTorch model (version 1.4.0) was used for the segmentation of apical lesions. In the test data set, the AI model segmented 63 periapical lesions on 47 panoramic radiographs. The sensitivity, precision, and F1-score for segmentation of periapical lesions at 70% IoU values were 0.92, 0.84, and 0.88, respectively. AI systems have the potential to overcome clinical problems. AI may facilitate the assessment of periapical pathology based on panoramic radiographs.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

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