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AI in Dentistry: CNNs for Segmenting and Classifying Dental Images

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Manage episode 444599856 series 3605861
Innhold levert av Brian Carter. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Brian Carter eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

This systematic literature review investigates the use of convolutional neural networks (CNNs) for segmenting and classifying dental images. The review analyzes 45 studies that employed CNNs for various tasks, including tooth detection, periapical lesion detection, caries identification, and age and sex determination. The authors explore the different deep learning frameworks and algorithms used in these studies, along with the evaluation metrics employed to assess model performance. They highlight the limitations of existing research, such as the scarcity of publicly available datasets and the need for more robust models capable of handling variations in image quality. The review concludes by identifying areas for future research, emphasizing the need for larger and more diverse datasets, improved image quality, and increased interpretability of deep learning models.

Read more: https://arxiv.org/pdf/2401.09190

  continue reading

44 episoder

Artwork
iconDel
 
Manage episode 444599856 series 3605861
Innhold levert av Brian Carter. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Brian Carter eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

This systematic literature review investigates the use of convolutional neural networks (CNNs) for segmenting and classifying dental images. The review analyzes 45 studies that employed CNNs for various tasks, including tooth detection, periapical lesion detection, caries identification, and age and sex determination. The authors explore the different deep learning frameworks and algorithms used in these studies, along with the evaluation metrics employed to assess model performance. They highlight the limitations of existing research, such as the scarcity of publicly available datasets and the need for more robust models capable of handling variations in image quality. The review concludes by identifying areas for future research, emphasizing the need for larger and more diverse datasets, improved image quality, and increased interpretability of deep learning models.

Read more: https://arxiv.org/pdf/2401.09190

  continue reading

44 episoder

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