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Deep Learning in Medical Image Processing and Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 1486

Special Issue Editors


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Guest Editor
Department of Electrical Engineering, Chang Gung University, Tao-Yuan 33302, Taiwan
Interests: medical imaging processing; pattern recognition; computer visualization; VLSI design
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Informatics, Kainan University, Tao-Yuan 33857, Taiwan
Interests: digital image processing; artificial intelligence; machine vision; digital signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, deep learning (DL) techniques have revolutionized the field of medical image processing, enabling automated analysis of medical images and enhancing the accuracy and efficiency of clinical diagnosis. The use of DL in medical image processing has significantly improved the detection, classification, and segmentation of various pathological conditions, leading to improved patient outcomes. Therefore, the application of DL in medical image processing in clinical settings is becoming increasingly common.

Prof. Dr. Jiann-Der Lee
Dr. Jong-Chih Chien
Guest Editors

Manuscript Submission Information

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Keywords

  • deep learning techniques for medical image analysis
  • AI-based detection and diagnosis of medical images using deep learning
  • AI-based approaches for image reconstruction and enhancement
  • AI-assisted decision making in medical imaging
  • medical image registration using deep learning
  • deep learning for multimodal medical image fusion

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Published Papers (1 paper)

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Review

20 pages, 3843 KiB  
Review
Revolutionizing Periodontal Care: The Role of Artificial Intelligence in Diagnosis, Treatment, and Prognosis
by Giacomo Spartivento, Viviana Benfante, Muhammad Ali, Anthony Yezzi, Domenico Di Raimondo, Antonino Tuttolomondo, Antonio Lo Casto and Albert Comelli
Appl. Sci. 2025, 15(6), 3295; https://doi.org/10.3390/app15063295 - 18 Mar 2025
Viewed by 656
Abstract
This review evaluates the application of artificial intelligence (AI), particularly neural networks, in diagnosing and staging periodontal diseases through radiographic analysis. Using a systematic review of 22 studies published between 2017 and 2024, it examines various AI models, including convolutional neural networks (CNNs), [...] Read more.
This review evaluates the application of artificial intelligence (AI), particularly neural networks, in diagnosing and staging periodontal diseases through radiographic analysis. Using a systematic review of 22 studies published between 2017 and 2024, it examines various AI models, including convolutional neural networks (CNNs), hybrid networks, generative adversarial networks (GANs), and transformer networks. The studies analyzed diverse datasets from panoramic, periapical, and hybrid imaging techniques, assessing diagnostic accuracy, sensitivity, specificity, and interpretability. CNN models like Deetal-Perio and YOLOv5 achieved high accuracy in detecting alveolar bone loss (ABL), with F1 scores up to 0.894. Hybrid networks demonstrate strength in handling complex cases, such as molars and vertical bone loss. Despite these advancements, challenges persist, including reduced performance in severe cases, limited datasets for vertical bone loss, and the need for 3D imaging integration. AI-driven tools offer transformative potential in periodontology by rivaling clinician performance, improving diagnostic consistency, and streamlining workflows. Addressing current limitations with large, diverse datasets and advanced imaging techniques will further optimize their clinical utility. AI stands poised to revolutionize periodontal care, enabling early diagnosis, personalized treatment planning, and better patient outcomes. Full article
(This article belongs to the Special Issue Deep Learning in Medical Image Processing and Analysis)
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