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Special Issues
Medical Image Processing Use in Personalized Medicine
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Medical Image Processing Use in Personalized Medicine

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Omics/Informatics".

Deadline for manuscript submissions: closed (25 December 2024) | Viewed by 1817

Special Issue Editor

Dr. Boran Şekeroğlu

E-Mail Website
Guest Editor
Applied Artificial Intelligence Research Center, Near East University, Nicosia, Cyprus
Interests: AI; machine learning; computer vision

Special Issue Information

Dear Colleagues,

Image processing is still of significant importance in medical applications, particularly in personalized medicine. It is used for preprocessing, postprocessing, content, and information analysis prior to diagnostic tools such as a tumor or abnormal cell segmentation, disease classification, and personalized treatment. Researchers can make advanced deep learning methods more effective using medical image processing techniques and help reveal personalized disease characteristics to make treatments more effective. Therefore, comprehensive analyses could improve diagnostic accuracy and therapeutic decision-making. This Special Issue seeks to bring together different disciplines, such as image processing, computer vision, and medicine, to provide outstanding approaches to personalized medicine and improved patient outcomes.

Dr. Boran Şekeroğlu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • medical image processing
  • low-level image preprocessing
  • deep learning
  • segmentation
  • disease classification
  • personalized medicine
  • bioinformatics

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

14 pages, 12006 KiB  
Article
Comparative Evaluation of Deep Learning Models for Diagnosis of Helminth Infections
by Omid Mirzaei, Ahmet Ilhan, Emrah Guler, Kaya Suer and Boran Sekeroglu
J. Pers. Med. 2025, 15(3), 121; https://doi.org/10.3390/jpm15030121 - 20 Mar 2025
Viewed by 419
Abstract
(1) Background: Helminth infections are a widespread global health concern, with Ascaris and taeniasis representing two of the most prevalent infestations. Traditional diagnostic methods, such as egg-based microscopy, are fraught with challenges, including subjectivity and low throughput, often leading to misdiagnosis. This [...] Read more.
(1) Background: Helminth infections are a widespread global health concern, with Ascaris and taeniasis representing two of the most prevalent infestations. Traditional diagnostic methods, such as egg-based microscopy, are fraught with challenges, including subjectivity and low throughput, often leading to misdiagnosis. This study evaluates the efficacy of advanced deep learning models in accurately classifying Ascaris lumbricoides and Taenia saginata eggs from microscopic images, proposing a technologically enhanced approach for diagnostics in clinical settings. (2) Methods: Three state-of-the-art deep learning models, ConvNeXt Tiny, EfficientNet V2 S, and MobileNet V3 S, are considered. A diverse dataset comprising images of Ascaris, Taenia, and uninfected eggs was utilized for training and validating these models by performing multiclass experiments. (3) Results: All models demonstrated high classificatory accuracy, with ConvNeXt Tiny achieving an F1-score of 98.6%, followed by EfficientNet V2 S at 97.5% and MobileNet V3 S at 98.2% in the experiments. These results prove the potential of deep learning in streamlining and improving the diagnostic process for helminthic infections. The application of deep learning models such as ConvNeXt Tiny, EfficientNet V2 S, and MobileNet V3 S shows promise for efficient and accurate helminth egg classification, potentially significantly enhancing the diagnostic workflow. (4) Conclusion: The study demonstrates the feasibility of leveraging advanced computational techniques in parasitology and points towards a future where rapid, objective, and reliable diagnostics are standard. Full article
(This article belongs to the Special Issue Medical Image Processing Use in Personalized Medicine)
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14 pages, 7475 KiB  
Article
The Three-Dimensional Investigation of the Radiographic Boundary of Mandibular Full-Arch Distalization in Different Facial Patterns
by Yin-Yu Chou, Chia-Hsuan Chan, Yu-Jen Chang, Shiu-Shiung Lin, Chen-Feng Cheng and Te-Ju Wu
J. Pers. Med. 2024, 14(11), 1071; https://doi.org/10.3390/jpm14111071 - 24 Oct 2024
Viewed by 965
Abstract
Objective: Mandibular full-arch distalization (MFD) is a popular approach, particularly in non-extraction cases. However, we still cannot confirm whether facial patterns affect the amount of limits. This study aimed to determine the anatomical MFD limits in patients with different facial patterns. Study design: [...] Read more.
Objective: Mandibular full-arch distalization (MFD) is a popular approach, particularly in non-extraction cases. However, we still cannot confirm whether facial patterns affect the amount of limits. This study aimed to determine the anatomical MFD limits in patients with different facial patterns. Study design: Using computed tomography (CT), the shortest distances from the mandibular second molar to the inner cortex of the mandibular lingual surface and from the lower central incisor to the inner cortex of the lingual mandibular symphysis were measured in 60 samples (30 patients). The available distalization space in both regions was compared between groups with different facial patterns. Results: The available space in symphysis was more critical than that in retromolar area: the shortest distances to the inner cortex of the lingual mandibular symphysis at root levels 8 mm apical to the cementoenamel junction of the incisor were 1.28, 1.60, and 3.48 mm in the high-, normal-, and low-angle groups, respectively. Conclusions: Facial patterns affected the MFD capacity, and the thickness of the lingual mandibular symphysis was the most critical anatomic limit encountered. Practitioners should always pay attention to the possible impacts from facial patterns, especially in the treatment of high-angle cases. Full article
(This article belongs to the Special Issue Medical Image Processing Use in Personalized Medicine)
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