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Medicina
Special Issues
AI in Imaging—New Perspectives
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AI in Imaging—New Perspectives

A special issue of Medicina (ISSN 1648-9144).

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 3250

Special Issue Editor

Dr. Ivo Dumić-Čule

E-Mail Website1 Website2 Website3
Guest Editor
Department of Nursing, University North, 42000 Varazdin, Croatia
Interests: artificial intelligence in medicine; musculoskeletal radiology; healthcare management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to provide an overview of all relevant aspects of imaging involving artificial intelligence and machine learning tools:

  • Technical solutions for AI in imaging;
  • Testing of AI-based solutions for imaging in various medical disciplines;
  • Imaging results in clinical practice obtained by AI and ML tools;
  • Short- and long-term experience with AI imaging tools;
  • Education about AI principles;
  • Management of big data;
  • Perception of AI among professionals and patients;
  • Ethical considerations and data ownership.

The authors are also encouraged to submit their technical notes, imaging strategies and decision-making, outcome studies, patient satisfaction studies, ethical considerations, and systematic reviews and meta-analyses for novel techniques. Case series and case reports of high quality would also be considered for publication.

Dr. Ivo Dumić-Čule
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. Medicina 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 1800 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

  • artificial intelligence
  • imaging
  • machine learning
  • radiology
  • ethics
  • big data

Published Papers (2 papers)

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Research

11 pages, 1408 KiB  
Article
Clinical Utility of Breast Ultrasound Images Synthesized by a Generative Adversarial Network
by Shu Zama, Tomoyuki Fujioka, Emi Yamaga, Kazunori Kubota, Mio Mori, Leona Katsuta, Yuka Yashima, Arisa Sato, Miho Kawauchi, Subaru Higuchi, Masaaki Kawanishi, Toshiyuki Ishiba, Goshi Oda, Tsuyoshi Nakagawa and Ukihide Tateishi
Medicina 2024, 60(1), 14; https://doi.org/10.3390/medicina60010014 - 21 Dec 2023
Cited by 2 | Viewed by 1428
Abstract
Background and Objectives: This study compares the clinical properties of original breast ultrasound images and those synthesized by a generative adversarial network (GAN) to assess the clinical usefulness of GAN-synthesized images. Materials and Methods: We retrospectively collected approximately 200 breast ultrasound images for [...] Read more.
Background and Objectives: This study compares the clinical properties of original breast ultrasound images and those synthesized by a generative adversarial network (GAN) to assess the clinical usefulness of GAN-synthesized images. Materials and Methods: We retrospectively collected approximately 200 breast ultrasound images for each of five representative histological tissue types (cyst, fibroadenoma, scirrhous, solid, and tubule-forming invasive ductal carcinomas) as training images. A deep convolutional GAN (DCGAN) image-generation model synthesized images of the five histological types. Two diagnostic radiologists (reader 1 with 13 years of experience and reader 2 with 7 years of experience) were given a reading test consisting of 50 synthesized and 50 original images (≥1-month interval between sets) to assign the perceived histological tissue type. The percentages of correct diagnoses were calculated, and the reader agreement was assessed using the kappa coefficient. Results: The synthetic and original images were indistinguishable. The correct diagnostic rates from the synthetic images for readers 1 and 2 were 86.0% and 78.0% and from the original images were 88.0% and 78.0%, respectively. The kappa values were 0.625 and 0.650 for the synthetic and original images, respectively. The diagnoses made from the DCGAN synthetic images and original images were similar. Conclusion: The DCGAN-synthesized images closely resemble the original ultrasound images in clinical characteristics, suggesting their potential utility in clinical education and training, particularly for enhancing diagnostic skills in breast ultrasound imaging. Full article
(This article belongs to the Special Issue AI in Imaging—New Perspectives)
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12 pages, 10046 KiB  
Article
Detection and Segmentation of Radiolucent Lesions in the Lower Jaw on Panoramic Radiographs Using Deep Neural Networks
by Mario Rašić, Mario Tropčić, Pjetra Karlović, Dragana Gabrić, Marko Subašić and Predrag Knežević
Medicina 2023, 59(12), 2138; https://doi.org/10.3390/medicina59122138 - 9 Dec 2023
Viewed by 1518
Abstract
Background and Objectives: The purpose of this study was to develop and evaluate a deep learning model capable of autonomously detecting and segmenting radiolucent lesions in the lower jaw by utilizing You Only Look Once (YOLO) v8. Materials and Methods: This [...] Read more.
Background and Objectives: The purpose of this study was to develop and evaluate a deep learning model capable of autonomously detecting and segmenting radiolucent lesions in the lower jaw by utilizing You Only Look Once (YOLO) v8. Materials and Methods: This study involved the analysis of 226 lesions present in panoramic radiographs captured between 2013 and 2023 at the Clinical Hospital Dubrava and the School of Dental Medicine, University of Zagreb. Panoramic radiographs included radiolucent lesions such as radicular cysts, ameloblastomas, odontogenic keratocysts (OKC), dentigerous cysts and residual cysts. To enhance the database, we applied techniques such as translation, scaling, rotation, horizontal flipping and mosaic effects. We have employed the deep neural network to tackle our detection and segmentation objectives. Also, to improve our model’s generalization capabilities, we conducted five-fold cross-validation. The assessment of the model’s performance was carried out through metrics like Intersection over Union (IoU), precision, recall and mean average precision (mAP)@50 and mAP@50-95. Results: In the detection task, the precision, recall, mAP@50 and mAP@50-95 scores without augmentation were recorded at 91.8%, 57.1%, 75.8% and 47.3%, while, with augmentation, were 95.2%, 94.4%, 97.5% and 68.7%, respectively. Similarly, in the segmentation task, the precision, recall, mAP@50 and mAP@50-95 values achieved without augmentation were 76%, 75.5%, 75.1% and 48.3%, respectively. Augmentation techniques led to an improvement of these scores to 100%, 94.5%, 96.6% and 72.2%. Conclusions: Our study confirmed that the model developed using the advanced YOLOv8 has the remarkable capability to automatically detect and segment radiolucent lesions in the mandible. With its continual evolution and integration into various medical fields, the deep learning model holds the potential to revolutionize patient care. Full article
(This article belongs to the Special Issue AI in Imaging—New Perspectives)
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