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Biomedicines
Special Issues
Utilizing Biomedical Image Analysis for Translational Research and Precision Medicine
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Utilizing Biomedical Image Analysis for Translational Research and Precision Medicine

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Biomedical Engineering and Materials".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 2364

Special Issue Editor

Dr. Nir Pillar

E-Mail Website
Guest Editor
Department of Electrical Engineering, University of California, Los Angeles, CA, USA
Interests: pathology; cancer; deep learning; image analysis; virtual staining; genomics

Special Issue Information

Dear Colleagues,

The field of pathology has undergone tremendous changes in the last decade. Whole slide image (WSI) scanning has become more available in many clinical labs, transitioning pathologists from the microscope to the digital screen. Artificial intelligence (AI) algorithms are rapidly evolving to analyze digital slide images, and have shown significant progress across many tasks, including detecting distant metastases, classifying cancers, predicting survival, and predicting treatment response, among others.

At the same time, recent advances in spatially resolved imaging, which have greatly expanded the knowledge of complex multicellular biological systems, are venturing into experimental pathology and shedding light on cellular composition and interaction within human tissues, allowing for accurate disease stratification and treatment response prediction.

This Special Issue aims to focus on the latest advances in data-driven pathology and spatial imaging, and their potential to transform our understanding of human diseases. This includes the development and exploration of novel AI algorithms for digital imaging and spatial tissue morphology analysis, the integration of imaging technologies for higher diagnostic accuracy, and utilizing them for precision medicine.

Dr. Nir Pillar
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. Biomedicines 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

  • digital pathology
  • deep learning
  • AI
  • spatial analysis
  • early detection
  • precision medicine
  • cancer
  • cellular composition

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

Published Papers (1 paper)

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Research

12 pages, 4744 KiB  
Article
A Computer-Assisted Diagnostic Method for Accurate Detection of Early Nondisplaced Fractures of the Femoral Neck
by S. L. Hsieh, J. L. Chiang, C. H. Chuang, Y. Y. Chen and C. J. Hsu
Biomedicines 2023, 11(11), 3100; https://doi.org/10.3390/biomedicines11113100 - 20 Nov 2023
Cited by 6 | Viewed by 1979
Abstract
Nondisplaced femoral neck fractures are sometimes misdiagnosed by radiographs, which may deteriorate into displaced fractures. However, few efficient artificial intelligent methods have been reported. We developed an automatic detection method using deep learning networks to pinpoint femoral neck fractures on radiographs to assist [...] Read more.
Nondisplaced femoral neck fractures are sometimes misdiagnosed by radiographs, which may deteriorate into displaced fractures. However, few efficient artificial intelligent methods have been reported. We developed an automatic detection method using deep learning networks to pinpoint femoral neck fractures on radiographs to assist physicians in making an accurate diagnosis in the first place. Our proposed accurate automatic detection method, called the direction-aware fracture-detection network (DAFDNet), consists of two steps, namely region-of-interest (ROI) segmentation and fracture detection. The first step removes the noise region and pinpoints the femoral neck region. The fracture-detection step uses a direction-aware deep learning algorithm to mark the exact femoral neck fracture location in the region detected in the first step. A total of 3840 femoral neck parts in anterior–posterior (AP) pelvis radiographs collected from the China Medical University Hospital database were used to test our method. The simulation results showed that DAFDNet outperformed the U-Net and DenseNet methods in terms of the IOU value, Dice value, and Jaccard value. Our proposed DAFDNet demonstrated over 94.8% accuracy in differentiating non-displaced Garden type I and type II femoral neck fracture cases. Our DAFDNet method outperformed the diagnostic accuracy of general practitioners and orthopedic surgeons in accurately locating Garden type I and type II fracture locations. This study can determine the feasibility of applying artificial intelligence in a clinical setting and how the use of deep learning networks assists physicians in improving correct diagnoses compared to the current traditional orthopedic manual assessments. Full article
(This article belongs to the Special Issue Utilizing Biomedical Image Analysis for Translational Research and Precision Medicine)
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