Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Machine Learning for Medical Data Analysis

Special Issue Editors
Special Issue Information
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 14918

Share This Special Issue

Special Issue Editor

Prof. Dr. Antonio Napolitano

E-Mail Website
Guest Editor

School of Dentistry and Dental Prosthetics, Unicamillus-Saint Camillus International University of Health Sciences, Via di S. Alessandro, 8, 00131 Rome, Italy
Interests: machine learning; computer tomog-raphy (CT), magnetic resonance techniques, neuroimaging applications

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to highlight the most recent research regarding machine learning for medical data analysis. This Special Issue focuses on its development and challenges. The broader aim is to collect high-quality papers from researchers around the world working in this area. Research articles and reviews are solicited that provide comprehensive insight into machine learning for medical data analysis on any aspect of sensor development and applications. Topics of interest include but are not limited to the following:

Machine learning for medical applications;
Medical image analysis/reconstruction;
Medical signal processing;
Medical images;
Sensing, detection, and recognition in medical image/signals;
Medical data acquisition, cleaning, and integration using machine learning;
Data-efficient domain adaptation and transfer learning;
Synthetic data generation and data augmentation for medical applications.

Prof. Dr. Antonio Napolitano
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. Sensors is an international peer-reviewed open access semimonthly 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.

Published Papers (5 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

16 pages, 31571 KiB

Open AccessArticle

Dual-Stage Deeply Supervised Attention-Based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans

by Muhammad Usman, Azka Rehman, Amal Muhammad Saleem, Rabeea Jawaid, Shi-Sub Byon, Sung-Hyun Kim, Byoung-Dai Lee, Min-Suk Heo and Yeong-Gil Shin

Sensors 2022, 22(24), 9877; https://doi.org/10.3390/s22249877 - 15 Dec 2022

Cited by 7 | Viewed by 2644

Abstract

Accurate segmentation of mandibular canals in lower jaws is important in dental implantology. Medical experts manually determine the implant position and dimensions from 3D CT images to avoid damaging the mandibular nerve inside the canal. In this paper, we propose a novel dual-stage deep learning-based scheme for the automatic segmentation of the mandibular canal. In particular, we first enhance the CBCT scans by employing the novel histogram-based dynamic windowing scheme, which improves the visibility of mandibular canals. After enhancement, we designed 3D deeply supervised attention UNet architecture for localizing the Volumes Of Interest (VOIs), which contain the mandibular canals (i.e., left and right canals). Finally, we employed the Multi-Scale input Residual UNet (MSiR-UNet) architecture to segment the mandibular canals using VOIs accurately. The proposed method has been rigorously evaluated on 500 and 15 CBCT scans from our dataset and from the public dataset, respectively. The results demonstrate that our technique improves the existing performance of mandibular canal segmentation to a clinically acceptable range. Moreover, it is robust against the types of CBCT scans in terms of field of view. Full article

(This article belongs to the Special Issue Machine Learning for Medical Data Analysis)

► Show Figures

Figure 1

26 pages, 1422 KiB

Open AccessArticle

Assessment of Immune Status Using Inexpensive Cytokines: A Literature Review and Learning Approaches

by Noor Jamaludeen, Juliane Lehmann, Christian Beyer, Katrin Vogel, Mandy Pierau, Monika Brunner-Weinzierl and Myra Spiliopoulou

Sensors 2022, 22(24), 9785; https://doi.org/10.3390/s22249785 - 13 Dec 2022

Viewed by 2198

Abstract

The emergence of point-of-care (POC) testing has lately been promoted to deliver rapid, reliable medical tests in critical life-threatening situations, especially in resource-limited settings. Recently, POC tests have witnessed further advances due to the technological revolution in smartphones. Smartphones are integrated as reliable readers to the POC results to improve their quantitative detection. This has enabled the use of more complex medical tests by the patient him/herself at home without the need for professional staff and sophisticated equipment. Cytokines, the important immune system biomarkers, are still measured today using the time-consuming Enzyme-Linked Immunosorbent Assay (ELISA), which can only be performed in specially equipped laboratories. Therefore, in this study, we investigate the current development of POC technologies suitable for the home testing of cytokines by conducting a PRISMA literature review. Then, we classify the collected technologies as inexpensive and expensive depending on whether the cytokines can be measured easily at home or not. Additionally, we propose a machine learning-based solution to even increase the efficiency of the cytokine measurement by leveraging the cytokines that can be inexpensively measured to predict the values of the expensive ones. In total, we identify 12 POCs for cytokine quantification. We find that Interleukin 1

β

(IL-1

β

), Interleukin 3 (IL-3), Interleukin 6 (IL-6), Interleukin 8 (IL-8) and Tumor necrosis factor (TNF) can be measured with inexpensive POC technology, namely at home. We build machine-learning models to predict the values of other expensive cytokines such as Interferon-gamma (IFN-

γ

), IL-10, IL-2, IL-17A, IL-17F, IL-4 and IL-5 by relying on the identified inexpensive ones in addition to the age of the individual. We evaluate to what extent the built machine learning models can use the inexpensive cytokines to predict the expensive ones on 351 healthy subjects from the public dataset 10k Immunomes. The models for IFN-

γ

show high results for the coefficient of determination:

R^{2}

= 0.743. The results for IL-5 and IL-4 are also promising, whereas the predictive model of IL-10 achieves only

R^{2}

= 0.126. Lastly, the results demonstrate the vital role of TNF and IL-6 in the immune system due to its high importance in the predictions of all the other expensive cytokines. Full article

(This article belongs to the Special Issue Machine Learning for Medical Data Analysis)

► Show Figures

Figure 1

17 pages, 14982 KiB

Open AccessArticle

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra

by Pietro Manganelli Conforti, Mario D’Acunto and Paolo Russo

Sensors 2022, 22(19), 7492; https://doi.org/10.3390/s22197492 - 03 Oct 2022

Cited by 4 | Viewed by 2144

Abstract

The grading of cancer tissues is still one of the main challenges for pathologists. The development of enhanced analysis strategies hence becomes crucial to accurately identify and further deal with each individual case. Raman spectroscopy (RS) is a promising tool for the classification of tumor tissues as it allows us to obtain the biochemical maps of the tissues under analysis and to observe their evolution in terms of biomolecules, proteins, lipid structures, DNA, vitamins, and so on. However, its potential could be further improved by providing a classification system which would be able to recognize the sample tumor category by taking as input the raw Raman spectroscopy signal; this could provide more reliable responses in shorter time scales and could reduce or eliminate false-positive or -negative diagnoses. Deep Learning techniques have become ubiquitous in recent years, with models able to perform classification with high accuracy in most diverse fields of research, e.g., natural language processing, computer vision, medical imaging. However, deep models often rely on huge labeled datasets to produce reasonable accuracy, otherwise occurring in overfitting issues when the training data is insufficient. In this paper, we propose a chondrogenic tumor CLAssification through wavelet transform of RAman spectra (CLARA), which is able to classify with high accuracy Raman spectra obtained from bone tissues. CLARA recognizes and grades the tumors in the evaluated dataset with

97 %

accuracy by exploiting a classification pipeline consisting of the division of the original task in two binary classification steps, where the first is performed on the original RS signals while the latter is accomplished through the use of a hybrid temporal-frequency 2D transform. Full article

(This article belongs to the Special Issue Machine Learning for Medical Data Analysis)

► Show Figures

Graphical abstract

14 pages, 2637 KiB

Open AccessArticle

IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing

by Muhammad Umar Nasir, Safiullah Khan, Shahid Mehmood, Muhammad Adnan Khan, Atta-ur Rahman and Seong Oun Hwang

Sensors 2022, 22(14), 5444; https://doi.org/10.3390/s22145444 - 21 Jul 2022

Cited by 20 | Viewed by 3028

Abstract

Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma’s manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency. Full article

(This article belongs to the Special Issue Machine Learning for Medical Data Analysis)

► Show Figures

Figure 1

14 pages, 15550 KiB

Open AccessArticle

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

by Atta-ur Rahman, Abdullah Alqahtani, Nahier Aldhafferi, Muhammad Umar Nasir, Muhammad Farhan Khan, Muhammad Adnan Khan and Amir Mosavi

Sensors 2022, 22(10), 3833; https://doi.org/10.3390/s22103833 - 18 May 2022

Cited by 44 | Viewed by 4268

Abstract

Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively. Full article

(This article belongs to the Special Issue Machine Learning for Medical Data Analysis)

► Show Figures

Journal Menu

Journal Browser

Machine Learning for Medical Data Analysis

Share This Special Issue

Special Issue Editor

Special Issue Information

Published Papers (5 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI