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Diagnostics
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Clinical Diagnosis, Prognosis and Data Analysis of Medical Parasites and...
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Clinical Diagnosis, Prognosis and Data Analysis of Medical Parasites and Arthropods

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Diagnostic Microbiology and Infectious Disease".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 1816

Special Issue Editors

Prof. Dr. Tamer Sanlidag

E-Mail Website
Guest Editor
DESAM Research Institute, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
Interests: virology; microbiology; COVID-19; mathematical modelling; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Dilber Uzun Ozsahin

E-Mail Website
Guest Editor
1. Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates
2. Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
Interests: AI in healthcare; decision making in healthcare; medical imaging; nuclear medicine imaging devices
Special Issues, Collections and Topics in MDPI journals
Dr. Ilker Ozsahin

E-Mail Website
Guest Editor
1. Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
2.Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey;
Interests: medical imaging; radiology; operational research; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Parasitic diseases have a considerable socio-economic impact on society. Globally, around 3.5 billion people are affected by intestinal parasitic infections, and more than 200,000 related deaths are reported annually. Many arthropods play a critical role in human health, acting as vectors and intermediate hosts of human pathogens and displaying the potential to cause outbreaks in overcrowded areas.

In medical parasitology, diagnoses are mainly based on traditional diagnostic methods. Microscopy, rapid diagnostic tests (RDTs), and PCR are the most commonly used diagnostic methods.

To overcome the limitations of traditional diagnostic methods in parasite and arthropod identification, advanced diagnostic approaches, such as artificial intelligence technology, have emerged. Artificial intelligence (AI) algorithms have been highly developed, and deep learning algorithms have also emerged, becoming an important component of clinical microbiology informatics. AI in general and computer vision specifically are emerging tools that clinical microbiologists need to study, develop, and implement in order to improve clinical microbiology.

This Special Issue aims to invite authors to present their experiences in the diagnosis, prognosis, and data analysis of medical parasites and arthropods.

Prof. Dr. Tamer Sanlidag
Dr. Dilber Uzun Ozsahin
Dr. Ilker Ozsahin
Guest Editors

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. Diagnostics 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.

Keywords

  • clinical diagnosis
  • prognosis
  • data analysis
  • medical parasites
  • arthropods

Published Papers (2 papers)

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13 pages, 1104 KiB  
Article
Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random Forest
by Dilber Uzun Ozsahin, Basil Barth Duwa, Ilker Ozsahin and Berna Uzun
Diagnostics 2024, 14(4), 385; https://doi.org/10.3390/diagnostics14040385 - 09 Feb 2024
Viewed by 834
Abstract
Malaria continues to be a major barrier to socioeconomic development in Africa, where its death rate is over 90%. The predictive power of many machine learning models—such as multi-linear regression (MLR), artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFISs) and Random Forest [...] Read more.
Malaria continues to be a major barrier to socioeconomic development in Africa, where its death rate is over 90%. The predictive power of many machine learning models—such as multi-linear regression (MLR), artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFISs) and Random Forest classifier—is investigated in this study using data from 2207 patients. The dataset was reduced from the initial dataset of thirty-two criteria samples to fifteen. Assessment measures such as the root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), and adjusted correlation coefficient R were used. ANFIS, Random Forest, MLR, and ANN are among the models. After training, ANN outperforms ANFIS (97%), MLR (92%), and Random Forest (68%) with the greatest R (99%) and R2 (99%), respectively. The testing stage confirms the superiority of ANN. The paper also presents a statistical forecasting sheet with few errors and excellent accuracy for MLR models. When the models are assessed with Random Forest, the latter shows the least results, thus broadening the modeling techniques and offering significant insights into the prediction of malaria and healthcare decision making. The outcomes of using machine learning models for precise and efficient illness prediction add to an expanding body of knowledge, assisting healthcare systems in making better decisions and allocating resources more effectively. Full article
(This article belongs to the Special Issue Clinical Diagnosis, Prognosis and Data Analysis of Medical Parasites and Arthropods)
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5 pages, 2317 KiB  
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Pulmonary and Liver Toxocariasis Mimicking Metastatic Tumors in a Patient with Colon Cancer
by Miju Cheon and Jang Yoo
Diagnostics 2024, 14(1), 58; https://doi.org/10.3390/diagnostics14010058 - 26 Dec 2023
Viewed by 730
Abstract
Toxocariasis is an uncommon cause of multiple cavitary lung lesions and an ill-defined liver lesion. We herein report a patient with lung and liver toxocariasis, which mimicked metastatic lesions of colon cancer on 18F-FDG PET–CT and chest and abdominal CT performed for [...] Read more.
Toxocariasis is an uncommon cause of multiple cavitary lung lesions and an ill-defined liver lesion. We herein report a patient with lung and liver toxocariasis, which mimicked metastatic lesions of colon cancer on 18F-FDG PET–CT and chest and abdominal CT performed for cancer staging after diagnosis of colon cancer. The patient was diagnosed with lung and liver toxocariasis by a positive enzyme-linked immunosorbent assay. Lung toxocariasis may occur as multiple cavitary lung lesions, and liver toxocariasis may appear as a solitary ill-defined nodule, which may be misdiagnosed as metastatic tumors. Clinicians should consider toxocariasis when multiple cavitary lung lesions and a solitary ill-defined focal liver lesion are detected, especially in a patient with cancer. Full article
(This article belongs to the Special Issue Clinical Diagnosis, Prognosis and Data Analysis of Medical Parasites and Arthropods)
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