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Medical Diagnosis - Europe PMC

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Last Updated: 19 June 2022

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Early infant diagnosis of HIV infection at the John F. Kennedy Medical Center, Monrovia, Liberia.

"Background: The overwhelming number of new HIV infections among children worldwide are due to vertical transmission. Following the introduction and extension of prevention of MTCT programs, HIV in Africa stood at 25 percent in 2004 - 40%, but following implementation and expansion of prevention of MTCT programs, the incidence of HIV in Africa has drastically reduced to less than 5% in most African countries. Object: To determine the prevalence and causes of vertical transmission of HIV among attendees of an early infant diagnosis service at an academic and community-based tertiary facility in Liberia's early infant diagnosis program. Methods: A retrospective analysis of medical records of babies seen at the Pediatric Unit of the Infectious Disease Clinic of John F Kennedy Medical Center in Monrovia, Liberia, between January 1, 2016 and December 31, 2020. Both groups were children born to HIV-positive mothers and who underwent HIV DNA PCR testing between the ages of 6 weeks and 6 months. 1. 3:1. Result: During the study period, 284 children had a HIV DNA PCR test with a male:female ratio of 1. 3:1. For 239 mothers who had full PMTCT, 1. 3% of their children were positive, while for 45 mothers with partial PMTCT, 28. 8% of their children were positive. Both children were breastfed, 13 of whom were positive, 13 of whom were positive, while two children who were mixed fed were positive. Children who received Nevirapine vs no prophylaxis were more likely to have negative HIV tests," according to a caesarian section vs. vaginal delivery and full versus partial participation in PMTCT programs. Or = 4. 02 [95% CI 2. 06 - 4. 13] Children who received Nevirapine vs. no prophylaxis were more likely to have negative HIV tests," the most likely to have negative HIV positive results were more likely to participate in PMTCT.

Source link: https://europepmc.org/article/MED/35703420


Recent Advancements in Multimodal Medical Image Fusion Techniques for Better Diagnosis: An overview.

"A Medical imaging plays a vital role in medical diagnosis and clinical therapy. " Medical Image Fusion is the process of combining multiple medical images from various media formats into a single fused image. The main aim of the medical image fusion is to obtain a large amount of relevant data to improve the quality and make it more useful for improving clinical therapy for better diagnosis and more concrete identification of medical related problems.

Source link: https://europepmc.org/article/MED/35670346


Advanced Medical Images Recognition and Diagnosis of Respiratory System Viruses

"Respiratory infections are a difficult and time-consuming task of continuously reviewing clinical photographs of patients. " To prevent disease transmission, there's a need to develop and refine the respiratory case prediction system for Covid-19 and Viral Pneumonia as soon as possible. Deep learning makes it possible to discover that respiratory viruses such as Covid-19 and Viral Pneumonia can be quickly identified using its classification systems such as CNN. MFCC is a common and cost-effective method of signal processing. Experimental results show that using a CT image converted to Mel-frequency cepstral spectrogram as input to CNN can lead to high accuracy results; with the classification of validation data of 100 percent reliability of the correct Covid-19 and Viral Pneumonia categories and images with the normal healthy label. Therefore, it will almost be used to determine whether or not Covid-19 or Viral Pneumonia are present in the CT photos. ".

Source link: https://europepmc.org/article/PPR/PPR500509


Computer-aided Diagnosis through Medical Image Retrieval in Radiology

"Decision support systems can be used to prioritize and assist radiologists in making faster decisions. " In this sense, medical content-based image retrieval services can be of utmost value by delivering well-curated similar examples. Nonetheless, most medical content-based image retrieval schemes work by finding the most similar image, which is not equivalent to finding the most similar image in terms of disease and its severity. We recommend an interpretability-driven and an attention-driven medical image retrieval device here. We carried out experiments in a large and publicly available database of chest radiographs with organized labels derived from free-text radiology studies. We investigated the results on two common conditions: pleural effusion and pneumonia. We also quantitatively reviewed the proposed solutions by calculating the normalized Discounted Cumulative Gain based on our ground-truth ranking and qualitatively evaluated the results. The Interpretability-guided approach stands out against the other state-of-the-art approaches and exhibits the most satisfactory agreement with the most experienced radiologist, according to the authors.

Source link: https://europepmc.org/article/PPR/PPR500275


A novel method using Covid-19 dataset and machine learning algorithms FOR THE MOST ACCURATE DIAGNOSIS that can be obtained in medical diagnosis.

"Pandemics and several other diseases threaten human life, health, and quality of life in several ways. " For this reason, the medical diagnosis to be used for any disease is vital in terms of the most accurate diagnosis by the doctors and the most appropriate therapy for the identified diagnosis. The COVID-19 pandemic, which began in China in December 2019, has spread around the world in a short time. Doctors in multiple countries' health sector were also caught off guard due to the rapid dissemination of COVID-19. Machine Learning Algorithms are of utmost importance in the establishment of computer-aided early and accurate diagnosis devices in today's healthcare field, as they greatly support doctors in the medical diagnosis process. Using the COVID-19 image images, a tool was developed for the most accurate diagnosis of COVID-19 patients in this research.

Source link: https://europepmc.org/article/MED/35663432


Pulmonary Nodule Clinical Trial Data Collection and Intelligent Differential Diagnosis for Medical Internet of Things.

"The inherent characteristics of EEG signals are investigated in the threshold compression module, and the resulting EEG data are divided into several symbolic streams and compressed by different thresholds to reduce the compression ratio while still maintaining the application's quality. " Each AIADS' sensitivity in determining lung nodules under different convolution kernel conditions, false positives, false positives, relative volume errors, and relative volume errors, as well as the success of each AIADS in estimating the four types of nodules are estimated. The proposed model not only produces interpretable lung nodule classification results, but also improves lung nodule classification results with an accuracy rate of 97 percent. ".

Source link: https://europepmc.org/article/MED/35685674

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions