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Fulltext Lung Diseases Detection Using Various Deep Learning Algorithms

Jasmine Pemeena Priyadarsini M, Kotecha K, Rajini GK, Hariharan K, Utkarsh Raj K, Bhargav Ram K, et al.

J Healthc Eng, 2023;2023:3563696.
PMID: 36776955 DOI: 10.1155/2023/3563696

The primary objective of this proposed framework work is to detect and classify various lung diseases such as pneumonia, tuberculosis, and lung cancer from standard X-ray images and Computerized Tomography (CT) scan images with the help of volume datasets. We implemented three deep learning models namely Sequential, Functional & Transfer models and trained them on open-source training datasets. To augment the patient's treatment, deep learning techniques are promising and successful domains that extend the machine learning domain where CNNs are trained to extract features and offers great potential from datasets of images in biomedical application. Our primary aim is to validate our models as a new direction to address the problem on the datasets and then to compare their performance with other existing models. Our models were able to reach higher levels of accuracy for possible solutions and provide effectiveness to humankind for faster detection of diseases and serve as best performing models. The conventional networks have poor performance for tilted, rotated, and other abnormal orientation and have poor learning framework. The results demonstrated that the proposed framework with a sequential model outperforms other existing methods in terms of an F1 score of 98.55%, accuracy of 98.43%, recall of 96.33% for pneumonia and for tuberculosis F1 score of 97.99%, accuracy of 99.4%, and recall of 98.88%. In addition, the functional model for cancer outperformed with an accuracy of 99.9% and specificity of 99.89% and paves way to less number of trained parameters, leading to less computational overhead and less expensive than existing pretrained models. In our work, we implemented a state-of-the art CNN with various models to classify lung diseases accurately.
Fulltext Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging

Awais M, Ghayvat H, Krishnan Pandarathodiyil A, Nabillah Ghani WM, Ramanathan A, Pandya S, et al.

Sensors (Basel), 2020 Oct 12;20(20).
PMID: 33053886 DOI: 10.3390/s20205780

Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to differentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche-Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83%, 85%, and 84%, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia.
Fulltext Clinical remission of rheumatoid arthritis in a multicenter real-world study in Asia-Pacific region

Sun X, Li R, Cai Y, Al-Herz A, Lahiri M, Choudhury MR, et al.

Lancet Reg Health West Pac, 2021 Oct;15:100240.
PMID: 34528015 DOI: 10.1016/j.lanwpc.2021.100240

Background: Clinical remission is an attainable goal for Rheumatoid Arthritis (RA). However, data on RA remission rates from multinational studies in the Asia-Pacific region are limited. We conducted a cross-sectional multicentric study to evaluate the clinical remission status and the related factors in RA patients in the Asia-Pacific region.
Methods: RA patients receiving standard care were enrolled consecutively from 17 sites in 11 countries from APLAR RA SIG group. Data were collected on-site by rheumatologists with a standardized case-report form. Remission was analyzed by different definitions including disease activity score using 28 joints (DAS28) based on ESR and CRP, clinical disease activity index (CDAI), simplified disease activity index (SDAI), Boolean remission definition, and clinical deep remission (CliDR). Logistic regression was used to determine related factors of remission.
Findings: A total of 2010 RA patients was included in the study, the overall remission rates were 62•3% (DAS28-CRP), 35•5% (DAS28-ESR), 30•8% (CDAI), 26•5% (SDAI), 24•7% (Boolean), and 17•1% (CliDR), respectively, and varied from countries to countries in the Asia-Pacific region. Biological and targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) prescription rate was low (17•9%). Compared to patients in non-remission, patients in remission had higher rates of b/tsDMARDs usage and lower rates of GC usage. The favorable related factors were male sex, younger age, fewer comorbidities, fewer extra-articular manifestations (EAM), and use of b/tsDMARDs, while treatment with GC was negatively related to remission.
Interpretation: Remission rates were low and varied in the Asia-Pacific region. Treatment with b/tsDMARDs and less GC usage were related to higher remission rate. There is an unmet need for RA remission in the Asia-Pacific region.

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