ArticlePLoS neglected tropical diseases2022
Artificial intelligence in differentiating tropical infections: A step ahead.
Article in PLoS neglected tropical diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07328997 (Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence), which is not on this map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning and Deep Learning Techniques for Prediction and Diagnosis of Leptospirosis: Systematic Literature Review.JMIR medical informatics · 2025Pooled it
- AI-Assisted Differentiation of Dengue and Chikungunya Using Big, Imbalanced Epidemiological Data.Tropical medicine and infectious disease · 2026Article
- Article
- Reinventing malaria control: hierarchical intervention strategies for African settings based on the microbiota-barrier-innate immunity axis.Frontiers in immunology · 2026Review
- Article
- Article
- Validation of artificial intelligence-based digital microscopy for automated detection of Schistosoma haematobium eggs in urine in Gabon.PLoS neglected tropical diseases · 2024Article
- Opportunities, challenges and future perspectives of using bioinformatics and artificial intelligence techniques on tropical disease identification using omics data.Frontiers in digital health · 2024Review
- Possible role of artificial intelligence in diagnosis of cases with non-specific signs and symptoms of dengue: A comment.Clinics (Sao Paulo, Brazil) · 2024Article
- Development of a low-cost robotized 3D-prototype for automated optical microscopy diagnosis: An open-source system.PloS one · 2024Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveDifferentiating tropical infections are difficult due to its homogenous nature of clinical and laboratorial presentations among them. Sophisticated differential tests and prediction tools are better ways to tackle this issue. Here, we aimed to develop a clinician assisted decision making tool to differentiate the common tropical infections. METHODOLOGY: A cross sectional study through 9 item self-administered questionnaire were performed to understand the need of developing a decision making tool and its parameters. The most significant differential parameters among the identified infections were measured through a retrospective study and decision tree was developed. Based on the parameters identified, a multinomial logistic regression model and a machine learning model were developed which could better differentiate the infection.
resultsA total of 40 physicians involved in the management of tropical infections were included for need analysis. Dengue, malaria, leptospirosis and scrub typhus were the common tropical infections in our settings. Sodium, total bilirubin, albumin, lymphocytes and platelets were the laboratory parameters; and abdominal pain, arthralgia, myalgia and urine output were the clinical presentation identified as better predictors. In multinomial logistic regression analysis with dengue as a reference revealed a predictability of 60.7%, 62.5% and 66% for dengue, malaria and leptospirosis, respectively, whereas, scrub typhus showed only 38% of predictability. The multi classification machine learning model observed to have an overall predictability of 55-60%, whereas a binary classification machine learning algorithms showed an average of 79-84% for one vs other and 69-88% for one vs one disease category.
conclusionThis is a first of its kind study where both statistical and machine learning approaches were explored simultaneously for differentiating tropical infections. Machine learning techniques in healthcare sectors will aid in early detection and better patient care.
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