Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in the diagnosis of various diseases, including tropical fevers such as dengue and malaria. However, there is a lack of standard guidelines to develop the AI models, the gap between clinical and engineering expertise and clinical validation of the models, and hence there is a critical need for the development of an integrated diagnostic tool which uses demographical, laboratory variables and epidemiological parameters of patient and provides early prediction.

The present study aimed to develop and evaluate a machine-learning (ML) prediction tool for differential diagnosis of tropical fevers for adult patients (>18 years) using a three-phase approach in a tertiary care centre in South India by January 2026. Phase involves identification of the prevalent tropical fevers and associated clinical parameters to develop the AI model through a retrospective audit and qualitative interview. Phase Ⅱ involves retrospective data collection from hospital medical records for finalised diseases (1000 cases per disease) and clinical parameters, with data being used for model development using the Python language. Support vector machine, logistic regression, K-Nearest Neighbors, Naïve Bayes and ensemble models such as decision tree and Random Forest will be employed along with explainable AI techniques. They are used as they are easy to understand and interpret, well established, most effective for structured data, enhancing the transparency and interpretability of the predictive machine learning models, and their use has been widely supported in previous studies across various contexts. Suitable statistical parameters like specificity, sensitivity and area under receiver operating characteristic (AUROC) will be applied to evaluate model performance. In phase , the developed model will be implemented prospectively to assess the feasibility of model implementation. Model performance such as specificity, sensitivity and AUROC will be calculated, and the finally developed model will be implemented in a single tertiary care hospital to evaluate its overall performance.

Ethical approval for the study has been obtained from the institutional ethics committee of the Kasturba Medical College and Kasturba Hospital, Manipal (IEC number: 6/2024). Informed consent will be taken for obtaining the data of the patient for the evaluation of the model in the third phase of the study, and data will be kept confidential. The study results will be disseminated by publishing them in a peer-reviewed journal.

The protocol has been registered with the Clinical Trial Registry of India (CTRI) (CTRI/2024/04/065866) and approved on 16 April 2024.

To assess the relationship of infant growth, feeding practices and tummy time to their motor development at 12 months, with a special focus on how maternal physical activity during late pregnancy relates to infants’ motor skills.

Longitudinal study.

Rural city in the Mid-Southern USA.

16 singleton pregnant women in the third trimester and their term infants were recruited, excluding mother–infant pairs with health issues that impact infants’ motor development and restrict mothers’ physical activity.

Maternal physical activity and sedentary time during the third trimester were measured using Actigraph activity monitors. Labour nurses measured neonatal birth weight and length using standard procedures. Infants’ motor percentiles at 4 and 12 months were measured respectively using the Alberta Infant Motor Scale and Peabody Developmental Motor Scales II test by a licensed paediatric physical therapist. Feeding practices, infants’ time spent in different positions and family composition were evaluated separately at 4 and 12 months using a study-specific survey.

Infant motor percentiles at 4 months were positively associated with their 12-month motor percentiles (r=0.649, p=0.009). For each additional percentile at 4 months, the mean 12-month percentile increased by 0.4. Motor percentiles at 12 months were also positively associated with infants’ birth weight (r=0.553, p=0.026) and length (r=0.637, p=0.008), but not significantly associated with tummy time (r=–0.069, p=0.840). Infant motor percentiles at 12 months were not associated with time spent sedentary (r=–0.134, p=0.634), light activity (r=0.213, p=0.447) or moderate activity (r=–0.050, p=0.858) during the third trimester. At 12 months, breastfeeding status (p=0.576) and having siblings (p=0.230) were not related to motor scores.

Motor percentiles at 4 months, birth weight and length correlated with motor skills at 12 months, whereas tummy time, siblings, and breastfeeding were not significant predictors. Physical activity during pregnancy did not significantly correlate to motor skills at 12 months.