AI-augmented clinical decision in paediatric appendicitis: can an AI-generated model improve trainees' diagnostic capability?

Accurate diagnosis of paediatric appendicitis remains a challenge due to its diverse clinical presentations and reliance on subjective assessments. The integration of artificial intelligence (AI) with an expert's ''clinical sense'' has the potential to improve diagnostic accuracy. In this study, we aimed to evaluate the effectiveness of the Artificial Intelligence Pediatric Appendicitis Decision-tree (AiPAD) model in enhancing the diagnostic capabilities of trainees and compare their performance with that of an expert supervisor. Between March 2019 and October 2022, we included paediatric patients aged 0-12 years who were referred for suspected appendicitis. Trainees collected clinical findings using five predefined parameters before ordering any imaging studies. The AiPAD model, which was blinded to the surgical team, made predictions from the supervisor's and trainees' findings independently. The diagnosis verdicts of the supervisor and the trainees were statistically evaluated in comparison to the prediction of the AI model, taking into account the revealed correct diagnosis. A total of 136 cases were included, comprising 58 cases of acute appendicitis (AA) and 78 cases of non-appendicitis (NA). The supervisor's correct verdict showed 91% accuracy compared to an average of 70% for trainees. However, if trainees were enabled with AiPAD, their accuracy would improve significantly to an average of 97%. Significantly, a strong association was observed between the expert's clinical sense and the predictions generated by AiPAD. CONCLUSION:  The utilisation of the AiPAD model in diagnosing paediatric appendicitis has significant potential to improve trainees' diagnostic accuracy, approaching the level of an expert supervisor. This hybrid approach combining AI and expert knowledge holds promise for enhancing diagnostic capabilities, reducing medical errors and improving patient outcomes. WHAT IS KNOWN: • Sharpening clinical judgement for pediatric appendicitis takes time and seasoned exposure. Traditional training leaves junior doctors yearning for a faster path to diagnostic mastery. WHAT IS NEW: • AI-generated models unlock the secrets of expert intuition, crafting an explicit guide for juniors to rapidly elevate their diagnostic skills. This leapfrog advancement empowers young doctors, democratizing medical expertise and paving the way for brighter outcomes in clinical training.

The Development and Validation of Artificial Intelligence Pediatric Appendicitis Decision-Tree for Children 0 to 12 Years Old.

INTRODUCTION: Diagnosing appendicitis in young children (0-12 years) still poses a special difficulty despite the advent of radiological investigations. Few scoring models have evolved and been applied worldwide, but with significant fluctuations in accuracy upon validation. AIM: To utilize artificial intelligence (AI) techniques to develop and validate a diagnostic model based on clinical and laboratory parameters only (without imaging), in addition to prospective validation to confirm the findings. METHODS: In Stage-I, observational data of children (0-12 years), referred for acute appendicitis (March 1, 2016-February 28, 2019, n = 166), was used for model development and evaluation using 10-fold cross-validation (XV) technique to simulate a prospective validation. In Stage-II, prospective validation of the model and the XV estimates were performed (March 1, 2019-November 30, 2021, n = 139). RESULTS: The developed model, AI Pediatric Appendicitis Decision-tree (AiPAD), is both accurate and explainable, with an XV estimation of average accuracy to be 93.5% ± 5.8 (91.4% positive predictive value [PPV] and 94.8% negative predictive value [NPV]). Prospective validation revealed that the model was indeed accurate and close to the XV evaluations, with an overall accuracy of 97.1% (96.7% PPV and 97.4% NPV). CONCLUSION: The AiPAD is validated, highly accurate, easy to comprehend, and offers an invaluable tool to use in diagnosing appendicitis in children without the need for imaging. Ultimately, this would lead to significant practical benefits, improved outcomes, and reduced costs.

Gaining Insights on Nasopharyngeal Carcinoma Treatment Outcome Using Clinical Data Mining Techniques.

The analysis of Electronic Health Records (EHRs) is attracting a lot of research attention in the medical informatics domain. Hospitals and medical institutes started to use data mining techniques to gain new insights from the massive amounts of data that can be made available through EHRs. Researchers in the medical field have often used descriptive statistics and classical statistical methods to prove assumed medical hypotheses. However, discovering new insights from large amounts of data solely based on experts' observations is difficult. Using data mining techniques and visualizations, practitioners can find hidden knowledge, identify interesting patterns, or formulate new hypotheses to be further investigated. This paper describes a work in progress on using data mining methods to analyze clinical data of Nasopharyngeal Carcinoma (NPC) cancer patients. NPC is the fifth most common cancer among Malaysians, and the data analyzed in this study was collected from three states in Malaysia (Kuala Lumpur, Sabah and Sarawak), and is considered to be the largest up-to-date dataset of its kind. This research is addressing the issue of cancer recurrence after the completion of radiotherapy and chemotherapy treatment. We describe the procedure, problems, and insights gained during the process.