The mortality burden of head and neck cancer (HNC) is increasing globally and disproportionately affects people in low-and middle-income countries with limited medical workforce. To address this issue, artificial intelligence (AI) algorithms are increasingly being explored to process medical imaging data, demonstrating competitive performance. However, the clinical adoption of AI remains challenging as clinicians struggle to understand how complex AI works and trust it to use in practice. In addition, AI may not perform well on varying data qualities of endoscopy videos for HNC screening and diagnosis from multiple sites. In this project, our international and interdisciplinary team will collaborate with clinicians from multiple sites (e.g. Singapore, the U.S., and Bangladesh) to collect a diverse, multi-site dataset. In addition, we aim to design and develop computational techniques and practices to improve collaborations between clinicians and AI for the triage and diagnosis of HNC. Specifically, these techniques include a YOLOv5-based glottis detector, a classifier of patient's status using clinical endoscopy videos, uncertainty quantification techniques, and interactive Vision Language Model-based AI explanations, which will enable clinicians to understand AI outputs and provide their inputs to improve AI. After developing our system, we will evaluate the effectiveness of these computational techniques in enabling AI-assisted point-of-care triage and decision-support for HNC, particularly in resource-limited settings.

The American College of Radiology (ACR) has guidelines on appropriate ordering of Magnetic Resonance Imaging (MRI) brain scans. MRI requests are currently manually reviewed by radiologists to ensure compliance to these guidelines. In this paper, we implemented a stacked recurrent neural network (RNN) utilizing a bidirectional long short-term memory (Bi-LSTM) sequence with BioWordVec, a biomedical word embedding vector that uses word representations from a lexicon developed from medical publications, to develop an automated classification system for request audit. To overcome the problems of interpretation by black-box models, the RNN is integrated with a model agnostic explainer LIME (Local Interpretable Model-Agnostic Explanations) to provide explainable support for clinicians in the healthcare environment. The performance of the RNN is compared with a Random Forest (RF) algorithm that utilizes the bag-of-words concept. The RNN was trained and validated on 2470 rows of different patient free-text orders and tested on a separate 2711 orders, producing an accuracy of 82.51% and a ROC value of 0.89 which was either comparable to or surpassing RF both in performance and usability. The use of deep learning with explainable LIME in this study provided a good use case towards an augmented decision-making framework in healthcare.

Previous studies revealed that the ordering of Magnetic resonance imaging (MRI) brain scans following American College of Radiology (ACR) guidelines showed a higher percentage of brain abnormalities compared to scans that do not. As the process of manually labelling patient orders obtained from a local tertiary hospital in accordance to ACR guidelines is intensive and time consuming, this study aims to develop predictive machine learning models; Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and XGBoost (XGB), to automate the classification process through text mining methods and derive insights that are useful for future clinical decision-making and resource optimization. Using 1,924 observations as the labelled training data, RF and XGB were found to be the best performing robust models with ROC values of 0.9459 and 0.9508 respectively on the validation set (481 observations). Further exploration into the interpretability of black-box algorithms using the model agnostic LIME (Local Interpretable Model-Agnostic Explanations) framework was used to generate further insights for decisions made using a separate XGB model with respect to individual patients. The LIME framework is a significant first step towards the development of a comprehensive decision support system for patient-level decisions in the ordering of MRI scans.