Predicting Memory Score Using Paralinguistic Features.

An automated method of assessing short term memory can act as a dementia risk predictor, as poor short-term memory is strongly linked to early signs of dementia. While previous works show the feasibility of using speech to predict healthy and diagnosed dementia participants, there are still gaps in predicting 'dementia risk' and clear difficulties distinguishing early dementia with regular ageing. We extracted paralinguistic features from audio of individuals completing an over the phone episodic memory test, LOGOS. These paralinguistic features were used to discriminate between those with strong and poor short term memory performance. This work also explored various feature selection methods and tested this method across multiple datasets. Our best result was achieved using a Support Vector Machine (SVM) classifier, obtaining accuracy of 84% per audio recording.Clinical relevance- This work establishes the efficacy of using speech from older participants completing the LOGOS episodic memory test to estimate risk of dementia.

Domain and Patient Adversarial Multi-Task Learning for Arrhythmia Classification.

Manual screening of electrocardiograms (ECGs) for heart arrhythmias by clinicians is time-consuming and labor-intensive. A machine learning model for the automated diagnosis of heart arrhythmia from ECG signals can facilitate improved diagnosis, greater accessibility and earlier intervention for patients. The potential of such models is limited however by the small size of clinical datasets available for training. Methods that can be trained with multiple datasets to classify heart arrhythmia are needed to overcome this problem.In this paper, we propose using adversarial multi-task learning (AMTL) to extract domain and patient invariant features from two electrocardiogram databases. We further investigated the influence of beat segmentation location and beat normalization on domain invariance. Our proposed methods were tested on the MIT-BIH Arrhythmia and the St Petersburg INCART 12-lead Arrhythmia Databases. The domain adversarial models achieved a higher accuracy and average F1 score than their counterparts without domain adversarial learning. In particular, the patient and domain adversarial model improved the F1 scores on the two tested databases from 70% and 74% to 77% each.Clinical Relevance-This establishes that adversarial multitask learning with multiple datasets and multiple adversarial tasks can improve the F1 score of arrhythmia classification.

Equitable access to speech practice for rural Australian children using the SayBananas! mobile game.

PURPOSE: To evaluate the effect, usage, and user-experience for SayBananas!, a Mario-style mobile game providing Australian children access to high-dose individualised speech therapy practice. METHOD: Participants were 45 rural Australian children with speech sound disorders (SSD; 4;4-10;5 years) with internet access. This mixed-methods study involved: (a) recruitment, (b) eligibility screening, (c) questionnaire, (d) online pre-assessment, (e) SayBananas! intervention using motor learning principles (4 weeks, 10-15 target words), and (f) online post-assessment and interview. Usage and performance were automatically monitored. RESULT: Most participants were highly engaged with SayBananas! completing a median of 44.71 trials/session (∼45% of the 100 trial/session target, range 7-194). After intervention, participants made significant gains on treated words and on formal assessment of percentage of consonants, vowels, and phonemes correct. There was no reliable change for parent-rated intelligibility or children's feelings about talking. The number of practice sessions was significantly correlated with percent change on treated words. On average, children rated the app as "happy/good/fun" providing detailed drawings of playing SayBananas!. Families provided high ratings of engagement, functionality, aesthetics, and quality. CONCLUSION: SayBananas! is a viable and engaging solution for rural Australian children with SSD to gain access to equitable, cost-effective speech practice. The amount of app use was associated with amount of speech production improvement over a 4-week period.

Influence of Channel Selection and Subject's Age on the Performance of the Single Channel EEG-Based Automatic Sleep Staging Algorithms.

The electroencephalogram (EEG) signal is a key parameter used to identify the different sleep stages present in an overnight sleep recording. Sleep staging is crucial in the diagnosis of several sleep disorders; however, the manual annotation of the EEG signal is a costly and time-consuming process. Automatic sleep staging algorithms offer a practical and cost-effective alternative to manual sleep staging. However, due to the limited availability of EEG sleep datasets, the reliability of existing sleep staging algorithms is questionable. Furthermore, most reported experimental results have been obtained using adult EEG signals; the effectiveness of these algorithms using pediatric EEGs is unknown. In this paper, we conduct an intensive study of two state-of-the-art single-channel EEG-based sleep staging algorithms, namely DeepSleepNet and AttnSleep, using a recently released large-scale sleep dataset collected from 3984 patients, most of whom are children. The paper studies how the performance of these sleep staging algorithms varies when applied on different EEG channels and across different age groups. Furthermore, all results were analyzed within individual sleep stages to understand how each stage is affected by the choice of EEG channel and the participants' age. The study concluded that the selection of the channel is crucial for the accuracy of the single-channel EEG-based automatic sleep staging methods. For instance, channels O1-M2 and O2-M1 performed consistently worse than other channels for both algorithms and through all age groups. The study also revealed the challenges in the automatic sleep staging of newborns and infants (1-52 weeks).

Aligning Small Datasets Using Domain Adversarial Learning: Applications in Automated in Vivo Oral Cancer Diagnosis.

Deep learning approaches for medical image analysis are limited by small data set size due to factors such as patient privacy and difficulties in obtaining expert labelling for each image. In medical imaging system development pipelines, phases for system development and classification algorithms often overlap with data collection, creating small disjoint data sets collected at numerous locations with differing protocols. In this setting, merging data from different data collection centers increases the amount of training data. However, a direct combination of datasets will likely fail due to domain shifts between imaging centers. In contrast to previous approaches that focus on a single data set, we add a domain adaptation module to a neural network and train using multiple data sets. Our approach encourages domain invariance between two multispectral autofluorescence imaging (maFLIM) data sets of in vivo oral lesions collected with an imaging system currently in development. The two data sets have differences in the sub-populations imaged and in the calibration procedures used during data collection. We mitigate these differences using a gradient reversal layer and domain classifier. Our final model trained with two data sets substantially increases performance, including a significant increase in specificity. We also achieve a significant increase in average performance over the best baseline model train with two domains (p = 0.0341). Our approach lays the foundation for faster development of computer-aided diagnostic systems and presents a feasible approach for creating a robust classifier that aligns images from multiple data centers in the presence of domain shifts.

Clinical label-free endoscopic imaging of biochemical and metabolic autofluorescence biomarkers of benign, precancerous, and cancerous oral lesions.

Early detection is critical for improving the survival rate and quality of life of oral cancer patients; unfortunately, dysplastic and early-stage cancerous oral lesions are often difficult to distinguish from oral benign lesions during standard clinical oral examination. Therefore, there is a critical need for novel clinical technologies that would enable reliable oral cancer screening. The autofluorescence properties of the oral epithelial tissue provide quantitative information about morphological, biochemical, and metabolic tissue and cellular alterations accompanying carcinogenesis. This study aimed to identify novel biochemical and metabolic autofluorescence biomarkers of oral dysplasia and cancer that could be clinically imaged using novel multispectral autofluorescence lifetime imaging (maFLIM) endoscopy technologies. In vivo maFLIM clinical endoscopic images of benign, precancerous, and cancerous lesions from 67 patients were acquired using a novel maFLIM endoscope. Widefield maFLIM feature maps were generated, and statistical analyses were applied to identify maFLIM features providing contrast between dysplastic/cancerous vs. benign oral lesions. A total of 14 spectral and time-resolved maFLIM features were found to provide contrast between dysplastic/cancerous vs. benign oral lesions, representing novel biochemical and metabolic autofluorescence biomarkers of oral epithelial dysplasia and cancer. To the best of our knowledge, this is the first demonstration of clinical widefield maFLIM endoscopic imaging of novel biochemical and metabolic autofluorescence biomarkers of oral dysplasia and cancer, supporting the potential of maFLIM endoscopy for early detection of oral cancer.

End-to-End Neural Network for Feature Extraction and Cancer Diagnosis of In Vivo Fluorescence Lifetime Images of Oral Lesions.

In contrast to previous studies that focused on classical machine learning algorithms and hand-crafted features, we present an end-to-end neural network classification method able to accommodate lesion heterogeneity for improved oral cancer diagnosis using multispectral autofluorescence lifetime imaging (maFLIM) endoscopy. Our method uses an autoencoder framework jointly trained with a classifier designed to handle overfitting problems with reduced databases, which is often the case in healthcare applications. The autoencoder guides the feature extraction process through the reconstruction loss and enables the potential use of unsupervised data for domain adaptation and improved generalization. The classifier ensures the features extracted are task-specific, providing discriminative information for the classification task. The data-driven feature extraction method automatically generates task-specific features directly from fluorescence decays, eliminating the need for iterative signal reconstruction. We validate our proposed neural network method against support vector machine (SVM) baselines, with our method showing a 6.5%-8.3% increase in sensitivity. Our results show that neural networks that implement data-driven feature extraction provide superior results and enable the capacity needed to target specific issues, such as inter-patient variability and the heterogeneity of oral lesions.Clinical relevance- We improve standard classification algorithms for in vivo diagnosis of oral cancer lesions from maFLIm for clinical use in cancer screening, reducing unnecessary biopsies and facilitating early detection of oral cancer.

An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech.

Childhood apraxia of speech (CAS) commonly affects the production of lexical stress contrast in polysyllabic words. Automated classification tools have the potential to increase reliability and efficiency in measuring lexical stress. Here, factors affecting the accuracy of a custom-built deep neural network (DNN)-based classification tool are evaluated. Sixteen children with typical development (TD) and 26 with CAS produced 50 polysyllabic words. Words with strong-weak (SW, e.g., dinosaur) or WS (e.g., banana) stress were fed to the classification tool, and the accuracy measured (a) against expert judgment, (b) for speaker group, and (c) with/without prior knowledge of phonemic errors in the sample. The influence of segmental features and participant factors on tool accuracy was analysed. Linear mixed modelling showed significant interaction between group and stress type, surviving adjustment for age and CAS severity. For TD, agreement for SW and WS words was >80%, but CAS speech was higher for SW (>80%) than WS (~60%). Prior knowledge of segmental errors conferred no clear advantage. Automatic lexical stress classification shows promise for identifying errors in children's speech at diagnosis or with treatment-related change, but accuracy for WS words in apraxic speech needs improvement. Further training of algorithms using larger sets of labelled data containing impaired speech and WS words may increase accuracy.

Machine-Learning Assisted Discrimination of Precancerous and Cancerous from Healthy Oral Tissue Based on Multispectral Autofluorescence Lifetime Imaging Endoscopy.

Multispectral autofluorescence lifetime imaging (maFLIM) can be used to clinically image a plurality of metabolic and biochemical autofluorescence biomarkers of oral epithelial dysplasia and cancer. This study tested the hypothesis that maFLIM-derived autofluorescence biomarkers can be used in machine-learning (ML) models to discriminate dysplastic and cancerous from healthy oral tissue. Clinical widefield maFLIM endoscopy imaging of cancerous and dysplastic oral lesions was performed at two clinical centers. Endoscopic maFLIM images from 34 patients acquired at one of the clinical centers were used to optimize ML models for automated discrimination of dysplastic and cancerous from healthy oral tissue. A computer-aided detection system was developed and applied to a set of endoscopic maFLIM images from 23 patients acquired at the other clinical center, and its performance was quantified in terms of the area under the receiver operating characteristic curve (ROC-AUC). Discrimination of dysplastic and cancerous from healthy oral tissue was achieved with an ROC-AUC of 0.81. This study demonstrates the capabilities of widefield maFLIM endoscopy to clinically image autofluorescence biomarkers that can be used in ML models to discriminate dysplastic and cancerous from healthy oral tissue. Widefield maFLIM endoscopy thus holds potential for automated in situ detection of oral dysplasia and cancer.

Adversarial Multi-Task Learning for Robust End-to-End ECG-based Heartbeat Classification.

In clinical practice, heart arrhythmias are manually diagnosed by a doctor, which is a time-consuming process. Furthermore, this process is error-prone due to noise from the recording equipment and biological non-idealities of patients. Thus, an automated arrhythmia classifier would be time and cost-effective as well as offer better generalization across patients. In this paper, we propose an adversarial multitask learning method to improve the generalization of heartbeat arrythmia classification. We built an end-to-end deep neural network (DNN) system consisting of three sub-networks; a generator, a heartbeat-type discriminator, and a subject (or patient) discriminator. Each of these two discriminators had its own loss function to control its impact. The generator was "friendly" to the heartbeat-type discrimination task by minimizing its loss function and "hostile" to the subject discrimination task by maximizing its loss function. The network was trained using raw ECG signals to discriminate between five types of heartbeats - normal heartbeats, right bundle branch blocks (RBBB), premature ventricular contractions (PVC), paced beats (PB) and fusion of ventricular and normal beats (FVN). The method was tested with the MIT-BIH arrhythmia dataset and achieved a 17% reduction in classification error compared to a baseline using a fully-connected DNN classifier.Clinical Relevance-This work validates that it is possible to develop a subject-independent automated heart arrhythmia detection system to assist clinicians in the diagnosis process.

Subject Independent Dementia Risk Prediction Models Using Paralinguistic and Memory Test Features with Feature Warping.

Clinical and biological changes during the prodromal stages of dementia are both complicated and expensive. A biomarker for cognitive reserve exposure would be highly useful as a dementia risk predictor, but has eluded researchers. Speech, which exhibits disfluencies due to dementia, is a good candidate as it is easy to collect and non-invasive. However, previous studies have only looked at the impact of dementia on speech after diagnosis. Here we extend our previous work that showed paralinguistic features extracted from audio recordings of older participants completing the LOGOS episodic memory test can be used to discriminate between high vs low cognitive reserve, hence low vs high risk of dementia. Specifically, we use the clinically validated Lifetime of Experiences Questionnaire (LEQ) to refine our ground truth estimate of cognitive reserve instead of an abridged version. Also, we improve the generalizability of our system by using feature warping to normalize across speakers. Our k-nearest neighbours (KNN) based classifier achieved an accuracy of 84% when trained with paralinguistic features alone and 91% with paralinguistic and episodic memory features.Clinical Relevance- This establishes efficacy of using speech from older participants completing the LOGOS episodic memory test to estimate risk of dementia.

The influence of type of feedback during tablet-based delivery of intensive treatment for childhood apraxia of speech.

PURPOSE: One of the key principles of motor learning supports using knowledge of results feedback (KR, i.e., whether a response was correct / incorrect only) during high intensity motor practice, rather than knowledge of performance (KP, i.e., whether and how a response was correct/incorrect). In the future, mobile technology equipped with automatic speech recognition (ASR) could provide KR feedback, enabling this practice to move outside the clinic, supplementing speech pathology sessions and reducing burden on already stretched speech-language pathology resources. Here, we employ a randomized controlled trial design to test the impact of KR vs KP feedback on children's response to the Nuffield Dyspraxia Programme 3, delivered through an android tablet. At the time of testing, ASR was not feasible and so correctness of responses was decided by the treating clinician. METHOD: Fourteen children with CAS, aged 4-10 years, participated in a parallel group design, matched for age and severity of CAS. Both groups attended a university clinic for 1-hr therapy sessions 4 days a week for 3 weeks. One group received high frequency feedback comprised of both KR and KP, in the style of traditional, face-to-face intensive intervention on all days. The other group received high frequency KR + KP feedback on 1 day per week and high frequency KR feedback on the other 3 days per week, simulating the service delivery model of one clinic session per week supported by tablet-based home practice. RESULTS: Both groups had significantly improved speech outcomes at 4-months post-treatment. Post-hoc comparisons suggested that only the KP group showed a significant change from pre- to immediately post-treatment but the group difference had dissipated by 1-month post-treatment. Heterogeneity in response to intervention within the groups suggests that other factors, not measured here, may be having a substantive influence on response to intervention and feedback type. CONCLUSION: Mobile technology has the potential to increase motivation and engagement with therapy and to mitigate barriers associated with distance and access to speech pathology services. Further research is needed to explore the influence of type and frequency of feedback on motor learning, optimal timing for transitioning from KP to KR feedback, and how these parameters interact with task, child and context-related factors.

Clinical label-free biochemical and metabolic fluorescence lifetime endoscopic imaging of precancerous and cancerous oral lesions.

INTRODUCTION: Incomplete head and neck cancer resection occurs in up to 85% of cases, leading to increased odds of local recurrence and regional metastases; thus, image-guided surgical tools for accurate, in situ and fast detection of positive margins during head and neck cancer resection surgery are urgently needed. Oral epithelial dysplasia and cancer development is accompanied by morphological, biochemical, and metabolic tissue and cellular alterations that can modulate the autofluorescence properties of the oral epithelial tissue. OBJECTIVE: This study aimed to test the hypothesis that autofluorescence biomarkers of oral precancer and cancer can be clinically imaged and quantified by means of multispectral fluorescence lifetime imaging (FLIM) endoscopy. METHODS: Multispectral autofluorescence lifetime images of precancerous and cancerous lesions from 39 patients were imaged in vivo using a novel multispectral FLIM endoscope and processed to generate widefield maps of biochemical and metabolic autofluorescence biomarkers of oral precancer and cancer. RESULTS: Statistical analyses applied to the quantified multispectral FLIM endoscopy based autofluorescence biomarkers indicated their potential to provide contrast between precancerous/cancerous vs. healthy oral epithelial tissue. CONCLUSION: To the best of our knowledge, this study represents the first demonstration of label-free biochemical and metabolic clinical imaging of precancerous and cancerous oral lesions by means of widefield multispectral autofluorescence lifetime endoscopy. Future studies will focus on demonstrating the capabilities of endogenous multispectral FLIM endoscopy as an image-guided surgical tool for positive margin detection during head and neck cancer resection surgery.

A Two Stage Approach for the Automatic Detection of Insomnia.

Chronic insomnia can significantly impair an individual's quality of life leading to a high societal cost. Unfortunately, limited automated tools exist that can assist clinicians in the timely detection of insomnia. In this paper, we propose a two stage approach to automatically detect insomnia from an overnight EEG recording. In the first stage we trained a sleep stage scoring model and an epoch-level insomnia detection model. Both models are deep neural network (DNN)- based which are fed by a set of temporal and spectral features derived from 2 EEG channels. In the second stage we computed two subject-level feature sets. One is computed using the output of the sleep stage scoring model and consists of the sleep stage ratios, the stage pair ratios and the stage transition ratios. The second feature set is derived from the output of the epoch-level insomnia detection model and represents the ratio of detected insomniac epochs in each stage and their average posterior probability. These features are then used to train a final binary classifier to classify each subject as control, i.e., with no sleep complaints, or insomniac. We compared 5 different binary classifiers, namely the linear discriminant analysis (LDA), the classification and regression trees (CART) and the support vector machine (SVM) with linear, Gaussian and sigmoid kernels. The system was evaluated against data collected from 115 participants, 61 control and 54 with insomnia, and achieved $F1$ score, sensitivity and specificity of 0.88, 84% and 91% respectively.

Endogenous Fluorescence Lifetime Imaging (FLIM) Endoscopy For Early Detection Of Oral Cancer And Dysplasia.

We have performed a pilot clinical study, in which multispectral endogenous fluorescence (or autofluorescence) lifetime imaging (FLIM) was performed on clinically suspicious oral lesions of 73 patients undergoing tissue biopsy for oral dysplasia and cancer diagnosis. The results from this pilot study indicated that mild-dysplasia and early stage oral cancer could be detected from benign lesions using a computed aided diagnosis system developed based on biochemical and metabolic biomarkers derived from the endogenous FLIM images. The diagnostic performance of this novel FLIM clinical tool was estimated using a leave-onepatient-out cross-validation approach, which reported levels of sensitivity >90%, specificity >85%, and Area Under the Receiving Operating Curve (ROC-AUC) >0.9.

Speech-driven mobile games for speech therapy: User experiences and feasibility.

Purpose: To assist in remote treatment, speech-language pathologists (SLPs) rely on mobile games, which though entertaining, lack feedback mechanisms. Games integrated with automatic speech recognition (ASR) offer a solution where speech productions control gameplay. We therefore performed a feasibility study to assess children's and SLPs' experiences towards speech-controlled games, game feature preferences and ASR accuracy. Method: Ten children with childhood apraxia of speech (CAS), six typically developing (TD) children and seven SLPs trialled five games and answered questionnaires. Researchers also compared the results of ASR to perceptual judgment. Result: Children and SLPs found speech-controlled games interesting and fun, despite ASR-human disagreements. They preferred games with rewards, challenge and multiple difficulty levels. Automatic speech recognition-human agreement was higher for SLPs than children, similar between TD and CAS and unaffected by CAS severity (77% TD, 75% CAS - incorrect; 51% TD, 47% CAS, 71% SLP - correct). Manual stop recording yielded higher agreement than automatic. Word length did not influence agreement. Conclusion: Children's and SLPs' positive responses towards speech-controlled games suggest that they can engage children in higher intensity practice. Our findings can guide future improvements to the ASR, recording methods and game features to improve the user experience and therapy adherence.

Blood pressure and cardiovascular parameters during sleep arousals.

Sleep arousal is generally known as a transient episode of wakefulness into the sleepiness. Sleep arousals can be classified based on their association and accompany with pathological episodes. In this paper, our objective was to find out whether various types of sleep arousals influence on blood pressure and Heart Rate Variability (HRV). We analysed continuous Diastolic and Systolic Blood Pressures (DBP and SBP), Pulse Transit Time (PTT) as well as High and Low Frequency components (HF and LF) of HRV in different types of arousals. We developed Slope Index (SI) to determine whether a feature was ascending or descending before, during and after the occurrence of a sleep arousal. Slope Index Positive Percentage (SIPP) was created and computed for all features to find out the percentage of arousals with an ascending trend of a cardiovascular feature. In pre-arousal epochs, we obtained SIPPDBP= 48.9%, SIPPSBP = 48.2% and SIPPLF = 41%. Whilst during the arousal episodes, the SIPPDBP, SIPPSBP and SIPPLF increased to 57.2%, 57.4% and 78.9%, correspondingly. This means that during arousal occurrence these parameters were likelier to rise. Whereas SIPP of PTT and HF component of HRV during arousals were less than prearousal. This indicated PTT and HF were highly probable to drop during the arousal than to rise. The high SIPPDBP and SIPPSBP parameters, approximately 76%, during the arousals indicates that sleep arousals may cause a sudden increase in blood pressure.

Comparing two insomnia detection models of clinical diagnosis techniques.

Sleep disorders are becoming increasingly prevalent in society. However most of the burgeoning research on automated sleep analysis has been in the realm of sleep stage classification with limited focus on accurately diagnosing these disorders. In this paper, we explore two different models to discriminate between control and insomnia patients using support vector machine (SVM) classifiers. We validated the models using data collected from 124 participants, 70 control and 54 with insomnia. The first model uses 57 features derived from two channels of EEG data and achieved an accuracy of 81%. The second model uses 15 features from each participant's hypnogram and achieved an accuracy of 74%. The impetus behind using these two models is to follow the clinician's diagnostic decision-making process where both the EEG signals and the hypnograms are used. These results demonstrate that there is potential for further experimentation and improvement of the predictive capability of the models to help in diagnosing sleep disorders like insomnia.

Deep Learning and Insomnia: Assisting Clinicians With Their Diagnosis.

Effective sleep analysis is hampered by the lack of automated tools catering to disordered sleep patterns and cumbersome monitoring hardware. In this paper, we apply deep learning on a set of 57 EEG features extracted from a maximum of two EEG channels to accurately differentiate between patients with insomnia or controls with no sleep complaints. We investigated two different approaches to achieve this. The first approach used EEG data from the whole sleep recording irrespective of the sleep stage (stage-independent classification), while the second used only EEG data from insomnia-impacted specific sleep stages (stage-dependent classification). We trained and tested our system using both healthy and disordered sleep collected from 41 controls and 42 primary insomnia patients. When compared with manual assessments, an NREM + REM based classifier had an overall discrimination accuracy of 92% and 86% between two groups using both two and one EEG channels, respectively. These results demonstrate that deep learning can be used to assist in the diagnosis of sleep disorders such as insomnia.

Objective Detection of Oral Carcinoma with Multispectral Fluorescence Lifetime Imaging In Vivo.

Successful early detection and demarcation of oral carcinoma can greatly impact the associated morbidity and mortality rates. Current methods for detection of oral cancer include comprehensive visual examination of the oral cavity, typically followed by tissue biopsy. A noninvasive means to guide the clinician in making a more objective and informed decision toward tissue biopsy can potentially improve the diagnostic yield of this process. To this end, we investigate the potential of fluorescence lifetime imaging (FLIM) for objective detection of oral carcinoma in the hamster cheek pouch model of oral carcinogenesis in vivo. We report that systematically selected FLIM features can differentiate between low-risk (normal, benign and low-grade dysplasia) and high-risk (high-grade dysplasia and cancer) oral lesions with sensitivity and specificity of 87.26% and 93.96%, respectively. We also show the ability of FLIM to generate "disease" maps of the tissue which can be used to evaluate relative risk of neoplasia. The results demonstrate the potential of multispectral FLIM with objective image analysis as a noninvasive tool to guide comprehensive oral examination.