Oral squamous cell carcinoma (OSCC) accounts for >350, 000 new cases and >177, 000 deaths worldwide. Oral potentially malignant disorders (OPMD), harboring oral epithelial dysplasia (OED) are 15 times more common than OSCC - a significant proportion transforming to malignancy. Furthermore, field cancerization may contribute to multifocal disease-risk comprising other oral sites. Oral epithelia lesions (OEL) are often challenging to diagnose for front-line clinicians and process of monitoring patients for new/evolving local disease lacks consensus - challenging opportunities for triage, surveillance and early detection. To address these gaps there is a compelling need to complete accurate surveillance testing of OEL for disease evolution in at-risk patients chairside/point-of-care, aiding clinical decision making. This study leverages an NIH-funded, multisite time-course investigation of patients with a history of OSCC or OED, utilizing AI-linked cytopathology capabilities derived from a microfluidics engine analyzed with a three-color fluorescence analyzer. Minimally invasive oral brush cytology specimens were collected from suspicious lesions and contralateral sites, with clinical and histopathological diagnoses blinded until patient exit. The study employs a chairside-ready, AI-assisted cytopathology-on-a-chip (COC) platform. This platform integrates microfluidics-facilitated immunoassays, cytomic measurements, automated analysis of cellular and molecular signatures, and result generation. The output includes a numerical index that profiles disease severity and progression, linked to the patient’s clinical and histopathological data, all delivered in approximately 40 minutes. Initial results from this study highlight the strong potential of a disease severity numerical index for assessing risk and distinguishing between oral cavity cancer patients who experience recurrence and those who do not. Similarly, OED patients at risk of malignant transformation exhibit higher numerical indexes. Moreover, contralateral lesions provide an advanced perspective on patients with a stronger predisposition to field cancerization. This noninvasive approach aligns well with findings obtained through more invasive biopsy and histological analysis. The AI-assisted chairside-ready COC minimally-invasive platform has strong potential to aid oral lesion characterization, for early-identification of disease risk and evolution. These capabilities include use of static numerical indexes and associated time-course changes. Measurement of contralateral-site specimen also offers new insights for referral or routine-surveillance, towards achieving better outcomes, disease prevention and management. Kritika Srinivasan Rajsri, Michael McRae, Nancy Ruel, A Ross Kerr, Nadarajah Vigneswaran, Adam Jacobson, Jonathan Shum, Rachelle Wolk, Nicolaos Christodoulides, John T. McDevitt. Characterization of oral carcinoma risk in patients with oral epithelial lesions employing an AI-assisted cytopathology-on-a-chip platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2105.

ObjectivesA small fraction of oral lichenoid conditions (OLC) have potential for malignant transformation (MT). Distinguishing OLCs from other oral potentially malignant disorders (OPMD) can help prevent unnecessary concern or testing, but accurate identification by non-expert clinicians is challenging due to overlapping clinical features. In this study, the authors developed a cytomics-on-a-chip tool and integrated predictive model for aiding the identification of OLCs.Study DesignAll study subjects underwent both scalpel biopsy for histopathology and brush cytology. A predictive model and OLC Index comprising clinical, demographic, and cytologic features was generated to discriminate between subjects with lichenoid (OLC+) (N=94) and non-lichenoid (OLC−) (N=237) histologic features in a population with OPMDs.ResultsThe OLC Index discriminated OLC+ and OLC− subjects with area under the curve (AUC) of 0.76. Diagnostic accuracy of the OLC Index was not significantly different from expert clinician impressions, with AUC of 0.81 (p=0.0704). Percent agreement was comparable across all raters, with 83.4% between expert clinicians and histopathology, 78.3% between OLC Index and expert clinician, and 77.3% between OLC Index and histopathology.ConclusionThe cytomics-on-a-chip tool and integrated diagnostic model have the potential to facilitate both the triage and diagnosis and risk stratification of patients presenting with OPMDs and OLCs.

Oral cytology is a non-invasive adjunctive diagnostic tool with a number of potential applications in the practice of dentistry. This brief review begins with a history of cytology in medicine and how cytology was initially applied in oral medicine. A description of the different technical aspects of oral cytology is provided, including the collection and processing of oral cytological samples, and the microscopic interpretation and reporting, along with their advantages and limitations. Applications for oral cytology are listed with a focus on the triage of patients presenting with oral potentially malignant disorders and oral mucosal infections. Furthermore, the utility of oral cytology roles across both expert (for example, secondary oral medicine or tertiary head and neck oncology services) and non-expert (for example, primary care general dental practice) clinical settings is explored. A detailed section covers the evidence-base for oral cytology as a diagnostic adjunctive technique in both the early detection and monitoring of patients with oral cancer and oral epithelial dysplasia. The review concludes with an exploration of future directions, including the integration of artificial intelligence for automated analysis and point of care ‘smart diagnostics', thereby offering some insight into future opportunities for a wider application of oral cytology in dentistry.

Abstract Cancer is the 2nd leading cause of death (over 605,000 people) in the US, at an expense of over $200B, with 1 in 3 people projected to have cancer during their lifetime per CDC. Despite the significant impact of early detection and screening on prognosis, only some cancers are diagnosed at an early stage. Carcinomas, comprising >80% of cancer incidence, allow ease in cytology sample access chairside, due to the lesions’ epithelial presentation. This presents a unique opportunity for early detection and screening in epithelial cancers. In low-resource healthcare settings, from clinical examination to the long, tedious and expensive diagnostic journey for cancers & pre-cancerous lesions, can lead to missed, delayed or over diagnosis scenarios. This affects treatment initiation and potentially outcome. To facilitate early intervention, there is compelling need to develop accurate and effective minimally invasive screening platforms. Recent advances in the -omics disciplines, microfluidics and AI tools are starting to reveal promising signatures of early disease detection, with potential to drastically improve screening and diagnostic systems. We are developing a novel application of the cytomics-on-chip platform, for enabling chairside, quantitative screening of suspicious epithelial lesions. The biosensor module involves 1. a single use, cytomics platform employing a cartridge with cellular array and high specificity biomarker reagents, that allows single cell molecular imaging to be completed in a portable analyzer. 2. a microfluidics module that allows cytomorphometric measurements to be completed. 3. The results generated are utilized to train machine learning algorithms to detect cyto-signatures and provide an intuitive result that may be utilized by health care practitioners in clinical-decision making. The first cell-based point-of-care oncology tool has recently been validated with high accuracy (99.3%), sensitivity and specificity, in a multi-site prospective clinical study. Here we demonstrate a pilot study towards development of a smart single cell cytomics-on-chip platform for prompt cytomorphometric and biomarker characterization towards diagnosis of urothelial, anal and cervical cancers, and dysplasia lesions, utilizing brush/pap and fresh urine samples. This has potential for continuous quantitative indexing, for disease categorization. As cancers become more pervasive, improved early detection/screening methods that are accurate, cost effective, easy to implement during routine clinical practice, and providing minimal discomfort to the patient, are urgently needed, improving confidence in clinicians’ decisions. Citation Format: Kritika Srinivasan Rajsri, Michael P. McRae, Nicolaos J. Christodoulides, Khaled Algashaamy, Monica T. Garcia-Buitrago, Fei Chen, Fang-Ming Deng, Jennifer S. Smith, John T. McDevitt. Cytomics-on-chip and AI-driven predictive analysis platform for early detection of epithelial cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6087.

Supplementary Figure 2. Lower range of values for the correlation of all four biomarkers on the p-BNC to Luminex

Supplementary Figure 3. Isolated longitudinal plasma data of case 2 (from Fig. 7)

As COVID-19 pandemic public health measures are easing globally, the emergence of new SARS-CoV-2 strains continue to present high risk for vulnerable populations. The antibody-mediated protection acquired from vaccination and/or infection is seen to wane over time and the immunocompromised populations can no longer expect benefit from monoclonal antibody prophylaxis. Hence, there is a need to monitor new variants and its effect on vaccine performance. In this context, surveillance of new SARS-CoV-2 infections and serology testing are gaining consensus for use as screening methods, especially for at-risk groups. Here, we described an improved COVID-19 screening strategy, comprising predictive algorithms and concurrent, rapid, accurate, and quantitative SARS-CoV-2 antigen and host antibody testing strategy, at point of care (POC). We conducted a retrospective analysis of 2553 pre- and asymptomatic patients who were tested for SARS-CoV-2 by RT-PCR. The pre-screening model had an AUC (CI) of 0.76 (0.73-0.78). Despite being the default method for screening, body temperature had lower AUC (0.52 [0.49-0.55]) compared to case incidence rate (0.65 [0.62-0.68]). POC assays for SARS-CoV-2 nucleocapsid protein (NP) and spike (S) receptor binding domain (RBD) IgG antibody showed promising preliminary results, demonstrating a convenient, rapid (<20 min), quantitative, and sensitive (ng/mL) antigen/antibody assay. This integrated pre-screening model and simultaneous antigen/antibody approach may significantly improve accuracy of COVID-19 infection and host immunity screening, helping address unmet needs for monitoring vaccine effectiveness and severe disease surveillance.

Supplementary Table 1. Dates of plasma collection for longitudinal clinical samples

Background & Objective: Oral squamous cell carcinoma (OSCC) affects over 400,000 individuals globally, every year. When diagnosed early, the 5-year survival rate for OSCC is 64%, but two-third of these lesions are diagnosed in later stages, leading to lower survival rates (<40%). Clinical diagnosis of these lesions is complicated by benign and Oral-Potentially-Malignant-Disorders (OPMD) like oral lichenoid conditions (OLC), mimicking OSCC in clinical presentation. Studies in literature demonstrate high rates of incorrect chair-side diagnosis for OSCC and OPMD, making their early and accurate detection challenging. This clinical gap prompts a strong need for clinical chair-side point-of-care (POC) platforms, to aid accurate screening of these lesions and prevent diagnostic delays. Methods: In this work we present a ‘smart diagnostic approach’ that combines three key capabilities into an integrated sensing modality as follows: i) powerful microfluidic engine that allows for cytology measurements to be completed outside of a sophisticated lab infrastructure, ii) cytomics platform that allows for single cell molecular imaging to be completed in a portable analyzer, iii) AI-linked diagnostic models for early disease detection using cyto-signatures. This platform has been clinically validated across a multi-site prospective clinical study. Results: Multiple parameters including cellular phenotypes, nuclear parameters, biomarker expressions were indexed. Further, combining these features allowed discrimination and stratification of these lesions with high accuracy (99.3%) and significance. Further examination using logistic regression and receiver operating characteristic curve analyses yielded significant lesion identifiers and AUC values towards positive discrimination of OLC and OSCC (0.824 and 0.95, respectively vs benign lesions), with high sensitivity and specificity. Conclusion: This rapid (<30 minutes) cytopathology POC solution has the potential to impact OSCC and OPMD screening accurately, characterizing subtle cellular changes to aid long-term monitoring of these lesions. Additionally, this platform has the capability to uncover new parameters that can further aid these assessments and improve confidence in clinicians’ decisions. Citation Format: Kritika Srinivasan Rajsri, Michael P. McRae, Glennon W. Simmons, Alexander Ross Kerr, Nadarajah Vigneswaran, Spencer W. Redding, Malvin Janal, Stella Kang, Leena Paloma, Nicolaos J. Christodoulides, John T. McDevitt. A smart cytopathology risk-assessment platform for oral potentially malignant disorders and oral squamous cell carcinoma at the point-of-care [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 785.

Supplementary Figure 1. Lower region of dose response curve to demonstrate limit of detection

PDF file - 19K, Representative linear calibration curve (0-50 U/mL range highlighted for clarity

PDF file - 29K, Matched Pair Optimization Summary

Supplementary Table 2. Concentrations of biomarkers used in precision study

PDF file - 29K, p-BNC construction, sample collection, Image analysis

As of 8 August 2022, SARS-CoV-2, the causative agent of COVID-19, has infected over 585 million people and resulted in more than 6.42 million deaths worldwide. While approved SARS-CoV-2 spike (S) protein-based vaccines induce robust seroconversion in most individuals, dramatically reducing disease severity and the risk of hospitalization, poorer responses are observed in aged, immunocompromised individuals and patients with certain pre-existing health conditions. Further, it is difficult to predict the protection conferred through vaccination or previous infection against new viral variants of concern (VoC) as they emerge. In this context, a rapid quantitative point-of-care (POC) serological assay able to quantify circulating anti-SARS-CoV-2 antibodies would allow clinicians to make informed decisions on the timing of booster shots, permit researchers to measure the level of cross-reactive antibody against new VoC in a previously immunized and/or infected individual, and help assess appropriate convalescent plasma donors, among other applications. Utilizing a lab-on-a-chip ecosystem, we present proof of concept, optimization, and validation of a POC strategy to quantitate COVID-19 humoral protection. This platform covers the entire diagnostic timeline of the disease, seroconversion, and vaccination response spanning multiple doses of immunization in a single POC test. Our results demonstrate that this platform is rapid (~15 min) and quantitative for SARS-CoV-2-specific IgG detection.

We are beginning a new era of Smart Diagnostics—integrated biosensors powered by recent innovations in embedded electronics, cloud computing, and artificial intelligence (AI). Universal and AI-based in vitro diagnostics (IVDs) have the potential to exponentially improve healthcare decision making in the coming years. This perspective covers current trends and challenges in translating Smart Diagnostics. We identify essential elements of Smart Diagnostics platforms through the lens of a clinically validated platform for digitizing biology and its ability to learn disease signatures. This platform for biochemical analyses uses a compact instrument to perform multiclass and multiplex measurements using fully integrated microfluidic cartridges compatible with the point of care. Image analysis digitizes biology by transforming fluorescence signals into inputs for learning disease/health signatures. The result is an intuitive Score reported to the patients and/or providers. This AI-linked universal diagnostic system has been validated through a series of large clinical studies and used to identify signatures for early disease detection and disease severity in several applications, including cardiovascular diseases, COVID-19, and oral cancer. The utility of this Smart Diagnostics platform may extend to multiple cell-based oncology tests via cross-reactive biomarkers spanning oral, colorectal, lung, bladder, esophageal, and cervical cancers, and is well-positioned to improve patient care, management, and outcomes through deployment of this resilient and scalable technology. Lastly, we provide a future perspective on the direction and trajectory of Smart Diagnostics and the transformative effects they will have on health care.

While COVID-19 has yielded devastating consequences over the past few years, the global pandemic also has opened the door for acceleration of development of core diagnostic capabilities that have the potential to lead to lasting impact for our society.With this vantage point in mind, in the recent past the McDevitt laboratory has launched a series of efforts that target the development and deployment of 'smart diagnostics' that serve as distributed point of care sensor nodes with capacity to learn.These mini-sensor ensembles with embedded artificial intelligence integrate programmable chip-based diagnostic systems capable of multiplexed measurements alongside clinical decision support tools that utilize strategically chosen nonclinical data elements that elicit signatures that can be used to capture diseases before they spiral out of control.As such, these efforts link for the first-time the following five key disciplines: i) lab-on-a-chip technologies, ii) in vitro diagnostics, iii) -omics research, iv) artificial intelligence, and v) digital healthcare delivery systems.Importantly, the combination of point-of-care medical microdevices and machine learning has the potential transform the practice of medicine.In this area, scalable lab-on-a-chip devices have many advantages over standard laboratory methods including portability, faster analysis, reduced cost, lower power consumption, and higher levels of integration and automation.Despite significant advances in medical microdevice technologies over the years, several remaining obstacles are preventing clinical implementation and market penetration of these novel medical microdevices.Similarly, while machine learning has seen explosive growth in recent years and promises to shift the practice of medicine toward dataintensive and evidence-based decision making, its uptake has been hindered due to the lack of integration between clinical measurements and disease determinations.In this talk, our recent advances in 'smart diagnostics' will be highlighted.These smart diagnostics include single-use microfluidic cartridges that serve as fully integrated, self-contained devices that contain aqueous buffers suitable for automated completion of all assay steps within nontraditional healthcare settings.Further, a portable analyzer instrument is fashioned to integrate fluid delivery, optical detection, image analysis, and user interface, representing a universal system for acquiring, processing, and managing clinical data while overcoming many of the challenges facing the widespread clinical adoption of lab-on-a-chip technologies.Intimate linkages between these medical microdevices and cloud connected databases allows for early disease detection algorithms to be used to impact clinical.This talk will summarize the development and deployment for 'smart diagnostics' for the areas for the following areas:1) Oral cancer lesion adjunct testing: multiparameter single cell lesion diagnostics for precision medicine.2) Cardiac scorecard: a clinical decision support system for a spectrum of cardiovascular diseases.3) COVID-19 severity and immunity diagnostics: point of care tools to help better manage the corona virus.

Introduction: Tracheostomy in patients with COVID-19 is a controversial and difficult clinical decision. We hypothesized that a recently validated COVID-19 Severity Score (CSS) would be associated with survival in patients considered for tracheostomy.Methods: We reviewed 77 mechanically ventilated COVID-19 patients evaluated for decision for percutaneous dilational tracheostomy (PDT) from March to June 2020 at a public tertiary care center. Decision for PDT was based on clinical judgment of the screening surgeons. The CSS was retrospectively calculated using mean biomarker values from admission to time of PDT consult. Our primary outcome was survival to discharge, and all patient charts were reviewed through August 31, 2021. ROC curve and Youden index were used to esti-mate an optimal cut-point for survival.Results: The mean CSS for 42 survivors significantly differed from that of 35 nonsurvivors (CSS 52 versus 66, P = 0.003). The Youden index returned an optimal CSS of 55 (95% con-fidence interval 43-72), which was associated with a sensitivity of 0.8 and a specificity of 0.6. The median CSS was 40 (interquartile range 27, 49) in the lower CSS (<55) group and 72 (interquartile range 66, 93) in the high CSS (>= 55 group). Eighty-seven percent of lower CSS patients underwent PDT, with 74% survival, whereas 61% of high CSS patients underwent PDT, with only 41% surviving. Patients with high CSS had 77% lower odds of survival (odds ratio = 0.2, 95% confidence interval 0.1-0.7).Conclusions: Higher CSS was associated with decreased survival in patients evaluated for PDT, with a score >= 55 predictive of mortality. The novel CSS may be a useful adjunct in determining which COVID-19 patients will benefit from tracheostomy. Further prospective validation of this tool is warranted. (c) 2022 Elsevier Inc. All rights reserved.

Oral cavity cancer has a low 5-y survival rate, but outcomes improve when the disease is detected early. Cytology is a less invasive method to assess oral potentially malignant disorders relative to the gold-standard scalpel biopsy and histopathology. In this report, we aimed to determine the utility of cytological signatures, including nuclear F-actin cell phenotypes, for classifying the entire spectrum of oral epithelial dysplasia and oral squamous cell carcinoma. We enrolled subjects with oral potentially malignant disorders, subjects with previously diagnosed malignant lesions, and healthy volunteers without lesions and obtained brush cytology specimens and matched scalpel biopsies from 486 subjects. Histopathological assessment of the scalpel biopsy specimens classified lesions into 6 categories. Brush cytology specimens were analyzed by machine learning classifiers trained to identify relevant cytological features. Multimodal diagnostic models were developed using cytology results, lesion characteristics, and risk factors. Squamous cells with nuclear F-actin staining were associated with early disease (i.e., lower proportions in benign lesions than in more severe lesions), whereas small round parabasal-like cells and leukocytes were associated with late disease (i.e., higher proportions in severe dysplasia and carcinoma than in less severe lesions). Lesions with the impression of oral lichen planus were unlikely to be either dysplastic or malignant. Cytological features substantially improved upon lesion appearance and risk factors in predicting squamous cell carcinoma. Diagnostic models accurately discriminated early and late disease with AUCs (95% CI) of 0.82 (0.77 to 0.87) and 0.93 (0.88 to 0.97), respectively. The cytological features identified here have the potential to improve screening and surveillance of the entire spectrum of oral potentially malignant disorders in multiple care settings.