Complete ablation of liver tumors is vital for minimizing the risk of local tumor recurrence. Accurately identifying the hallmarks of tissue necrosis during thermal ablative therapies may significantly increase the efficacy of ablation, while minimizing unnecessary damage to the surrounding normal tissues or critical structures. Light propagation in biological tissues is sensitive to the tissue microstructure and chromophore concentrations. In our previous studies, we found that the wavelength (λ) averaged liver tissue absorption coefficient (µ_a) and reduced scattering coefficient (µ_s′) change significantly upon heating which may be used for assessment of tissue damage during thermal ablation of solid tumors. Here, we seek to demonstrate the use of an integrated fiber-optic probe for continuous monitoring of the local tissue temperature (T), µ_a(λ) and µ_s′(λ) during thermal ablation of ex vivo porcine livers. The wavelength-averaged (435–630 nm) tissue absorption and scattering (µ_a and µ_s′ ) increased rapidly at 45 °C and plateaued at 67 °C. The mean µ_a and µ_s′ for liver tissue at 37 °C (n = 10) were 8.5 ± 3.7 and 2.8 ± 1.1 cm⁻¹, respectively. The relative changes in µ_a and µ_s′ at 37, 55, and 65 °C were significantly different (p < .02) from each other. A relationship between the relative changes in µ_a and µ_s′ and the degree of tissue damage estimated using the temperature-based Arrhenius model for porcine liver tissues was established and studied.

Microscopy with ultraviolet surface excitation (MUSE) is increasingly studied for intraoperative assessment of tumor margins during breast-conserving surgery to reduce the re-excision rate. Here we report a two-step classification approach using texture analysis of MUSE images to automate the margin detection. A study dataset consisting of MUSE images from 66 human breast tissues was constructed for model training and validation. Features extracted using six texture analysis methods were investigated for tissue characterization, and a support vector machine was trained for binary classification of image patches within a full image based on selected feature subsets. A weighted majority voting strategy classified a sample as tumor or normal. Using the eight most predictive features ranked by the maximum relevance minimum redundancy and Laplacian scores methods has achieved a sample classification accuracy of 92.4% and 93.0%, respectively. Local binary pattern alone has achieved an accuracy of 90.3%.

We report a nonconcurrent dual-modality fiber-optic microendoscope (named SmartME) that integrates quantitative diffuse reflectance spectroscopy (DRS) and high-resolution fluorescence imaging (FLI) into a smartphone platform. The FLI module has a spatial resolution of ~3.5 µm, which allows the determination of the nuclear-cytoplasmic ratio (N/C) of epithelial tissues. The DRS has a spectral resolution of ~2 nm and can measure the total hemoglobin concentration (THC) and scattering properties of epithelial tissues with mean errors of 4.7% and 6.9%, respectively, which are comparable to the errors achieved with a benchtop spectrometer. Our preliminary in vivo studies from a single healthy human subject demonstrate that the SmartME can noninvasively quantify the tissue parameters of normal human oral mucosa tissues, including labial mucosa tissue, gingival tissue, and tongue dorsum tissue. The THCs of the three oral mucosa tissues are significantly different from each other (p ≤ 0.003). The reduced scattering coefficients of the gingival and labial tissues are significantly different from those of the tongue dorsum tissue (p < 0.001) but are not significantly different from each other. The N/Cs for all three tissue types are similar. The SmartME has great potential to be used as a portable, cost-effective, and globally connected tool to quantify the THC and scattering properties of tissues in vivo.

There is an unmitigated disparity in timely detection of cancer in high-resource settings versus low-resource settings. Patients that live in low-resource settings, such as inner cities, rural areas, or lower-to-middle-income countries, are at a higher risk of facing late-stage cancer diagnoses and higher mortality rates from this family of diseases.^{1 – 3} Cervical cancer especially highlights this disparity, as $\sim 90\%$ of cases occur in low-resource settings.⁴ This is, in part, caused by the fact that significant patient-to-clinician follow-up is required to obtain a diagnosis. Further, standard procedures for diagnosing cancer require rigorous sufficient healthcare resources and infrastructure. Timely access to clinicians and laboratory resources are often not feasible for those living at or below the poverty line.^1,2 Thus, there is a need to develop a means of cost-efficiently detecting cancer at the point-of-care, giving special consideration to the logistical challenges in low-resource clinical settings. To accomplish this, many groups have looked to visible diffuse reflectance spectroscopy (DRS) as a means of capturing “optical biopsies” of suspicious lesions. There are many advantages to this “optical biopsy” approach. For example, this imaging can be done in an outpatient point-of-care setting, and equipment to obtain these biopsies, such as a visible optical spectrometer, is relatively inexpensive. For instance, the DRS system used to collect the data presented here costs <$2500 USD⁵ and is sensitive to many changes in the tissue microenvironment that occur during malignancy. One example of these changes includes increased angiogenesis, which manifests as abnormally high absorption coefficient, ${\mu }_a$. Another example is breakdown of the extracellular matrix within the tumor microenvironment, which causes an abnormally low reduced extinction coefficient, ${\mu }_s^{\prime }$.⁶

Proper ventilation of a patient with an endotracheal tube (ETT) requires proper placement of the ETT. We present a sensitive, noninvasive, operator-free, and cost-effective optical sensor, called Opt-ETT, for the real-time assessment of ETT placement and alerting of the clinical care team should the ETT become displaced. The Opt-ETT uses a side-firing optical fiber, a near-infrared light-emitting diode, two photodetectors with an integrated amplifier, an Arduino board, and a computer loaded with a custom LabVIEW program to monitor the position of the endotracheal tube inside the windpipe. The Opt-ETT generates a visual and audible warning if the tube moves over a distance set by the operator. Displacement prediction is made using a second-order polynomial fit to the voltages measured from each detector. The system is tested on ex vivo porcine tissues, and the accuracy is determined to be better than 1.0 mm. In vivo experiments with a pig are conducted to test the performance and usability of the system.