Near infrared imaging system for preventing blood vision obstruction in endoscopy.

Spinal endoscopy procedure is commonly used in the diagnosis and treatment of various health problems and is effective. Bleeding is one of the most common complications of spinal endoscopy procedures. Blood vision obstruction (BVO), that is, obstruction of the endoscopic camera lens caused by the accumulation of blood in the surgical field, is a serious problem in endoscopic procedures. This study presents what we believe to be a new approach to addressing BVO with external multispectral imaging. The study was completed using a BVO simulation model, and the results reveal that this technology can be used to effectively overcome BVO and provide clear images of the anatomy, enabling more effective diagnosis and treatment. This technique may enable improvement of the outcomes of endoscopic procedures and could have far-reaching implications in the field of endoscopy.

Fully automated film mounting in dental radiography: a deep learning model.

BACKGROUND: Dental film mounting is an essential but time-consuming task in dental radiography, with manual methods often prone to errors. This study aims to develop a deep learning (DL) model for accurate automated classification and mounting of both intraoral and extraoral dental radiography. METHOD: The present study employed a total of 22,334 intraoral images and 1,035 extraoral images to train the model. The performance of the model was tested on an independent internal dataset and two external datasets from different institutes. Images were categorized into 32 tooth areas. The VGG-16, ResNet-18, and ResNet-101 architectures were used for pretraining, with the ResNet-101 ultimately being chosen as the final trained model. The model's performance was evaluated using metrics of accuracy, precision, recall, and F1 score. Additionally, we evaluated the influence of misalignment on the model's accuracy and time efficiency. RESULTS: The ResNet-101 model outperformed VGG-16 and ResNet-18 models, achieving the highest accuracy of 0.976, precision of 0.969, recall of 0.984, and F1-score of 0.977 (p < 0.05). For intraoral images, the overall accuracy remained consistent across both internal and external datasets, ranging from 0.963 to 0.972, without significant differences (p = 0.348). For extraoral images, the accuracy consistently achieved the highest value of 1 across all institutes. The model's accuracy decreased as the tilt angle of the X-ray film increased. The model achieved the highest accuracy of 0.981 with correctly aligned films, while the lowest accuracy of 0.937 was observed for films exhibiting severe misalignment of ± 15° (p < 0.001). The average time required for the tasks of image rotation and classification for each image was 0.17 s, which was significantly faster than that of the manual process, which required 1.2 s (p < 0.001). CONCLUSION: This study demonstrated the potential of DL-based models in automating dental film mounting with high accuracy and efficiency. The proper alignment of X-ray films is crucial for accurate classification by the model.

Developing Low-Cost Mobile Device and Apps for Accurate Skin Spectrum Measurement via Low-Cost Spectrum Sensors and Deep Neural Network Technology.

In recent years, skin spectral information has been gradually applied in various fields, such as the cosmetics industry and clinical medicine. However, the high price and the huge size of the skin spectrum measurement device make the related applications of the skin spectrum unable to be widely used in practical applications. We used convolutional neural network (CNN) to achieve a satisfying accuracy of the Fitzpatrick skin-type classification by using a simple self-developed device in 2018. Leveraging on the hardware, firmware, and software app-developing experience, a low-cost miniature skin spectrum measurement system (LMSSMS) using deep neural network (DNN) technology was further studied, and the feasibility of the system is verified in this paper. The developed LMSSMS is divided into three parts: (1) miniature skin spectrum measurement device (MSSMD), (2) DNN model, and (3) mobile app. The MSSMD was developed with innovative low-cost MSSC, 3D printing, and a simple LED light source. The DNN model is designed to enhance measurement accuracy. Finally, the mobile app is used to control and show the measurement results. The developed app also includes a variety of skin-spectrum-related applications, such as erythema index and melanin index (EI/MI) measurement, Fitzpatrick skin-type classification, Pantone SkinTone classification, sun-exposure estimation, and body-fat measurement. In order to verify the feasibility of LMSSMS, we used the standard instrumentation device as a reference. The results show that the accuracy of the LMSSMS can reach 94.7%, which also confirms that this development idea has much potential for further development.

Development of a Portable All-Wavelength PPG Sensing Device for Robust Adaptive-Depth Measurement: A Spectrometer Approach with a Hydrostatic Measurement Example.

Photoplethysmography (PPG), a noninvasive optical sensing technology, has been widely used to measure various physiological indices. Over-the-counter PPG devices are typically composed of a single-wavelength light source, namely, single-wavelength PPG (SW-PPG). It is known that signals of SW-PPG are easily contaminated or distorted by measurement conditions such as motion artifacts, wearing pressure, and skin type. Since lights of different wavelengths can penetrate skin tissues at different depths, how to effectively construct a multiwavelength PPG (MW-PPG) device or even an all-wavelength PPG (AW-PPG) device has attracted great attention. There is also a very interesting question, that is, what could be the potential benefits of using MW-PPG or AW-PPG devices? This paper demonstrates the construction of an AW-PPG portable device and conducts a preliminary evaluation. The presented device consists of four light-emitting diodes, a chip-scale spectrometer, a microcontroller, a Bluetooth Low Energy transceiver, and a phone app. The maximum ratio combining algorithm (MRC) is used to combine the PPG signals derived from different wavelengths to achieve a better signal-to-noise ratio (S/N). The PPG signals from the developed MRC-AW-PPG device versus those from the conventional SW-PPG device are compared in terms of different hydrostatic pressure conditions. It has been observed that the MRC-AW-PPG device can provide more stable PPG signals than that of a conventional PPG device. The results shine a light on the potential benefits of using multiple wavelengths for the next generation of noninvasive PPG sensing.

MW-PPG Sensor: An on-Chip Spectrometer Approach.

Multi-wavelength photoplethysmography (MW-PPG) sensing technology has been known to be superior to signal-wavelength photoplethysmography (SW-PPG) sensing technology. However, limited by the availability of sensing detectors, many prior studies can only use conventional bulky and pricy spectrometers as the detectors, and hence cannot bring the MW-PPG technology to daily-life applications. In this study we developed a chip-scale MW-PPG sensor using innovative on-chip spectrometers, aimed at wearable applications. Also in this paper we present signal processing methods for robustly extracting the PPG signals, in which an increase of up to 50% in the signal-to-noise ratio (S/N) was observed. Example measurements of saturation of peripheral blood oxygen (SpO2) and blood pressure were conducted.

Preparation and Characterization of Ion-Irradiated Nanodiamonds as Photoacoustic Contrast Agents.

Highly radiation-damaged or irradiated nanodiamonds (INDs) are a new type of nanomaterial developed recently as a potential photoacoustic (PA) contrast agent for deep-tissue imaging. This work characterized in detail the photophysical properties of these materials prepared by ion irradiation of natural diamond powders using various spectroscopic methods. For 40-nm NDs irradiated with 40-keV He+ at a dose of 3 x 10(15) ions/cm2, an average molar extinction coefficient of 4.2 M-1 cm-1 per carbon atom was measured at 1064 nm. Compared with gold nanorods of similar dimensions (10 nm x 67 nm), the INDs have a substantially smaller (by > 4 orders of magnitude) molar extinction coefficient per particle. However, the deficit is readily compensated by the much higher thermal stability, stronger hydrophilic interaction with water, and a lower nanobubble formation threshold (~30 mJ/cm2) of the sp3-carbon-based nanomaterial. No sign of photodamage was detected after high-energy (>100 mJ/cm2) illumination of the INDs for hours. Cell viability assays at the IND concentration of up to 100 µg/mL showed that the nanomaterial is non-cytotoxic and potentially useful for long-term PA bioimaging applications.

Using K-Nearest Neighbor Classification to Diagnose Abnormal Lung Sounds.

A reported 30% of people worldwide have abnormal lung sounds, including crackles, rhonchi, and wheezes. To date, the traditional stethoscope remains the most popular tool used by physicians to diagnose such abnormal lung sounds, however, many problems arise with the use of a stethoscope, including the effects of environmental noise, the inability to record and store lung sounds for follow-up or tracking, and the physician's subjective diagnostic experience. This study has developed a digital stethoscope to help physicians overcome these problems when diagnosing abnormal lung sounds. In this digital system, mel-frequency cepstral coefficients (MFCCs) were used to extract the features of lung sounds, and then the K-means algorithm was used for feature clustering, to reduce the amount of data for computation. Finally, the K-nearest neighbor method was used to classify the lung sounds. The proposed system can also be used for home care: if the percentage of abnormal lung sound frames is > 30% of the whole test signal, the system can automatically warn the user to visit a physician for diagnosis. We also used bend sensors together with an amplification circuit, Bluetooth, and a microcontroller to implement a respiration detector. The respiratory signal extracted by the bend sensors can be transmitted to the computer via Bluetooth to calculate the respiratory cycle, for real-time assessment. If an abnormal status is detected, the device will warn the user automatically. Experimental results indicated that the error in respiratory cycles between measured and actual values was only 6.8%, illustrating the potential of our detector for home care applications.

Two-photon fluorescence correlation spectroscopy of lipid-encapsulated fluorescent nanodiamonds in living cells.

Dynamics of fluorescent diamond nanoparticles in HeLa cells has been studied with two-photon fluorescence correlation spectroscopy (FCS). Fluorescent nanodiamond (FND) is an excellent fluorescent probe for bioimaging application, but they are often trapped in endosomes after cellular uptake. The entrapment prohibits FCS from being performed in a time frame of 60 s. Herein, we show that the encapsulation of FNDs within a lipid layer enhances the diffusion of the particles in the cytoplasm by more than one order of magnitude, and particles as small as 40 nm can be probed individually with high image contrast by two-photon excited luminescence. The development of the technique together with single particle tracking through one-photon excitation allows probing of both short-term and long-term dynamics of single FNDs in living cells.

Receptor-mediated cellular uptake of folate-conjugated fluorescent nanodiamonds: a combined ensemble and single-particle study.

Fluorescent nanodiamonds (FNDs) are nontoxic and photostable nanomaterials, ideal for long-term in vivo imaging applications. This paper reports that FNDs with a size of approximately 140 nm can be covalently conjugated with folic acid (FA) for receptor-mediated targeting of cancer cells at the single-particle level. The conjugation is made by using biocompatible polymers, such as polyethylene glycol, as crosslinked buffer layers. Ensemble-averaged measurements with flow cytometry indicate that more than 50% of the FA-conjugated FND particles can be internalized by the cells (such as HeLa cells) through receptor-mediated endocytosis, as confirmed by competitive inhibition assays. Confocal fluorescence microscopy reveals that these FND particles accumulate in the perinuclear region. The absolute number of FNDs internalized by HeLa cells after 3 h of incubation at a particle concentration of 10 microg mL(-1) is in the range of 100 particles per cell. The receptor-mediated uptake process is further elucidated by single-particle tracking of 35-nm FNDs in three dimensions and real time during the endocytosis.

Facile MALDI-MS analysis of neutral glycans in NaOH-doped matrixes: microwave-assisted deglycosylation and one-step purification with diamond nanoparticles.

A streamlined protocol has been developed to accelerate, simplify, and enhance matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS) of neutral underivatized glycans released from glycoproteins. It involved microwave-assisted enzymatic digestion and release of glycans, followed by rapid removal of proteins and peptides with carboxylated/oxidized diamond nanoparticles, and finally treating the analytes with NaOH before mixing them with acidic matrix (such as 2,5-dihydroxybenzoic acid) to suppress the formation of both peptide and potassiated oligosaccharide ions in MS analysis. The advantages of this protocol were demonstrated with MALDI-TOF-MS of N-linked glycans released from ovalbumin and ribonuclease B.

On the estimation of target spectrum for filter-array based spectrometers.

Miniature spectrometers have been drawn researchers much attention due to its wide variety of possible applications. In this paper, we show the achievability of a fine spectrometer on-a-chip based on a low-performance, low-cost filter-array. A low quality filter-array is augmented with digital signal processing techniques. A series of estimators for recovering target spectrum is introduced. By exploiting non-negative nature of spectral content, a non-negative least-square algorithm is found particularly useful for spectrum recovery. The concept is verified in a hardware implementation.

Photoacoustic emission from fluorescent nanodiamonds enhanced with gold nanoparticles.

Fluorescent nanodiamonds (FNDs) have drawn much attention in recent years for biomedical imaging applications due to their desired physical properties including excellent photostability, high biocompatibility, extended far-red fluorescence emission, and ease of surface functionalization. Here we explore a new feature of FNDs, i.e. their photoacoustic emission capability, which may lead to potential applications of using FNDs as a dual imaging contrast agent for combined fluorescence and photoacoustic imaging modalities. We observed significant enhancement of photoacoustic emission from FNDs when they were conjugated with gold nanoparticles (GNPs).

Dimensionality reduction in control and coordination of the human hand.

The concept of kinematic synergies is proposed to address the dimensionality reduction problem in control and coordination of the human hand. This paper develops a method for extracting kinematic synergies from joint-angular-velocity profiles of hand movements. Decomposition of a limited set of synergies from numerous movements is a complex optimization problem. This paper splits the decomposition process into two stages. The first stage is to extract synergies from rapid movement tasks using singular value decomposition (SVD). A bank of template functions is then created from shifted versions of the extracted synergies. The second stage is to find weights and onset times of the synergies based on l(1) -minimization, whose solutions provide sparse representations of hand movements using synergies.

Temporal postural synergies of the hand in rapid grasping tasks.

Postural synergies of the hand have been widely proposed in the literature, but only a few attempts were made to visualize temporal postural synergies, i.e., profiles of postural synergies varying over time. This paper aims to derive temporal postural synergies from kinematic synergies extracted from joint angular velocity profiles of rapid grasping movements. The rapid movements constrain the kinematic synergies to combine instantaneously, and thus, the movements can be approximated by a weighted summation of synchronous synergies. After being extracted by using singular value decomposition, the synchronous kinematic synergies were translated into temporal postural synergies, which revealed strategies of enslaving, metacarpal flexion for larger movements, and hierarchical recruitment of joints, adapted by subjects while grasping.

A power-efficient communication system between brain-implantable devices and external computers.

In this paper, we propose a power efficient communication system for linking a brain-implantable device to an external system. For battery powered implantable devices, the processor and the transmitter power should be reduced in order to both conserve battery power and reduce the health risks associated with transmission. To accomplish this, a joint source-channel coding/decoding system is devised. Low-density generator matrix (LDGM) codes are used in our system due to their low encoding complexity. The power cost for signal processing within the implantable device is greatly reduced by avoiding explicit source encoding. Raw data which is highly correlated is transmitted. At the receiver, a Markov chain source correlation model is utilized to approximate and capture the correlation of raw data. A turbo iterative receiver algorithm is designed which connects the Markov chain source model to the LDGM decoder in a turbo-iterative way. Simulation results show that the proposed system can save up to 1 to 2.5 dB on transmission power.