Study of Synthesis of Dual-Curing Thermoplastic Polyurethane Hot-Melt Adhesive and Optimization by Using Gray Relational Analysis to Apply in Fabric Industry to Solve Seamless Bonding Issues

People wear clothes for warmth, survival and necessity in modern life, but in the modern era, eco-friendliness, shortened production times, design and intelligence also matter. To determine the relationship between data series and verify the proximity of each data series, a gray relational analysis, or GRA, is applied to textiles, where seamless bonding technology enhances the bond between components. In this study, a polyurethane prepolymer, 2-hydroxyethyl acrylate (2-HEA) as an end-capping agent and n-octyl acrylate (ODA) as a photoinitiator were used to synthesize a dual-curing polyurethane hot-melt adhesive. Taguchi quality engineering and a gray relational analysis were used to discuss the influence of different mole ratios of NCO:OH and the effect of the molar ratio of the addition of octyl decyl acrylate on the mechanical strength. The Fourier transform infrared spectroscopy (FTIR) results showed the termination of the prepolymer's polymerization reaction and the C=O peak intensity at 1730 cm-1, indicating efficient bonding to the main chain. Advanced Polymer Chromatography (APC) was used to investigate the high-molecular-weight (20,000-30,000) polyurethane polymer bonded with octyl decyl acrylate to achieve a photothermosetting effect. The thermogravimetric analysis (TGA) results showed that the thermal decomposition temperature of the polyurethane hot-melt adhesive also increased, and they showed the highest pyrolysis temperature (349.89 °C) for the polyhydric alcohols. Furthermore, high peel strength (1.68 kg/cm) and shear strength (34.94 kg/cm2) values were detected with the dual-cure photothermosetting polyurethane hot-melt adhesive. The signal-to-noise ratio was also used to generate the gray relational degree. It was observed that the best parameter ratio of NCO:OH was 4:1 with five moles of monomer. The Taguchi quality engineering method was used to find the parameters of single-quality optimization, and then the gray relation calculation was used to obtain the parameter combination of multi-quality optimization for thermosetting the polyurethane hot-melt adhesive. The study aims to meet the requirements of seamless bonding in textile factories and optimize experimental parameter design by setting target values that can effectively increase production speed and reduce processing time and costs as well.

Photocurable Carbon Nanotube/Polymer Nanocomposite for the 3D Printing of Flexible Capacitive Pressure Sensors

A photocurable resin/carbon nanotube (CNT) nanocomposite was fabricated from aligned CNTs in an acrylic matrix. The conductivity of the nanocomposite increased rapidly and then stabilized when the CNT content was increased up to and beyond the percolation threshold. Various structures were created using a digital light processing (DLP) 3D printer. Various polymeric dispersants (SMA-amide) were designed and synthesized to improve the CNT dispersion and prevent aggregation. The benzene rings and lone electron pairs on the dispersant interacted with aromatic groups on the CNTs, causing the former to wrap around the latter. This created steric hindrance, thereby stabilizing and dispersing the CNTs in the solvent. CNT/polymer nanocomposites were created by combining the dispersant, CNTs, and a photocurable resin. The CNT content of the nanocomposite and the 3D printing parameters were tuned to optimize the conductivity and printing quality. A touch-based human interface device (HID) that utilizes the intrinsic conductivity of the nanocomposite and reliably detects touch signals was fabricated, enabling the free design of sensors of various styles and shapes using a low-cost 3D printer. The production of sensors without complex circuitry was achieved, enabling novel innovations.

In Vivo Automatic and Quantitative Measurement of Adult Human Larynx and Vocal Fold Images

INTRODUCTION

Qualitative laryngoscopy belongs to a diagnostic routine. Nevertheless, quantitative morphometric measurements of laryngeal structures remain challenging. This study aimed to introduce a special laser projection device that can facilitate computer-assisted digitalized analysis and provide important quantitative information for diagnostics and treatment planning.

MATERIALS AND METHODS

The laryngeal images were captured with our device, which contained two parallel laser beams in order to provide the scaling reference. The maximum length of the vocal fold during respiration and vibration (phonation), vocal width at midpoint, total fold area, maximum cross-sectional area of the glottic space, and maximum vocal fold angle were determined and calculated. These parameters were analyzed and compared on the basis of age, sex, body height, body weight and body mass index.

RESULTS

A total of 87 subjects were enrolled in this study, comprising 39 males and 48 females. The age range for all subjects was 21 to 80 years old. The maximum value of the glottic area and vocal angle showed no significant gender difference. Both the respiration and vibration vocal fold length was significantly longer in males than in females. The vocal width revealed no gender difference, but the fold area during both respiration and phonation was significantly larger in men than in women. As for the respiration-to-vibration ratio of the vocal length, there was a trend, but without statistical significance (P = 0.06), toward a higher length compression ratio in men than in women. Meanwhile, age was found to have a strong relationship with vocal width during phonation. The width of vibration vocal fold decreased with aging significantly.

CONCLUSION

Our innovative module can provide reference parameters, which makes it possible to directly estimate the objective absolute values of relevant laryngeal structures. Our non-invasive approach can be used during routine laryngoscopy and the findings easily documented. In future, we can extend its clinical application to measure subtle laryngeal or hypopharyngeal changes, which are difficult to objectively quantify.

Integration of Multivariate Statistical Control Chart and Machine Learning to Identify the Abnormal Process Parameters for Polylactide with Glass Fiber Composites in Injection Molding; Part I: The Processing Parameter Optimization for Multiple Qualities of Polylactide/Glass Fiber Composites in Injection Molding

This paper discusses the mixing of polylactide (PLA) and glass fiber which use injection molding to produce a functional composite material with glass fiber properties. The injection molding process explores the influence of glass fiber ratio, melt temperature, injection speed, packing pressure, packing time and cooling time on the mechanical properties of composite. Using the orthogonal table planning experiment of the Taguchi method, the optimal parameter level combination of a single quality process is obtained through main effect analysis (MEA) and Analysis of variance (ANOVA). Then, the optimal parameter level combination of multiple qualities is obtained through principal component analysis (PCA) and data envelopment analysis (DEA), respectively. It is observed that if all the quality characteristics of tensile strength, hardness, impact strength and bending strength are considered at the same time, the optimal process conditions are glass fiber addition 20 wt %, melt temperature 185 °C, injection speed 80 mm/s, holding pressure 60 MPa, holding time 1 s and cooling time 15 s, and the corresponding mechanical properties are tensile strength 95.04 MPa, hardness 86.52 Shore D, impact strength 4.4408 J/cm², bending strength 119.89 MPa. This study effectively enhances multiple qualities of PLA/GF composite.

Highly photoactive novel NiS/BiOI nanocomposite photocatalyst towards efficient visible light organic pollutant degradation and carcinogenetic Cr (VI) reduction for environmental remediation

Heterojunction engineering in catalyst structures is a promising approach for solving the main restriction of the narrow photoabsorption range and quick recombination of photogenerated charge carriers in the photocatalysts. Herein, a simple, eco-friendly, non-toxic, and novel Z-scheme heterojunction of nanoflower-like NiS/BiOI was systematically designed using the low-temperature solvothermal and precipitation methods. The physicochemical and photo-electrochemical properties of the as-synthesized nanomaterials were characterized using XRD, FESEM, FT-IR, XPS, BET, UV-vis, PL, and EIS. NiS/BiOI nanomaterials exhibited a wide photoabsorption range (200-1000 nm), a narrow bandgap energy (1.76 eV), a large surface area (35.82 m² g^-1), and a low charge carrier recombination rate because of the synergistic effects of the NiS and BiOI photocatalysts, which could be the basis for superior photocatalytic efficiency. Particularly, the optimal 40% NiS/BiOI nanocomposite exhibited better stability and efficiency than the pure NiS and BiOI. The maximum degradation efficiency of rhodamine B (RhB) was 99.8% after 200 min, tetracycline (TC) was 96.3% after 140 min, and the photoreduction of Cr(VI) was 92.8% after 180 min rather than the pure NiS and BiOI under visible light irradiation. The constant rate (k) of RhB was approximately 10 and 4, TC was 12 and 4, and Cr(VI) was 10 and 8 times that of pristine NiS and BiOI, respectively. Radical trapping experiments and Tauc plot analysis proposed the design of the plausible Z-scheme reaction mechanism between NiS and BiOI, which has a crucial role in the rate of transportation and separation of electron/hole pairs. This investigation provides a venue for the design of a photoactive NiS-based nanocomposite for environmental remediation.

Synthesis of novel Type-II MnNb2O6/g-C3N4 Mott-Schottky heterojunction photocatalyst: Excellent photocatalytic performance and degradation mechanism of fluoroquinolone-based antibiotics

Fluoroquinolone antibiotics have been encountered in aquatic environments in quantities giving rise to significant concern recently. To cope with this problem, it is necessary to design a semiconductor photocatalyst having excellent photocatalytic efficiency to eliminate the antibiotics. The heterojunction is a likely situate where the efficiency of relevant photocatalyst can be strengthened. In this study, the performance of MnNb₂O₆/g-C₃N₄ (MNO/g-CN) composites in the photocatalytic degradation of ciprofloxacin (CIP) and tetracycline-HCl (TCH) antibiotics was explored. Enhanced photocatalytic activity of MNO/g-CN was found to be owing to electron's shifting between the MNO, and g-CN sheets, which promotes the formation of photo-generated e⁻/h⁺ pairs. This shows a low-waste, high-performance material exists to eradicate CIP and TCH from wastewater. Further, the structural, photochemical and light interacted properties of the MNO/g-CN photocatalyst, prepared by solvothermal method and sonication, were described using photochemical, physiochemical and electrochemical approaches. The synthesized photocatalyst owes its particular efficiency to its methodical photo-degradation of CIP and TC using visible light. The optimum composite 15% MNO/g-CN evinced the greatest photocatalytic efficiency with CIP and TCH photo-degradation of 94.10%, and 98.50%, respectively, and degradation mechanism were investigated using LC-MS spectroscopy. The suitable photocatalytic activity is ascribed to lower the recombination's rate of e⁻/h⁺ pairs. The scavenging evaluations proved that the h⁺ and ^•O₂^- were two major photoactive species accomplishing the CIP and TCH photodegradation over MNO/g-CN under visible region. Our findings pave the way for the construction of efficient binary photocatalysts for antibiotic restitution.

Au Nanorods on Carbon-Based Nanomaterials as Nanohybrid Substrates for High-Efficiency Dynamic Surface-Enhanced Raman Scattering

Gold nanorods (AuNRs) with different aspect ratios were prepared by the seed-mediated growth method and combined with three carbon-based nanomaterials of multiple dimensions (i.e., zero-dimensional (0D) carbon black (CB), one-dimensional (1D) carbon nanotubes (CNTs), and two-dimensional (2D) graphene oxide (GO)). The AuNR/carbon-based nanomaterial hybrids were utilized in dynamic surface-enhanced Raman scattering (D-SERS). First, cetyltrimethylammonium bromide (CTAB) was used to stabilize and coat the AuNRs, enabling them to be dispersed in water and conferring a positive charge to the surface. AuNR/carbon-based nanomaterial hybrids were then formed via electrostatic attraction with the negatively charged carbon-based nanomaterials. Subsequently, the AuNR/carbon-based nanomaterial hybrids were utilized as large-area and highly sensitive Raman spectroscopy substrates. The AuNR/GO hybrids afforded the best signal enhancement because the thickness of GO was less than 5 nm, which enabled the AuNRs adsorbed on GO to produce a good three-dimensional hotspot effect. The enhancement factor (EF) of the AuNR/GO hybrids for the dye molecule Rhodamine 6G (R6G) reached 1 × 10⁷, where the limit of detection (LOD) was 10^-8 M. The hybrids were further applied in D-SERS (detecting samples transitioning from the wet state to the dry state). During solvent evaporation, the system spontaneously formed many hotspots, which greatly enhanced the SERS signal. The final experimental results demonstrated that the AuNR/GO hybrids afforded the best D-SERS signal enhancement. The EF value for R6G reached 1.1 × 10⁸ after 27 min, with a limit of detection of 10^-9 M at 27 min. Therefore, the AuNR/GO nanohybrids have extremely high sensitivity as molecular sensing elements for SERS and are also very suitable for the rapid detection of single molecules in water quality and environmental management.

Design of Acrylate-Terminated Polyurethane for Nylon Seamless Bonding Fabric Part I: Design of the End-Capping Thermoplastic Polyurethane Adhesive with Acrylate Copolymer

This series of studies aims to design acrylate-terminated polyurethanes for use in nylon seamless bonded fabrics. The first part used N,N-dimethylacrylamide (DMAA) and methyl methacrylate (MMA) to replace the chain extender in polyurethane synthesis as end-capping agent to synthesize thermoplastic polyurethane (TPU) adhesive. The molecular weight of the TPU is controlled to further influence the mechanical and processing properties of the polyurethane. Here, polytetramethylene ether glycol (PTMG) and 4,4-methylene diphenyl diisocyanate (MDI) were polymerized, and then a blocking agent was added thereto. The results show that the characteristic peaks of benzene ring and carbamate of TPU adhesive are at 1596 cm−1 and 1413 cm−1, respectively, while the characteristic peaks of DMAA are at 1644 cm−1 and 1642 cm−1 in the FT-IR spectrum. There is an absorption peak –N=C=O– which is not shown near 2268 cm−1, which proves that the structure of TPU contains the molecular structure of capping agent, PTMG and MDI. When the DMAA concentration in the capping agent was increased from 3.0 wt% to 10 wt%, the –C=O (H-bond) area percentage of hydrogen bonds formed at 1711 cm−1 increased from 41.7% to 57.6%, while the –NH (H bond) produced at 3330 cm−1 increased from 70% to 81%. These phenomena suggest that increasing the concentration of DMAA capping agent can effectively promote the formation of complex supramolecular network structures by hydrogen bonding in TPU. The content and concentration of the capping agent affects the molecular weight of the TPU. Chain growth is terminated when molecular weight growth can be effectively controlled and reduced. It was observed in thermal analysis that with increasing DMAA concentration in the molecular structure, the concentration of capping agent in TPU, hydrogen bonding force between hard segments, melting point (Tmh) and melting enthalpy (ΔH) all increased the capping agent. The pyrolysis temperature of TPU is increased by 10–20 °C.

Automated Optical Inspection for Defect Identification and Classification in Actual Woven Fabric Production Lines

This paper presents a turnkey integrated system that can be operated in real time for real textile manufacturers. Eight types of defects in woven fabric, including stain, broken end, broken weft, hole, nep, double pick, kinky weft and float can be recognized and classified. First, an image is captured by a CMOS industrial camera with a pixel size of 4600 × 600 above the batcher at 20 m/min. After that, the four-stage image processing procedure is applied to detect defects and for classification. Stage 1 is image pre-processing; the filtration of the image noise is carried out by a Gaussian filter. The light source is corrected to reduce the uneven brightness resulting from halo formation. The improved mask dodging algorithm is used to reduce the standard deviation of the corrected original image. Afterwards, the background texture is filtered by an averaging filter, and the mean value is corrected for histogram shifting, so that this system is robust to the texture and color changes of woven fabric. The binary segmentation threshold is determined using the mean value and standard deviation of an image with a normal sample. Stage 2 uses adaptive binarization for separation of the background and defects and to filter the noise. In Stage 3, the morphological processing is used before the defect contour is circled, i.e., four features of each block, including the defect area, the aspect ratio of the defect, the average gray level of the defect and the defect orientation, which are calculated according to the range of contour. The image defect recognition dataset consists of 2246 images. The results show that the detection success rate is 96.44%, and the false alarm rate is 3.21%. In Stage 4, the defect classification is implemented. The support vector machine (SVM) is used for classification, 230 defect images are used as training samples, and 206 are used as test samples. The experimental results show that the overall defect recognition rate is 96.60%, providing that the software and hardware equipment designed in this study can implement defect detection and classification for woven fabric effectively.

Development of Multifunctional Nano-Graphene-Grafted Polyester to Enhance Thermal Insulation and Performance of Modified Polyesters

Nano-graphene materials have improved many thermal properties based on polymer systems. The additive polymers’ thermal insulation cannot be significantly increased for use as a reinforcement in multifunctional thermally insulating polymer foam. Herein, we present the development of far-infrared emissivity and antistatic properties using multifunctional nano-graphene polyester fibers. Nano-graphene far-infrared thermal insulation polyester was synthesized with 2% nano-graphene and dispersant polypropylene wax-maleic anhydride (PP wax-MA) using the Taguchi method combined with grey relational analysis (GRA) to improve the thermal properties and the performance of the polymer composite. The thermogravimetric analysis (TGA) shows that the pyrolysis temperature of spinning-grade polyester was increased when the nano-graphene powder was added to the polyester. The differential scanning calorimeter (DSC) analysis confirmed the modification of polyester by nano-graphene, showing the effect of the nucleating agent, which ultimately improved the performance of the polyester. The physical properties of the optimized polyester fibers were improved with a yarn count of 76.5 d, tensile strength of 3.3 g/d, and an elongation at break increased from 23.5% to 26.7% compared with unmodified polymer yarn. These far-infrared emission rates increased from 78% to 83%, whereas the far-infrared temperature increased from 4.0 °C to 22 °C, and the surface resistance increased to 108 Ω. The performance of the optimized modified polyester yarn is far better than single-polypropylene-grafted maleic anhydride yarn. The performance of optimized modified polyester yarn, further confirmed using grey correlation analysis (GRA), can improve the yarns’ mechanical properties and far-infrared functions. Our findings provide an alternative route for developing nano-graphene polyester fabrics suitable for the fabric industry.

Quality Prediction and Abnormal Processing Parameter Identification in Polypropylene Fiber Melt Spinning Using Artificial Intelligence Machine Learning and Deep Learning Algorithms

Melt spinning machines must be set up according to the process parameters that result in the best end product quality. In this study, artificial intelligence algorithms were employed to create a system that detects abnormal processing parameters and suggests strategies to improve quality. Polypropylene (PP) was selected as the experimental material, and the quality achieved by adjusting the melt spinning machine’s processing parameter settings was used as the basis for judgement. The processing parameters included screw temperature, gear pump temperature, die head temperature, screw speed, gear pump speed, and take-up speed as the six control factors. The four quality characteristics included fineness, breaking strength, elongation at break, and elastic energy modulus. In the first part of our study, we applied fast deep-learning characteristic grid calculations on a 440-item historical data set to train a deep learning neural network and determine methods for multi-quality optimization. In the second part, with the best processing parameters as a benchmark, and given abnormal quality data derived from processing parameter settings deviating from these optimal values, several machine learning and deep learning methods were compared in their ability to find the settings responsible for the abnormal data, which was randomly split into a 210-item training data set and a 210-item verification data set. The random forest method proved to be the best at identifying responsible parameter settings, with accuracy rates of single and double identification classifications together of 100%, for single factor classification of 98.3%, and for double factor classification of 96.0%, thereby confirming that the diagnostic method proposed in this study can effectively predict product abnormality and find the parameter settings responsible for product abnormality.

Synthesis of novel CuNb2O6/g-C3N4 binary photocatalyst towards efficient visible light reduction of Cr (VI) and dyes degradation for environmental remediation

The further development of an efficient and sustainable water treatment requires the development of a very active and controllable photocatalyst. The heterojunction is a promising site where the activity of such a photocatalyst can be enhanced. Organic dyes have become a severe concern in recent years owing to their significant presence in wastewater. Hexavalent Chromium (Cr (VI)) is a potential carcinogen also exhibiting great persistence in wastewater. So, a low-waste, high-performance materials is required to eliminate organic dyes and Cr (VI) from wastewater. In this study, CNO/g-CN (CuNb₂O₆/g-C₃N₄) photocatalyst synthesized via co-precipitation, followed by calcination which were characterized using physiochemical and photo-electrochemical approaches to identify their structural, photochemical and optical traits. The uniqueness of the synthesized photocatalyst is due to both its efficient photo-reduction of Cr (VI) and photo-degradation of Rhodamine B (RhB), Methylene Blue (MB) and Methyl Orange (MO) under visible light. The CNO/g-CN composite with 30% CNO heterojunctions exhibited the highest photocatalytic activity with Cr (VI) 92.80% photoreduction and efficiency degradation for RhB, MB, MO of 99.6%, 98.50%, 99.0%, respectively, with constant rate (k). This efficient photocatalytic activity is attributed to the lower recombination rate of electron-hole pairs. Free radical trapping experiments showed that ^•O₂^- and h⁺ play an important role in the photodegradation. The study, therefore, opens an alternative route in the synthesis of very efficient binary photocatalysts for application in environmental remediation.

Semi-Supervised Deep Learning Semantic Segmentation for 3D Volumetric Computed Tomographic Scoring of Chronic Rhinosinusitis: Clinical Correlations and Comparison with Lund-Mackay Scoring

Background: The traditional Lund-Mackay score (TLMs) is unable to subgrade the volume of inflammatory disease. We aimed to propose an effective modification and calculated the volume-based modified LM score (VMLMs), which should correlate more strongly with clinical symptoms than the TLMs. Methods: Semi-supervised learning with pseudo-labels used for self-training was adopted to train our convolutional neural networks, with the algorithm including a combination of MobileNet, SENet, and ResNet. A total of 175 CT sets, with 50 participants that would undergo sinus surgery, were recruited. The Sinonasal Outcomes Test-22 (SNOT-22) was used to assess disease-specific symptoms before and after surgery. A 3D-projected view was created and VMLMs were calculated for further comparison. Results: Our methods showed a significant improvement both in sinus classification and segmentation as compared to state-of-the-art networks, with an average Dice coefficient of 91.57%, an MioU of 89.43%, and a pixel accuracy of 99.75%. The sinus volume exhibited sex dimorphism. There was a significant positive correlation between volume and height, but a trend toward a negative correlation between maxillary sinus and age. Subjects who underwent surgery had significantly greater TLMs (14.9 vs. 7.38) and VMLMs (11.65 vs. 4.34) than those who did not. ROC-AUC analyses showed that the VMLMs had excellent discrimination at classifying a high probability of postoperative improvement with SNOT-22 reduction. Conclusions: Our method is suitable for obtaining detailed information, excellent sinus boundary prediction, and differentiating the target from its surrounding structure. These findings demonstrate the promise of CT-based volumetric analysis of sinus mucosal inflammation.

Laryngopharyngeal reflux image quantization and analysis of its severity

Laryngopharyngeal reflux (LPR) is a prevalent disease affecting a high proportion of patients seeking laryngology consultation. Diagnosis is made subjectively based on history, symptoms, and endoscopic assessment. The results depend on the examiner's interpretation of endoscopic images. There are still no consistent objective diagnostic methods. The aim of this study is to use image processing techniques to quantize the laryngeal variation caused by LPR, to judge and analyze its severity. This study proposed methods of screening sharp images automatically from laryngeal endoscopic images and using throat eigen structure for automatic region segmentation. The proposed image compensation improved the illumination problems from the use of laryngoscope lens. Fisher linear discriminant was used to find out features and classification performance while support vector machine was used as the classifier for judging LPR. Evaluation results were 97.16% accuracy, 98.11% sensitivity, and 3.77% false positive rate. To evaluate the severity, quantized data of the laryngeal variation was used. LPR images were combined with reflux symptom index score chart, and severity was graded using a neural network. The results indicated 96.08% accuracy. The experiment indicated that laryngeal variation induced by LPR could be quantized by using image processing techniques to assist in diagnosing and treating LPR.

Quantitative Morphometric Measurements of the Oropharynx in Obstructive Sleep Apnea Syndrome Using a Laser Depth Measurement Module

INTRODUCTION

Current diagnostic routines in obstructive sleep apnea syndrome (OSAS), including drug-induced sleep endoscopy (DISE), provide qualitative data. Quantitative morphometric measurements of oropharyngeal structures remain challenging. This study aims to introduce a special linear laser projection device that can facilitate computer-assisted digitalized analysis and provide important quantitative information for OSAS prediction.

MATERIALS AND METHODS

We used a single-wavelength green three-linear laser to provide the scaling reference, with one at an angle of 8.5 degrees with the other two which were parallel. The oropharyngeal images were divided into two groups: the non-OSAS and OSAS group, after polysomnography. A minimum of three evaluations were carried out to determine the maximum cross-sectional area of the oropharyngeal inlet (CSAOI) and the retropalatal depth.

RESULTS

A total of 132 subjects were enrolled in this study, with 76 subjects in the non-OSAS group and 56 cases in the OSAS group. In the non-OSAS group, the CSAOI was significantly larger in males than in females. There was a trend toward deeper retropalatal region in men than in women (14.25 vs 11.76 mm). Correlation analysis revealed that retropalatal depth is significantly related to body height and the CSAOI. The body weight and BMI of patients with OSAS were significantly higher than those of participants without OSAS. The retropalatal depth and CSAOI were significantly decreased in OSAS patients as compared to those without OSAS. Our new parameter, the oropharyngeal index, showed the most outstanding discrimination by ROC analysis to predict OSAS.

CONCLUSION

Our innovative module can provide reference parameters, which make it possible to directly estimate the objective absolute values of relevant oropharyngeal structures. Our non-invasive approach can be used for outpatient screening, since it allows the identification of potential OSAS patients who should be referred for polysomnography, as many patients do not require DISE early in their evaluation.

Prognostic value of tumor volume for patients with advanced lung cancer treated with chemotherapy

BACKGROUND AND OBJECTIVE

We aim to develop a reference system utilizing computed tomography to calculate changes in tumor volume of lung cancer patients after chemotherapy to assist physicians in clinical treatment and evaluation.

METHODS

Image processing techniques were used to analyze the computed tomography of lung cancer, locate the tumor, and calculate the tumor volume. The medical indicator was then evaluated and analyzed. We examined the correlation between reduced tumor volume and survival duration of 88 patients after chemotherapy at Tri-Service General Hospital, Taiwan. The innovative survival prediction index was obtained by four statistical methods: receiver operating characteristic curve, Youden index, Kaplan-Meier method, and log rank test.

RESULTS

From the image processing techniques, tumor volume from each patient were obtained within an average of 7.25 seconds. The proposed method was shown to achieve rapid positioning of lung tumors and volume reconstruction with an estimation error of 1.92% when calibrated with an irregularly shaped stone. In medical indicator evaluation and analysis, the area below the receiver operating characteristic curve is greater than 0.8, indicating good predictability of the medical index used herein. The Youden index spotted the best cut-off point of volume, and the correlation between the volume's cut-off point and survival time was confirmed again by Kaplan-Meier and log rank test. The p-values were all less than 0.05, presenting a high degree of correlation between the two, indicating that this medical indicator is highly reliable.

CONCLUSIONS

The proposed techniques can automatically find the location of tumors in the lung, reconstruct the volume, and calculate changes in volume before and after treatment, thus obtaining an innovative survival prediction index. This will help facilitate early and accurate predictions of disease outcomes during the course of therapy, and categorize patient stratification into risk groups for more efficient therapies.

Automatic and quantitative measurement of laryngeal video stroboscopic images

The laryngeal video stroboscope is an important instrument for physicians to analyze abnormalities and diseases in the glottal area. Stroboscope has been widely used around the world. However, without quantized indices, physicians can only make subjective judgment on glottal images. We designed a new laser projection marking module and applied it onto the laryngeal video stroboscope to provide scale conversion reference parameters for glottal imaging and to convert the physiological parameters of glottis. Image processing technology was used to segment the important image regions of interest. Information of the glottis was quantified, and the vocal fold image segmentation system was completed to assist clinical diagnosis and increase accuracy. Regarding image processing, histogram equalization was used to enhance glottis image contrast. The center weighted median filters image noise while retaining the texture of the glottal image. Statistical threshold determination was used for automatic segmentation of a glottal image. As the glottis image contains saliva and light spots, which are classified as the noise of the image, noise was eliminated by erosion, expansion, disconnection, and closure techniques to highlight the vocal area. We also used image processing to automatically identify an image of vocal fold region in order to quantify information from the glottal image, such as glottal area, vocal fold perimeter, vocal fold length, glottal width, and vocal fold angle. The quantized glottis image database was created to assist physicians in diagnosing glottis diseases more objectively.

Three-dimensional reconstruction of trachea using computed tomography imaging as therapy for tracheal stenosis in infants

BACKGROUND AND OBJECTIVE

In this study, we aim to develop a system that uses computed tomography (CT) imaging for three-dimensional (3D) reconstruction of the trachea as therapy for tracheal stenosis in infants, and further calculate the cross-sectional area and volume, assisting doctors in clinical diagnosis.

METHODS

We first used image processing, calculating the cross-sectional area and volume. We used the improved median filter for image processing and designed the system for capturing the cross-sectional area of endotracheal tube. We then established 3D reconstruction images with isosurface extraction technology and calculated the cross-sectional area and volume. Medical indicator data analysis was performed.

RESULTS

The median filter developed in this study performed better in filtering speckle noise compared to traditional filtering methods. Furthermore, the median filter can keep fine texture feature, so that the subsequent contour selection and 3D reconstructed volume are more accurate. We also proposed new medical grading indexes according to tracheal obstruction volume ratio to assist doctors with the diagnosis and provide recommendations on treatment. Seventeen samples were examined in this study. Four sections of each sample are reviewed. Sixty-eight sections were used for validation, and the overall accuracy is very reliable.

CONCLUSIONS

Using image processing we obtained tracheal CT images before 3D reconstruction and calculated the cross-sectional area and volume of the trachea. New medical indicators are proposed according to the location and severity of stenosis to assist doctors with diagnosis.

Using image processing technology and mathematical algorithm in the automatic selection of vocal cord opening and closing images from the larynx endoscopy video

The human larynx is an important organ for voice production and respiratory mechanisms. The vocal cord is approximated for voice production and open for breathing. The videolaryngoscope is widely used for vocal cord examination. At present, physicians usually diagnose vocal cord diseases by manually selecting the image of the vocal cord opening to the largest extent (abduction), thus maximally exposing the vocal cord lesion. On the other hand, the severity of diseases such as vocal palsy, atrophic vocal cord is largely dependent on the vocal cord closing to the smallest extent (adduction). Therefore, diseases can be assessed by the image of the vocal cord opening to the largest extent, and the seriousness of breathy voice is closely correlated to the gap between vocal cords when closing to the smallest extent. The aim of the study was to design an automatic vocal cord image selection system to improve the conventional selection process by physicians and enhance diagnosis efficiency. Also, due to the unwanted fuzzy images resulting from examination process caused by human factors as well as the non-vocal cord images, texture analysis is added in this study to measure image entropy to establish a screening and elimination system to effectively enhance the accuracy of selecting the image of the vocal cord closing to the smallest extent.

Using image processing technology combined with decision tree algorithm in laryngeal video stroboscope automatic identification of common vocal fold diseases

This study used the actual laryngeal video stroboscope videos taken by physicians in clinical practice as the samples for experimental analysis. The samples were dynamic vocal fold videos. Image processing technology was used to automatically capture the image of the largest glottal area from the video to obtain the physiological data of the vocal folds. In this study, an automatic vocal fold disease identification system was designed, which can obtain the physiological parameters for normal vocal folds, vocal paralysis and vocal nodules from image processing according to the pathological features. The decision tree algorithm was used as the classifier of the vocal fold diseases. The identification rate was 92.6%, and the identification rate with an image recognition improvement processing procedure after classification can be improved to 98.7%. Hence, the proposed system has value in clinical practices.