Matching Process Research Articles

A survey of feature matching methods

AbstractFeature matching plays a crucial role in computer vision, with applications in visual localization, simultaneous localization and mapping (SLAM), image stitching, and more. It establishes correspondences between sets of feature points from multiple images, enabling various tasks. Over the years, feature matching has witnessed significant development, with an increasing number of methods being applied. However, different methods exhibit different degrees of applicability in different scenarios and requirements due to their different rationales. To cope with these issues, a comprehensive analysis and comparison of matching methods are essential. Existing reviews often lack coverage of deep learning models and focus more on feature detection and description, neglecting the matching process. This survey investigates feature detection, description, and matching techniques within the feature‐based image‐matching pipeline. Representative methods, their mechanisms, and application scenarios are also briefly introduced. In addition, comprehensive evaluations of classical and state‐of‐the‐art methods are conducted through extensive experiments on representative datasets. Particularly, matching‐based applications are compared to fully demonstrate the advantages of the methods. Lastly, this survey highlights current problems and development directions in matching methods, serving as a reference for researchers in the field.

CROWDMATCH: Optimizing Crowdsourcing Matching through the Integration of Matching Theory and Coalition Games

This paper tackles the challenges inherent in crowdsourcing dynamics by introducing the CROWDMATCH mechanism. Aimed at enabling crowdworkers to strategically select suitable crowdsourcers while contributing information to crowdsourcing tasks, CROWDMATCH considers incentives, information availability and cost, and the decisions of fellow crowdworkers to model the utility functions for both the crowdworkers and the crowdsourcers. Specifically, the paper presents an initial Approximate CROWDMATCH mechanism grounded in matching theory principles, eliminating externalities from crowdworkers’ decisions and enabling each entity to maximize its utility. Subsequently, the Accurate CROWDMATCH mechanism is introduced, which is initiated by the outcome of the Approximate CROWDMATCH mechanism, and coalition game-theoretic principles are employed to refine the matching process by accounting for externalities. The paper’s contributions include the introduction of the CROWDMATCH system model, the development of both Approximate and Accurate CROWDMATCH mechanisms, and a demonstration of their superior performance through comprehensive simulation results. The mechanisms’ scalability in large-scale crowdsourcing systems and operational advantages are highlighted, distinguishing them from existing methods and highlighting their efficacy in empowering crowdworkers in crowdsourcer selection.

Trends in the Main Residency Match From 2007 to 2020.

Background The United States Medical Licensing Exam (USMLE)Step 1 was recently changed from a numerically scored grading system to a pass/fail grading system. Until late 2024, there will be no formal studies about the impact that the grading change will have on the match process. To thoroughly assess the impact that this change will have on the overall match process, it is important to look at what the trends in applicants' objective measures have been in the years before the change. We aim to systematically evaluate the rates of change and mean trend of objective metrics found in residency applications in the main residency match. Methods Objective medical student data of matched and unmatched applicants were queried from the National Matching Program's Charting Outcomes in the Match Reports for the 2007 to 2020 application cycles. Data were used to create linear regression analyses and statistical tests were performed to evaluate trends over time. Results For matched applicants, there were statistically significant positive trends for the mean number of contiguous ranks (m=0.33, p<0.01), having another non-doctoral graduate degree (m=0.67, p<0.01), membership to Alpha Omega Alpha(AOA) honor society (m=0.22, p<0.01), mean USMLE Step 1 score (m=1.01, p<0.01), mean USMLE Step 2 score (m=1.68, p<0.01), mean number of research experiences (m=0.12, p<0.01), and mean number of abstracts, presentations, and publications(m=0.34, p<0.01). Additionally, there was a statistically significant negative trend for the percentage who graduated from a top 40 National Institutes of Health-funded medical school (m=-0.41, p<0.01). For unmatched applicants, there were statistically significant positive trends for having another non-doctoral graduate degree (m=0.83, p<0.01), mean USMLE Step 1 score (m=1.26, p<0.01), mean USMLE Step 2 score (m=2.27, p<0.01), mean number of research experiences (m=0.13, p<0.01), and mean number of abstracts, presentations, and publications (m=0.33, p<0.01). Conclusion Our study shows that there have been statistically significant increases in almost all objective measures in the residency application. Recent changes to the abstracts, presentations, and publications on the Step 1 scoring system will force almost all residency programs to overhaul their application process and potentially increase reliance on Step 2, research, and other nonobjective factors. For students early in their medical education, emphasis on Step 2 and research will yield increased chances of matching into residency in the future.

Evaluation of an automated matching system of children and families to virtual mental health resources during COVID-19

BackgroundChildren and their families often face obstacles in accessing mental health (MH) services. The purpose of this study was to develop and pilot test an electronic matching process to match children with virtual MH resources and increase access to treatment for children and their families during COVID-19.MethodsWithin a large observational child cohort, a random sample of 292 families with children ages 6–12 years were invited to participate. Latent profile analysis indicated five MH profiles using parent-reported symptom scores from validated depression, anxiety, hyperactivity, and inattention measures: (1) Average Symptoms, (2) Low Symptoms, (3) High Symptoms, (4) Internalizing, and (5) Externalizing. Children were matched with virtual MH resources according to their profile; parents received surveys at Time 1 (matching process explanation), Time 2 (match delivery) and Time 3 (resource uptake). Data on demographics, parent MH history, and process interest were collected.Results128/292 families (44%) completed surveys at Time 1, 80/128 families (63%) at Time 2, and a final 67/80 families (84%) at Time 3, yielding an overall uptake of 67/292 (23%). Families of European-descent and those with children assigned to the Low Symptoms profile were most likely to express interest in the process. No other factors were associated with continued interest or uptake of the electronic matching process. Most participating parents were satisfied with the process.ConclusionsThe electronic matching process delivered virtual MH resources to families in a time-efficient manner. Further research examining the effectiveness of electronically matched resources in improving children’s MH symptoms is needed.

A Novel Framework for Image Matching and Stitching for Moving Car Inspection under Illumination Challenges.

Vehicle exterior inspection is a critical operation for identifying defects and ensuring the overall safety and integrity of vehicles. Visual-based inspection of moving objects, such as vehicles within dynamic environments abounding with reflections, presents significant challenges, especially when time and accuracy are of paramount importance. Conventional exterior inspections of vehicles require substantial labor, which is both costly and prone to errors. Recent advancements in deep learning have reduced labor work by enabling the use of segmentation algorithms for defect detection and description based on simple RGB camera acquisitions. Nonetheless, these processes struggle with issues of image orientation leading to difficulties in accurately differentiating between detected defects. This results in numerous false positives and additional labor effort. Estimating image poses enables precise localization of vehicle damages within a unified 3D reference system, following initial detections in the 2D imagery. A primary challenge in this field is the extraction of distinctive features and the establishment of accurate correspondences between them, a task that typical image matching techniques struggle to address for highly reflective moving objects. In this study, we introduce an innovative end-to-end pipeline tailored for efficient image matching and stitching, specifically addressing the challenges posed by moving objects in static uncalibrated camera setups. Extracting features from moving objects with strong reflections presents significant difficulties, beyond the capabilities of current image matching algorithms. To tackle this, we introduce a novel filtering scheme that can be applied to every image matching process, provided that the input features are sufficient. A critical aspect of this module involves the exclusion of points located in the background, effectively distinguishing them from points that pertain to the vehicle itself. This is essential for accurate feature extraction and subsequent analysis. Finally, we generate a high-quality image mosaic by employing a series of sequential stereo-rectified pairs.

Outcomes of Short and Long Tibial Stems for Primary Total Knee Arthroplasty in a Population of Obese Patients at Two-Year Follow-Up: A Clinical and Biomechanical Study

BackgroundObesity can be a source of higher failure rates and inferior clinical outcomes after total knee arthroplasty (TKA). The aim of this study was to compare outcomes, failure rates, and stress distributions of TKA in obese patients using a short, long, or no tibial stem. MethodsA matching process based on the type of stem used and the age allowed included 180 patients who had a body mass index (BMI) > 30 and underwent a TKA between January 2010 and December 2019, with a minimum follow-up of 2 years. They were classified as moderately obese (MO: 30 < BMI < 35, N = 90) and severely obese (SO: BMI > 35, N = 90). For each, 3 subgroups were defined: thirty patients received a 30 mm short stem (SS), thirty received a 100 mm long stem (LS), and thirty received no stem (NS). Patients were assessed preoperatively and postoperatively using the Knee Society Score (KSS). A finite element model was developed to evaluate the biomechanical effects of the tibial stem on stress distribution in the subchondral bone based on BMI. ResultsThe SS patients had significantly higher postoperative KSS knee score [MO: 88.9 (SS) versus 79 (LS) versus 80.6 (NS); SO: 84.5 versus 72.4 versus 78.2] (P < .0001) and function score [MO: 90.4 (SS) versus 78.4 (LS) versus 68.5 (NS); SO: 85.5 versus 73 versus 61.8] (P < .0001) compared to LS and NS patients. The biomechanical study demonstrated a BMI-dependent increase in stress in the subchondral bone in contact with the tibial components. These stresses were mainly distributed at the tibial cut for NS and along the stem for SS and LS. ConclusionsA short, cemented tibial stem offers better functional outcomes without increasing failure rates compared to a longer stem during primary TKA in a population of obese patients at two-year follow-up. A short tibial stem does not lead to increased stress compared to an LS, at least for certain BMI categories.

High resolution imaging of acoustic orbital angular momentum wave based on orthogonal matching tracking

The orbital angular momentum (OAM) wave has shown great potential for improving radar imaging and underwater communication performance due to its helical wavefront phase and infinite orthogonal modes. However, there are currently no known applications of this technology in underwater imaging. In this paper, we employed acoustic OAM wave for underwater imaging and established transceiver signal models using the uniform circular array. We concurrently achieved two-dimensional imaging of azimuth and elevation angles, which differs from radar imaging. We proposed a matching process for the echo signal in the modal domain, the OAM wave beam image's sidelobe decreased by 7.9 dB in the elevation direction and 6.1 dB in the azimuth direction compared to the plane wave, with the mainlobe decreased by 0.2° in the elevation direction and 0.4° in the azimuth direction. Furthermore, this paper introduced OAM wave high-resolution image reconstruction based on the orthogonal matching pursuit (OMP) algorithm. Finally, we implemented broadband acoustic OAM wave for underwater imaging and introduced an image reconstruction method based on the modal domain OMP algorithm. Simulation results demonstrate that the use of OAM wave in underwater imaging is feasible, and the proposed scheme can achieve high-resolution imaging.

Sistem Pendukung Keputusan Pemilihan Guru Terbaik Berdasarkan Penilaian Kinerja Menggunakan Metode Profile Matching

Teachers are the initial milestone in the creation of a nation with quality young generation, both in the academic and non-academic fields. To determine or choose the best teacher, a decision support system for selecting the best teacher is made to appreciate the work performance that has been done while being a teaching staff, and is expected to increase motivation so that teachers can improve their performance. For this reason, a decision support system is needed that can analyze several teachers according to the existing teacher selection profile. The Profile Matching process is carried out to determine teacher recommendations in the Best Teacher Selection System based on 3 variables, namely intellectual aspects, work attitude aspects and general aspects. The result of the selection process is in the form of a teacher's final score as a recommendation for decision makers to choose the right teacher in the selection of the best teacher.

Radargrammetric 3D Imaging through Composite Registration Method Using Multi-Aspect Synthetic Aperture Radar Imagery

Interferometric synthetic aperture radar (InSAR) and tomographic SAR measurement techniques are commonly used for the three-dimensional (3D) reconstruction of complex areas, while the effectiveness of these methods relies on the interferometric coherence among SAR images with minimal angular disparities. Radargrammetry exploits stereo image matching to determine the spatial coordinates of corresponding points in two SAR images and acquire their 3D properties. The performance of the image matching process directly impacts the quality of the resulting digital surface model (DSM). However, the presence of speckle noise, along with dissimilar geometric and radiometric distortions, poses considerable challenges in achieving accurate stereo SAR image matching. To address these aforementioned challenges, this paper proposes a radargrammetric method based on the composite registration of multi-aspect SAR images. The proposed method combines coarse registration using scale invariant feature transform (SIFT) with precise registration using normalized cross-correlation (NCC) to achieve accurate registration between multi-aspect SAR images with large disparities. Furthermore, the multi-aspect 3D point clouds are merged using the proposed radargrammetric 3D imaging method, resulting in the 3D imaging of target scenes based on multi-aspect SAR images. For validation purposes, this paper presents a comprehensive 3D reconstruction of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) using Ka-band airborne SAR images. It does not necessitate prior knowledge of the target and is applicable to the detailed 3D imaging of large-scale areas with complex structures. In comparison to other SAR 3D imaging techniques, it reduces the requirements for orbit control and radar system parameters. To sum up, the proposed 3D imaging method with composite registration guarantees imaging efficiency, while enhancing the imaging accuracy of crucial areas with limited data.

Numerical impedance matching via extremum seeking control of single-frequency capacitively coupled plasmas

Abstract Impedance matching is a critical component of semiconductor plasma processing for minimizing the reflected power and maximizing the plasma absorption power. In this work, a more realistic plasma model is proposed that couples lumped element circuit, transmission line, and particle-in-cell (PIC) models, along with a modified gradient descent algorithm (GD), to study the impact of presets on the automatic matching process. The effectiveness of the proposed conceptual method is validated by using a single-frequency capacitively coupled plasma as an example. The optimization process with the electrode voltage and the reflection coefficient as the objective function and the optimized state, including plasma parameters, circuit waveforms, and voltage and current on transmission lines, is provided. These results show that the presets, such as initial conditions and objective functions, are closely related to the automatic matching process, resulting in different convergence speeds and optimization results, proving the existence of saddle points in the matching network parameter space. These findings provide valuable information for future experimental and numerical studies in this field.

Bridge to M4 (B2M4): An innovative residency application preparation curriculum.

The medical residency application process is daunting, time-consuming, and variable, making mentorship in preparation for this process important. In light of changes to the residency application process due to the COVID-19 pandemic, medical students identified a need to create a formal residency application preparation curriculum. The curriculum focused on reflection, while also furthering professional development and preparing for the nuances of the residency application cycle through structured advising. Bridge to M4 (B2M4) included reflective activities to help guide curriculum vitae (CV) refinement, personal and professional values clarification, residency program exploration, elevator pitch formation, personal statement development, mentor identification, guidance on requesting letters of recommendation, and virtual residency interview preparation. The B2M4 elective first took place in June 2020 with 36 rising fourth year students enrolled. Perceived effectiveness of the curriculum was assessed by having student cohorts evaluate the importance of the CV, personal statement, general preparation, and residency program selection before and after the course. Student feedback was reviewed with content analysis, which highlighted the usefulness of a structured residency preparation course that was rooted in reflection and incorporated faculty mentorship and feedback. This student-driven, innovative curricular design provided students the opportunity to prepare for the residency application process through reflective activities including values clarification, strengths identification, and goal setting. B2M4 demonstrates a process that can be used not only during the Residency Match process, but also throughout graduate medical education and future career decisions.

Multi-stage probabilistic linguistic matching method with the screening mechanism and individual preference relationship fusion

Considering the diversification of transaction information and complexity of matching environment, the original information with the language tendency plays an important role. Thus, under this circumstance, this paper gives a matching decision-making system under the probabilistic linguistic environment so as to improve the matching efficiency and quality. First, this paper demonstrates the multi-stage two-sided matching problem and illustrates the system frame. Then, for the multiple indicators, a probabilistic linguistic integrated cloud Bayesian network is constructed to present the dependency relationship, and determine the corresponding probability. It is known that the accuracy of agents’ preference information acts on the stability of final matching results, which may involve the strong ranking, agents’ psychological preferences and personal interests, etc. Thus, the improved ORESTE (organísation, rangement et Synthèse de données relarionnelles, in French) method is introduced to derive strong ranking and determine preference, indifference, and incomparability relation (PIR). Furthermore, this paper constructs the dynamic two-sided matching model considering the screening effect. Finally, a case study in second-hand house transaction is used to demonstrate the matching process. Simulation and comparison analysis validate its feasibility and effectiveness.

Clinical outcomes and patient-reported outcomes after oncoplastic breast surgery in breast cancer patients: A matched cohort study.

Surgery is the recommended treatment for breast cancer, the most common cancer in women in Taiwan and the leading cause of cancer-related deaths. Although breast-conserving surgery (BCS) has good prognosis, in some cases, BCS may cause more significant deformities and interfere with the patient's psychosocial well-being. Oncoplastic breast surgery (OBS) is the treatment option in these cases. This study aimed to determine the outcomes of OBS and BCS regardless of clinical and patient-reported esthetic outcomes. Between 2015 and 2020, 50 patients who underwent OBS at our hospital after complete treatment were enrolled. With 1:2 matched ratios, 100 patients were enrolled in the BCS control group. Clinical outcomes were analyzed. The BREAST-Q questionnaire was then assessed 6 months after the completion of treatment for subjective patient-reported outcomes. Due to the matching process, no difference was noted between the two groups in terms of demographic data such as age, comorbidities, or tumor characteristics. There were no significant differences in the local recurrence rate, disease-free survival, overall survival, positive margin rate, rewide excision rate, conversion to mastectomy rate, or complication rate (major or minor) between both groups. However, the OBS group showed higher satisfaction with breasts in the BREAST-Q questionnaire ( p < 0.001). The mean follow-up time was 38.77 ± 14.70 months in the BCS group and 29.59 ± 14.06 months in the OBS group. OBS seems to be a safe and feasible surgery in breast cancer patients because clinical outcomes are compatible with BCS. Moreover, the OBS group had better patient-reported outcomes in terms of satisfaction.

Breast ultrasound image despeckling using multi-filtering DFrFT and adaptive fast BM3D

Improving the quality of breast ultrasound images is of great significance for clinical diagnosis which can greatly boost the diagnostic accuracy of ultrasonography. However, due to the influence of ultrasound imaging principles and acquisition equipment, the collected ultrasound images naturally contain a large amount of speckle noise, which leads to a decrease in image quality and affects clinical diagnosis. To overcome this problem, we propose an improved denoising algorithm combining multi-filter DFrFT (Discrete Fractional Fourier Transform) and the adaptive fast BM3D (Block Matching and 3D collaborative filtering) method. Firstly, we provide the multi-filtering DFrFT method for preprocessing the original breast ultrasound image so as to remove some speckle noise early in fractional transformation domain. Based on the fractional frequency spectrum characteristics of breast ultrasound images, three types of filters are designed correspondingly in low, medium, and high frequency domains. And by integrating filtered images, the enhanced images are obtained which not only remove some speckle noise in background but also preserve the details of breast lesions. Secondly, for further enhancing the image quality on the basis of multi-filter DFrFT, we propose the adaptive fast BM3D method by introducing the DBSCAN-based super pixel segmentation to block matching process, which utilizes super pixel segmentation labels to provide a reference on how similar it is between target block and retrieval blocks. It reduces the number of blocks to be retrieved and make the matched blocks with more similar features. At last, the local noise parameter estimation is also adopted in the hard threshold filtering process of traditional BM3D algorithm to achieve local adaptive filtering and further improving the denoising effect. The synthetic data and real breast ultrasound data examples show that this combined method can improve the speckle suppression level and keep the fidelity of structure effectively without increasing time cost.

Loop closure detection of visual SLAM based on variational autoencoder.

Loop closure detection is an important module for simultaneous localization and mapping (SLAM). Correct detection of loops can reduce the cumulative drift in positioning. Because traditional detection methods rely on handicraft features, false positive detections can occur when the environment changes, resulting in incorrect estimates and an inability to obtain accurate maps. In this research paper, a loop closure detection method based on a variational autoencoder (VAE) is proposed. It is intended to be used as a feature extractor to extract image features through neural networks to replace the handicraft features used in traditional methods. This method extracts a low-dimensional vector as the representation of the image. At the same time, the attention mechanism is added to the network and constraints are added to improve the loss function for better image representation. In the back-end feature matching process, geometric checking is used to filter out the wrong matching for the false positive problem. Finally, through numerical experiments, the proposed method is demonstrated to have a better precision-recall curve than the traditional method of the bag-of-words model and other deep learning methods and is highly robust to environmental changes. In addition, experiments on datasets from three different scenarios also demonstrate that the method can be applied in real-world scenarios and that it has a good performance.

Thangka Image—Text Matching Based on Adaptive Pooling Layer and Improved Transformer

Image–text matching is a research hotspot in the multimodal task of integrating image and text processing. In order to solve the difficult problem of associating image and text data in the multimodal knowledge graph of Thangka, we propose an image and text matching method based on the Visual Semantic Embedding (VSE) model. The method introduces an adaptive pooling layer to improve the feature extraction capability of semantic associations between Thangka images and texts. We also improved the traditional Transformer architecture by combining bidirectional residual concatenation and mask attention mechanisms to improve the stability of the matching process and the ability to extract semantic information. In addition, we designed a multi-granularity tag alignment module that maps global and local features of images and text into a common coding space, leveraging inter- and intra-modal semantic associations to improve image and text accuracy. Comparative experiments on the Thangka dataset show that our method achieves significant improvements compared to the VSE baseline method. Specifically, our method improves the recall by 9.4% and 10.5% for image-matching text and text-matching images, respectively. Furthermore, without any large-scale corpus pre-training, our method outperforms all models without pre-training and outperforms two out of four pre-trained models on the Flickr30k public dataset. Also, the execution efficiency of our model is an order of magnitude higher than that of the pre-trained models, which highlights the superior performance and efficiency of our model in the image–text matching task.

SATCount: A scale-aware transformer-based class-agnostic counting framework

This paper studies the class-agnostic counting problem, which aims to count objects regardless of their class, and relies only on a limited number of exemplar objects. Existing methods usually extract visual features from query and exemplar images, compute similarity between them using convolution operations, and finally use this information to estimate object counts. However, these approaches often overlook the scale information of the exemplar objects, leading to lower counting accuracy for objects with multi-scale characteristics. Additionally, convolution operations are local linear matching processes that may result in a loss of semantic information, which can limit the performance of the counting algorithm. To address these issues, we devise a new scale-aware transformer-based feature fusion module that integrates visual and scale information of exemplar objects and models similarity between samples and queries using cross-attention. Finally, we propose an object counting algorithm based on a feature extraction backbone, a feature fusion module and a density map regression head, called SATCount. Our experiments on the FSC-147 and the CARPK demonstrate that our model outperforms the state-of-the-art methods.

The experiences and support needs of applicants who go unmatched to genetic counseling graduate programs: An exploratory qualitative study.

As interest in the profession of genetic counseling continues to grow, the number of applications received by accredited genetic counseling graduate programs far outweighs the number of available training positions. In the 2023 application cycle, 46% of applicants who participated in the genetic counseling match went unmatched. There is limited research surrounding the experiences of unmatched candidates. The aims of this qualitative study were to highlight the experience of not matching and reapplying and to identify support resources for the genetic counseling admissions process. We conducted 15 semi-structured interviews with individuals who had previously applied to genetic counseling graduate programs, not matched, and were in the process of reapplying. Through reflexive thematic analysis, five major themes were conceptualized: (1) Emotional navigation through the stages of the match process; (2) external perceptions surrounding going unmatched and disclosure of results; (3) experiencing stagnancy throughout applying and reapplying; (4) advances in knowledge and personal growth; (5) areas for increased supports and resources. This study has shed light on emotional and practical challenges faced by individuals who did not match with genetic counseling graduate programs and chose to reapply. The participants' experiences highlighted the need for enhanced support, especially in terms of emotional assistance and program feedback. They also underscored the importance of standardization and transparency in the application process. This research offers valuable insights for genetic counseling programs and organizations seeking to enhance the reapplicant experience and address the evolving needs of prospective candidates in the genetic counseling profession.

An Intelligent Medical Q&amp;A System Based on Natural Language Processing

Aim: To improve the accuracy of Chinese word splitting. Background: With the development of Internet technology, people want to get some effective medical information from the Internet, but there are still technical difficulties for non-specialists. At the same time, the level of medical construction can not keep up with the demand of patients for medical treatment, the phenomenon of doctor-patient conflicts has not been fundamentally solved, and the problem of difficult consultation prevails. With the arrival of the era of big data and artificial intelligence, medical Q&amp;A has been applied. Objective: In order to meet the user's need to get the correct answer as soon as possible, medical Q&amp;A needs to have high execution efficiency. The accuracy of Chinese participle directly affects the execution efficiency of Q&amp;A. Improving the accuracy of Chinese participle can fundamentally improve the accuracy of medical Q&amp;A and shorten the answering time. Methods: Improvement of the Chinese Segmentation Algorithm based on BI-LSTM-CRF using natural language processing technology. Based on the same medical Q&amp;A dataset, the medical Q&amp;A is trained and tested under three commonly used segmentation algorithms and the segmentation algorithm designed in this paper. Results: The experiments show that the Chinese Segmentation Algorithm studied in this paper improves the accuracy of medical Q&amp;A and can improve the execution efficiency of medical Q&amp;A. Conclusion: Based on the calculation and matching process of the same similar answers, different word-splitting methods directly affect the effect of medical Q&amp;A in the later stage. The better the effect of segmentation, the higher the accuracy of the correct answers in medical Q&amp;A. The improved LSTM-CRF split word accuracy designed in this paper achieves a good split word effect in the training process. Compared with the HMM segmentation algorithm, which has the best segmentation performance among the other three algorithms, the segmentation accuracy is improved, and the accuracy of the Q&amp;A that delivers the correct answers is relatively high. Despite the improved accuracy in segmenting the medical dataset, the time complexity did not decrease much. The LSTMCRF combined network segmentation algorithm designed in this paper performs better in medical Q&amp;A compared to other commonly used segmentation algorithms in terms of subject operating characteristics and larger regions surrounded by coordinate axes.

Comparison between direct and indirect "digital image" dental visual shade matching considering the effect of clinical experience and gender.

Shade matching is an essential procedure to obtain an esthetic prosthesis. The Direct Visual Shade Matching (DSM) method using shade guides is the most used method by dentists. However, The Indirect Visual Digital Shade Matching (ISM) method based on digital imaging is concerned to be reliable and useful in determining shade matching in dental practice. Several factors such as method, clinical experience, and gender can affect the success of shade matching. This study aimed to compare these two methods and evaluate the effect of experience and gender on the results. Three volunteers' maxillary central incisor teeth were evaluated using (DSM) and (ISM) methods by 87 examiners (42 males, 45 females). These examiners were further divided into three groups based on their clinical experience: Undergraduate Preclinical Dental Students (UPDS) with 31 examiners, Undergraduate Clinical Dental Students (UCDS) with 34 examiners, and Postgraduate Dental Students from the Department of Prosthodontics (PDS) with 22 examiners. The spectrophotometer device results of three teeth were considered as a reference for shade matching. Statistical analyses of the data were performed using Kruskal-Wallis, Mann-Whitney U, and Chi-square tests. No significant difference was found between indirect and direct methods according to Perfect Matches (p > 0.05). However, according to Shade Matching Deviation Score (SMDS) values a statistically significant difference was found between the two methods where the best result was obtained with the ISM method (p < 0.05). There was a significant difference between the PDS group and the other two groups in terms of clinical experience (p < 0.05). There was no significant difference between gender groups (p > 0.05). Digital images, captured under standardized lighting conditions, may serve as a reliable alternative method for direct shade matching. The (ISM) method requires a camera, related accessories, and computer skills. However, as these tools are commonly available nowadays, the focus for practitioners should be on effectively learning and applying these tools to achieve the best results. While clinical experience plays a significant role in the shade matching process, gender does not appear to have a substantial influence. This study found no significant differences between the (DSM) and (ISM) methods, leaving the clinical relevance of this method open to debate. However, the ISM method yielded superior responses. Despite requiring specialized tools, conditions, and training, the widespread use of cameras and computers in modern clinics makes the necessary equipment and software readily available. This method enhances clinician-technician communication, potentially improving prosthesis esthetics and patient satisfaction, especially for less experienced clinicians or those with color deficiencies. Further research is crucial to determine its clinical significance.