Manual Marking Research Articles

POSSIBILITY OF DETERMINING ARGUMENTATION IN SOCIAL SCIENCE ARTICLES: THE CASE OF LATVIAN

Argumentation is an essential part of the research process and its linguistic representation. Argumentation, as well as the formation of appropriate wording requires that the author is familiar with different types of argumentation and the experience with their use to substantiate one's thoughts and statements in a proper scientific way. Until now, argumentation as an object of linguistic research in scientific has been widely researched, but in the context of scientific articles in Latvian, it has not been studied at all.
 This paper explores the possibilities of qualitative analysis of argumentation in order to develop a methodology for determining the presence of argumentation and further linguistic research on it (for example, classification of argumentation by strategy, structure, scope or expansion, etc.).
 To achieve the intended goal, 20 social science articles (published from 2016–2018) in Latvian have been selected from the fields of political science, economics and business, education, and sociology. These articles were structurally different, 8 articles strictly adhered to IMRaD format, 6 articles partially followed it (mainly the discussion section was missing) while the remaining 6 articles completely ignored the IMRaD format. The selection of articles was done to accept or reject the initial hypothesis that the proportion of argumentation is directly related to the IMRaD structure of the articles.
 To determine the argumentation, the same research methods that are used in computational linguistics are applied to qualitative research and manual text zone marking is used. The study was intended to obtain and test a universal methodological approach that could be used for future linguistic research in disciplines (mainly humanities and arts) where the IMRaD structure is not used.

The use of excel spread sheet and printer in marking medical science objective questions

Background: Multiple-choice question (MCQ) is an effective form of written assessments for knowledge acquisition. Optical mark reading scanner is used for grading MCQs. Major setback in the use of scanner is the cost of acquiring it. Consequently, several manual methods such as conference making and stencil overlay are used to mark MCQ. This paper highlights the use of Microsoft Excel Spreadsheet and printer to mark MCQ and compares the method with other manual methods of marking. Materials and Methods: Five sets of 100 stems of objective questions (true or false, one in five, and matching types) were administered to students. Answer script was designed using Excel Spreadsheet. Three groups of marking were done: directly on the question paper, use of stencil for marking on the answer script, use of Microsoft Excel soft copy. At the end of the marking process, mean marking time, mean collation time, mean counting time, and mean marking error were collated and compared. The instant computer statistical software was used to calculate means, standard deviations, and ANOVA. A P < 0.05 was regarded as statistically significant. Results: Results showed a statistically significant low mean marking time of 0.68 h (P < 0.0001) for the spreadsheet/printer marking method. Marking error was significantly low with this method (P < 0.001) as compared with other manual marking methods. The collation time and counting time were not significantly different across the groups. Conclusion: It is concluded that Excel Spreadsheet and printer marking method significantly reduced marking time and marking errors in marking MCQs.

P02.01 CT Integrated Bronchoscopy Manual Spraying Pigment Labeling to Localize of Small Pulmonary Nodules

Popularization of High Resolution Computed Tomography (HRCT) has greatly improved the detection rate of pulmonary nodules. Video Assistant Thoracic Surgery (VATS) lobectomy (sublobar lung resection in selected patients) has been the standard surgical treatment for these early stage non-small cell lung cancer. Uniportal VATS, a widely used innovative technique in minimally invasive thoracic surgery, makes less incision and also demands more precise intraoperative palpation/localization which is often challenging for small nodules. Various strategies such as the hookwire method, CT fluoroscopy-guided bronchoscopic dye marking, contrast median injection, radiotracer localization and a virtual assisted lung mapping technology (VAL-MAP). While they provide new options of nodule location for thoracic surgeons, all of these strategies require expensive hardware or software and additional two or three more doctors which are often not affordable for centers with limited resources. The purpose of this study is to develop a practically cheap method of pigment labeling for bronchoscope with no additional need of medical equipment and medical human resources. This is an ethic committee approved clinical trial. Medically operable patients with the HRCT detected small nodules were eligible. This nodule localization method involved a manual drawing the route by the surgeon/bronchoscopist, through a series of bronchial opening sketches and marking the leading bronchus at every bifurcation point based on the HRCT. Two to four appropriate bronchial pathways were selected for each lesion. A metal-tip catheter was inserted into a selected bronchus and advanced to the pleura. The location of the catheter tip was fluoroscopically confirmed, and 1 mL of diluted methylene blue was injected for each marking. All these procedures were completed by one doctor and one nurse. Then low dose CT scan was used to visualize the localization of the multiple markings, which were used as references in the subsequent operation. Success rate and procedure related complication are reported. From April 2019 to July 2020, 18 lesions in 17 patients were enrolled. All surgical procedures were performed using uniportal VATS. The lesions ranged in size from 3 to 23mm, 15 (83%) were nodules of Ground Glass Opacity (GGO). The depth ranged from 0 to 19 mm. A total of 57 lung markings were planned. 54 of them (95%) were completed. Of the 3 failed markings, the target bronchus could not be accessed under the bronchoscopy because of improper angle. Fifty-two (91%) were visible during the operations. The 3 unidentified markings were in the same patient, who had been a coal miner and had a severe carbon deposition on his lung and pleura. There was no adverse event associated with this manual marking procedures. All lesions were successfully resected. In 3 patients failed multiple markings, alternative localization was used successfully. This HRCT guided manual drawing method of lesion localization is safe and effective for small nodule localization for lung cancer surgery. Results of this study appears to be better than conventional percutaneous techniques in clinically evident complications. Because it does not need special equipment, it is more suitable for developing countries with limited equipment and human resources.

HFOApp: A MATLAB Graphical User Interface for High-Frequency Oscillation Marking.

Epilepsy affects 3.4 million people in the United States, and, despite the availability of numerous antiepileptic drugs, 36% of patients have uncontrollable seizures, which severely impact quality of life. High-frequency oscillations (HFOs) are a potential biomarker of epileptogenic tissue that could be useful in surgical planning. As a result, research into the efficacy of HFOs as a clinical tool has increased over the last 2 decades. However, detection and identification of these transient rhythms in intracranial electroencephalographic recordings remain time-consuming and challenging. Although automated detection algorithms have been developed, their results are widely inconsistent, reducing reliability. Thus, manual marking of HFOs remains the gold standard, and manual review of automated results is required. However, manual marking and review are time consuming and can still produce variable results because of their subjective nature and the limitations in functionality of existing open-source software. Our goal was to develop a new software with broad application that improves on existing open-source HFO detection applications in usability, speed, and accuracy. Here, we present HFOApp: a free, open-source, easy-to-use MATLAB-based graphical user interface for HFO marking. This toolbox offers a high degree of intuitive and ergonomic usability and integrates interactive automation-assist options with manual marking, significantly reducing the time needed for review and manual marking of recordings, while increasing inter-rater reliability. The toolbox also features simultaneous multichannel detection and marking. HFOApp was designed as an easy-to-use toolbox for clinicians and researchers to quickly and accurately mark, quantify, and characterize HFOs within electrophysiological datasets.

A Classification Method for Network Traffic Based on Semi-supervised Approach

Traffic data encryption has been a trend in most Internet applications, and traditional protocol filtering based on fixed port and traffic classification based on Deep Packet Inspection are unable to Identify encrypted traffic. Recently, the traffic classification method based on the statistical characteristics of network traffic, which can solve the problem of encrypting data or user privacy protection, has been widely discussed. However, the traditional supervised learning method requires manual marking of a large amount of network traffic data, which is tedious and time-consuming. In this paper, a improved semi-supervised traffic classification framework based on BIRCH clustering method is proposed, and through experiments, the proposed algorithm, supervised learning algorithm and classical semi-supervised traffic classification algorithm are analyzed and compared. The results show that the algorithm proposed in this paper has higher overall accuracy and classification accuracy, and the algorithm can increase the accuracy on traffic classification.

Stereo Vision-Based Relative Position and Attitude Estimation of Non-Cooperative Spacecraft

In on-orbit services, the relative position and attitude estimation of non-cooperative spacecraft has become the key issues to be solved in many space missions. Because of the lack of prior knowledge about manual marks and the inability to communicate between non-cooperative space targets, the relative position and attitude estimation system poses great challenges in terms of accuracy, intelligence, and power consumptions. To address these issues, this study uses a stereo camera to extract the feature points of a non-cooperative spacecraft. Then, the 3D position of the feature points is calculated according to the camera model to estimate the relationship. The optical flow method is also used to obtain the geometric constraint information between frames. In addition, an extended Kalman filter is used to update the measurement results and obtain more accurate pose optimization results. Moreover, we present a closed-loop simulation system, in which the Unity simulation engine is employed to simulate the relative motion of the spacecraft and binocular vision images, and a JetsonTX2 supercomputer is involved to conduct the proposed autonomous relative navigation algorithm. The simulation results show that our approach can estimate the non-cooperative target’s relative pose accurately.

A semiautomated measurement of muscle fiber size using the Imaris software.

The size and shape of skeletal muscle fibers are affected by various physiological and pathological conditions, such as muscle atrophy, hypertrophy, regeneration, and dystrophies. Hence, muscle fiber cross-sectional area (CSA) is an important determinant of muscle health and plasticity. We adapted the Imaris software to automatically segment muscle fibers based on fluorescent labeling of the plasma membrane and measure muscle fiber CSA. Analysis of muscle cross sections by the Imaris semiautomated and manual approaches demonstrated a similar decrease in CSA of atrophying muscles from fasted mice compared with fed controls. In addition, we previously demonstrated that downregulation of the Ca2+-specific protease calpain-1 attenuates muscle atrophy. Accordingly, both the Imaris semiautomated and manual approaches showed a similar increase in CSA of fibers expressing calpain-1 shRNA compared with adjacent nontransfected fibers in the same muscle cross section. Although both approaches seem valid for measurements of muscle fiber size, the manual marking method is less preferable because it is highly time-consuming, subjective, and limits the number of cells that can be analyzed. The Imaris semiautomated approach is user-friendly, requires little training or optimization, and can be used to efficiently and accurately mark thousands of fibers in a short period. As a novel addition to the commonly used statistics, we also describe statistical tests that quantify the strength of an effect on fiber size, enabling detection of significant differences between skewed distributions that would otherwise not be detected using typical methods.

Fast Segmentation Algorithm for Cystoid Macular Edema Based on Omnidirectional Wave Operator

Optical coherence tomography (OCT) is widely used in the field of ophthalmic imaging. The existing technology cannot automatically extract the contour of the OCT images of cystoid macular edema (CME) and can only evaluate the degree of lesions by detecting the thickness of the retina. To solve this problem, this paper proposes an automatic segmentation algorithm that can segment the CME in OCT images of the fundus quickly and accurately. This method firstly constructs the working environment by denoising and contrast stretching, secondly extracts the region of interest (ROI) containing CME according to the average gray distribution of the image, and then uses the omnidirectional wave operator to perform multidirectional automatic segmentation. Finally, the fused segmentation results are screened by gray threshold and position feature, and the contour extraction of CME is realized. The segmentation results of the proposed method on data set images are compared with those obtained by manual marking of experts. The accuracy, recall, Dice index, and F1-score are 88.8%, 75.0%, 81.1%, and 81.3%, respectively, with the average process time being 1.2 s. This algorithm is suitable for general CME image segmentation and has high robustness and segmentation accuracy.

Development significance and structure analysis of sports video image analysis system based on VR

Modern science and technology is highly differentiated and concentrated, and has also entered a new stage of multidisciplinary comprehensive utilization. Especially, VR technology has played a great role in competitive sports training. Intelligent video analysis system is a key point of modern video development. With the development of computer technology and the gradual progress of video technology, intelligent video analysis system has broad development space. In this paper, a key algorithm SISA (Sports Image Segmentation Algorithm) is designed. The algorithm improves and optimizes the semantic segmentation algorithm by using hole convolution, pyramid pooling and trainable conditional random field structure, and uses key node detection algorithm to enhance the segmentation effect of small objects. The common methods to realize virtual human animation are manual marking, motion capture and automatic recognition. In order to reduce the workload of manual marking, avoid the high cost of motion capture and make up for the precision error of automatic recognition, it can be practically applied to the guidance of physical training.

PMD3 The Current Burden of Cataract Surgery and Outcomes of Image-Guided Systems: A Targeted Literature Review

To conduct a targeted literature review evaluating the current burden of traditional cataract surgery (with manual marking) and the outcomes associated with digital image-guided systems (DIGS). A targeted review of the literature was undertaken using MEDLINE. The review targeted: 1) publications quantifying the burden of traditional cataract surgery (with manual marking) for Toric IOL implantation, and 2) publications evaluating the outcomes of cataract surgery with DIGS. There were seven studies included in this study, six prospective studies and one meta-analysis. Transcription errors, manual marking inaccuracies, and variabilities from incision size and implantation location were the biggest drivers of burden of traditional cataract surgeries. Transcription errors included incorrect measurement transcription, errors in handwritten results, and illegible writing. Measurement inaccuracies were found to be attributable to movement by the patient, marking pens, and experience of the marker. Additionally, difficulties in accounting for cyclotorsion when making corneal incision and aligning the IOL were observed. Use of DIGS was found to be safe, as or more effective, and more efficient alternative to manual marking. Of the five head-to-head prospective studies, three studies observed significant improvement in accuracy, IOL alignment, and/or visual quality (Strehl Ratio and Modulation Transfer Function). The DIGS significantly reduced Toric IOL misalignment, post-operative astigmatism, and difference vector compared to manual marking in the meta-analysis. This review found a significant burden associated with traditional cataract surgery, and DIGSs to be safe and effective, while also providing surgeons and practices with increased efficiency.

3D free-hand ultrasound to register anatomical landmarks at the pelvis and localize the hip joint center in lean and obese individuals

3D free-hand ultrasound (3DFUS) is becoming increasingly popular to assist clinical gait analysis because it is cost- and time-efficient and does not expose participants to radiation. The aim of this study was to evaluate its reliability in localizing the anterior superior iliac spine (ASIS) at the pelvis and the hip joint centers (HJC). Additionally, we evaluated its accuracy to get a rough estimation of the potential to use of 3DFUS to segment bony surface. This could offer potential to register medical images to motion capture data in future. To evaluate reliability, a test–retest study was conducted in 16 lean and 19 obese individuals. The locations of the ASIS were determined by manual marker placement (MMP), an instrumented pointer technique (IPT), and with 3DFUS. The HJC location was also determined with 3DFUS. To quantify reliability, intraclass correlation coefficients (ICCs), the standard error of measurement (SEm), among other statistical parameters, were calculated for the identified locations between the test and retest. To assess accuracy, the surface of a human plastic pelvic phantom was segmented with 3DFUS in a distilled water bath in 27 trials and compared to a 3D laser scan of the pelvis. Regarding reliability, the MMP, but especially the IPT showed high reliability in lean (SEm: 2–3 mm) and reduced reliability in obese individuals (SEm: 6–15 mm). Compared to MMP and IPT, 3DFUS presented lower reliability in the lean group (SEm: 2–4 mm vs. 2–8 mm, respectively) but slightly better values in the obese group (SEm: 7–11 mm vs. 6–16 mm, respectively). Correlations between test–retest reliability and torso body fat mass (% of body mass) indicated a moderate to strong relationship for MMP and IPT but only a weak correlation for the 3DFUS approach. The water-bath experiments indicated an acceptable level of 3.5 (1.7) mm of accuracy for 3DFUS in segmenting bone surface. Despite some difficulties with single trials, our data give further rise to the idea that 3DFUS could serve as a promising tool in future to inform marker placement and hip joint center location, especially in groups with higher amount of body fat.

A hybrid attention temporal sequential network for sleep stage classification

Sleep stage classification is a necessary fundamental method for the diagnosis of sleep diseases, which has attracted extensive attention in recent years. Traditional methods for sleep stage classification, such as manual marking methods and machine learning algorithms, have the limitations of low efficiency and defective generalization. Recently, deep neural networks have shown improved results by the capability of learning complex pattern in the sleep data. However, these models ignore the intra-temporal sequential information and the correlation among all channels in each segment of the sleep data. To solve these problems, a hybrid attention temporal sequential network model is proposed in this paper, choosing recurrent neural network to replace traditional convolutional neural network, and extracting temporal features of polysomnography from the perspective of time. Furthermore, intra-temporal attention mechanism and channel attention mechanism are adopted to achieve the fusion of the intra-temporal representation and the fusion of channel-correlated representation. And then, based on recurrent neural network and inter-temporal attention mechanism, this model further realized the fusion of inter-temporal contextual representation. Finally, the end-to-end automatic sleep stage classification is accomplished according to the above hybrid representation. This paper evaluates the proposed model based on two public benchmark sleep datasets downloaded from open-source website, which include a number of polysomnography. Experimental results show that the proposed model could achieve better performance compared with ten state-of-the-art baselines. The overall accuracy of sleep stage classification could reach 0.801, 0.801 and 0.717, respectively. Meanwhile, the macro average F1-scores of the proposed model could reach 0.752, 0.728 and 0.700. All experimental results could demonstrate the effectiveness of the proposed model.

Computer-Aided Intraoperative Toric Intraocular Lens Positioning and Alignment During Cataract Surgery.

Cataract causes more than half of all blindness worldwide. The most effective treatment is surgery, where cataract is often replaced by intraocular lens (IOL). Beyond saving vision, toric IOL implantation is becoming increasingly popular to correct corneal astigmatism. It is important to precisely position and align the axis of IOL during surgery to achieve optimal post-operative astigmatism correction. Comparing with conventional manual marking, automated markerless IOL alignment can be faster, more accurate and non-invasive. Here we propose a framework for computer-assisted intraoperative IOL positioning and alignment based on detection and tracking. Firstly, the iris boundary was segmented and the eye center was determined. A statistical sampling method was developed to segment iris and generate training labels, and both conventional algorithms and deep convolutional neural network (CNN) methods were evaluated. Then, regions of interests (ROIs) containing high density of scleral capillaries were used for tracking eye rotations. Both correlation filter and CNN methods were evaluated for tracking. Cumulative errors during long-term tracking were corrected using a reference image. Validation studies against manual labeling using 7 clinical cataract surgical videos demonstrated that the proposed algorithm achieved an average position error around 0.2 mm, an axis alignment error of < 1 °, and a frame rate of > 25 FPS, and can be potentially used intraoperatively for markerless IOL positioning and alignment during cataract surgery.

Three-Random-Point Marking Method for Toric Intraocular Lens Alignment Using the iTrace Aberrometer

Purpose To evaluate the clinical outcome of the three-random-point (TRP) marking method for toric intraocular lens (IOL) alignment using the iTrace aberrometer (Tracey Technologies Corp., Houston, TX). Setting. Department of Ophthalmology, Guangdong Eye Institute, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China. Design Prospective, randomized comparative trial. Method Thirty eyes of 30 patients undergoing cataract surgery with coexisting corneal astigmatism of over 1.0 D were included in this study. All patients were prospectively randomized into the TRP marking group or slit-lamp horizontal meridian (SHM) marking group. TRP marking involved marking three points randomly in the corneal limbus of the patients and accurately marking the horizontal meridian was not required. The follow-up duration was 3 months after cataract surgery. Results Fifteen eyes of 15 patients were in the TRP marking group and 15 eyes of 15 patients in the SHM marking group. There was no statistically significant difference in BCVA, UDVA, preexisting corneal astigmatism, or residual astigmatism between the groups before or after surgery (P > 0.05). The mean toric IOL misalignment was lesser but without significance in the TRP marking group than in the SHM marking group after 3 postoperative months (2.66° ± 1.42° versus 3.29° ± 1.67°; P=0.295). Conclusion The TRP marking method using the iTrace aberrometer is simple and accurate for preoperative marking of toric IOLs. It can eliminate the potential systematic errors resulting from varying head positions during the preoperative keratometry measurement and from manual marking.

A sliding window method to identify defects in 3D printing lattice structure based on the difference principle

Structural defects are inevitably produced by residual stress in the 3D printing process, which reduce the structural-functional properties. For a typical 3D printing lattice structure, a new sliding window approach to intelligently identify defects, based on the difference principle, in the inspection process was proposed. Industrial computed tomography (CT) was used for image information acquisition. The grayscale standard deviation in the local range of the CT slice is calculated to characterize the grayscale variation of the defect location. On this basis, a sliding window method is proposed for traversing CT slices. Three different sizes of window and the corresponding calculation methods were established, and then the appropriate window was selected through comparison. After differential processing is performed on the traversed image to extract the defect feature, automatic defect recognition rules are created. The experimental results show that the recognition rate of the proposed method is 98.5% for typical internal defects of metal 3D lattice structures and the validity of the method is verified. Compared with a manual marking method, this method can effectively improve the efficiency of defect detection.

Automatic detection of optic disc using distance regularized level-set segmentation for glaucoma screening system

PurposeThe abnormalities of glaucoma have high impact on deciding and representing the causes that effects severity of blindness in human beings. The simulation experimental results would help the ophthalmologist in diagnosing of glaucoma abnormality accurately. The significant effect of glaucoma has a huge impact on the quality of human life, and its growth rate in world population tremendously increases. Glaucoma is considered as second largest cause for the blindness in the world; hence identification of it marks the importance of its detection at the earliest.Design/methodology/approachThe prime objective of the work proposed is to build up a human intervention free image preparing framework for glaucoma screening. The disc calculation is assessed on retinal image dataset called retinal Image for glaucoma Analysis. The proposed method briefs a novel optic disc division calculation depending on applying a level-set strategy on a confined optic disc image. In the instance of low quality image, a twofold level set is designed, in which the principal level set is viewed as restriction for the optic disc. To keep the veins from meddling with the level-set procedure, an inpainting strategy has been applied. Also a significant commitment is to include the varieties in notion adopted by the ophthalmologists in distinguishing the disc localization and diagnosing the glaucoma. Most of the past investigations are prepared and tested depending on just a single feature, which can be thought to be one-sided for the ophthalmologist.FindingsIn continuation, the correctness has been determined depending on the quantity of image that matched with the investigation pattern adopted by the ophthalmologist. The 175 retinal images were utilized to test the results of proposed work with the manual markings of ophthalmologists. The error-free calculation in marking the optic disc region and centroid was 98.95% in comparison with the existing result of 87.34%.Originality/valueIn continuation, the correctness has been determined depending on the quantity of image that matched with the investigation pattern adopted by the ophthalmologist. The 175 retinal images were utilized to test the results of proposed work with the manual markings of ophthalmologists. The error-free calculation in marking the optic disc region and centroid was 98.95% in comparison with the existing result of 87.34%.

ManoMap: an automated system for characterization of colonic propagating contractions recorded by high-resolution manometry.

Colonic high-resolution manometry (cHRM) is an emerging clinical tool for defining colonic function in health and disease. Current analysis methods are conducted manually, thus being inefficient and open to interpretation bias. The main objective of the study was to build an automated system to identify propagating contractions and compare the performance to manual marking analysis. cHRM recordings were performed on 5 healthy subjects, 3 subjects with diarrhea-predominant irritable bowel syndrome, and 3 subjects with slow transit constipation. Two experts manually identified propagating contractions, from five randomly selected 10-min segments from each of the 11 subjects (72 channels per dataset, total duration 550 min). An automated signal processing and detection platform was developed to compare its effectiveness to manually identified propagating contractions. In the algorithm, individual pressure events over a threshold were identified and were then grouped into a propagating contraction. The detection platform allowed user-selectable thresholds, and a range of pressure thresholds was evaluated (2 to 20 mmHg). The automated system was found to be reliable and accurate for analyzing cHRM with a threshold of 15 mmHg, resulting in a positive predictive value of 75%. For 5-h cHRM recordings, the automated method takes 22 ± 2 s for analysis, while manual identification would take many hours. An automated framework was developed to filter, detect, quantify, and visualize propagating contractions in cHRM recordings in an efficient manner that is reliable and consistent.

Smart attendance using deep learning and computer vision

Attendance is an essential part of daily classroom evaluation. Traditional classroom follows a manual attendance marking system, i.e., calling a student's names or by forwarding an attendance sheet. This process is both time-consuming and error-prone, i.e., student proxy, etc. Hence a face recognition based smart classroom attendance management system using computer vision and deep learning implemented on a Raspberry Pi has been proposed. It has been proposed to mount a camera at the top of the blackboard so that the students are visible while they are sitting down. A face detection algorithm followed by face recognition has been used to mark the attendance of the detected student.

Food Image Recognition and Food Safety Detection Method Based on Deep Learning.

With the development of machine learning, as a branch of machine learning, deep learning has been applied in many fields such as image recognition, image segmentation, video segmentation, and so on. In recent years, deep learning has also been gradually applied to food recognition. However, in the field of food recognition, the degree of complexity is high, the situation is complex, and the accuracy and speed of recognition are worrying. This paper tries to solve the above problems and proposes a food image recognition method based on neural network. Combining Tiny-YOLO and twin network, this method proposes a two-stage learning mode of YOLO-SIMM and designs two versions of YOLO-SiamV1 and YOLO-SiamV2. Through experiments, this method has a general recognition accuracy. However, there is no need for manual marking, and it has a good development prospect in practical popularization and application. In addition, a method for foreign body detection and recognition in food is proposed. This method can effectively separate foreign body from food by threshold segmentation technology. Experimental results show that this method can effectively distinguish desiccant from foreign matter and achieve the desired effect.

Comparison of the Orientation of the Corneal Steep Meridian Determined by Image-Guided System and Manual Method in the Same Eye.

PurposeTo evaluate the difference between the preoperative marking methods for toric intraocular lens (IOL) implantations using an image-guided system (IGS) and the manual marking method in the same eye.Patients and MethodsIn this retrospective case series, 82 patients (101 eyes) who underwent cataract surgery using both manual and IGS (VERION, Alcon Laboratories) marking were enrolled. First, preoperative reference marks were placed at 6 o’clock and 3 or 9 o’clock position under slit-lamp biomicroscope in the outpatient department using the manual method. Using the reference unit of IGS, the ocular surface data were captured and overlaid. The difference was then measured (preoperative axis misalignment). In the operating room, the orientation of the steep meridian of the manual method was determined based on this reference mark under the surgical microscope. Just before surgery, the digital degree gauge of IGS was overlaid on the ocular surface, and the difference was then measured (total axis misalignment). We calculated the intraoperative axis misalignment by subtracting preoperative axis misalignment from the total axis misalignment.ResultsMean absolute preoperative, intraoperative, and total axis misalignment values were 3.87±3.95 degrees, 5.46±4.42 degrees, and 4.98±4.49 degrees, respectively. In preoperative, intraoperative, and total misalignment, the ratios of 10 degrees or greater were 10 (14.7%), 12 (17.6%), and 20 (19.8%) eyes, respectively.ConclusionThe manual method that determines the fixed position of the toric intraocular lens (IOL) may cause large misalignment compared with the IGS, suggesting that using manual method could sometimes result in a large misalignment of toric IOL implantation.