Minimum clinically important difference and substantial clinical benefit in patients with chronic temporomandibular disorders

BACKGROUND

Research on temporomandibular disorder (TMD) responsiveness is scarce and limited regarding patients' representativeness.

OBJECTIVE(S)

This study aimed to estimate minimum clinically important difference (MCID) and substantial clinical benefit (SCB) among a large and diverse patient population regarding sex and age.

METHODS

In this study, 162 patients participated from five hospitals. MCID and SCB in pain, functional disability and quality of life were examined with anchor-based methods. Patients' global impression of change was used as the anchor. Area under the curve (AUC) values were determined for testing accuracy. Changes from baseline and coefficient of variation by responsiveness status were calculated to explain the results of accuracy.

RESULTS

SCB was estimated to be 2.18 for the numeric rating scale (NRS) for pain (AUC: 0.80 [95% CI: 0.72-0.88]) in all patients and 2.50 in women (AUC: 0.81 [95% CI: 0.71-0.89]). The estimated SCB of NRS for discomfort (1.50) and Jaw Functional Limitation Scale for mastication (1.35) had wide CIs for AUCs. Likewise, the estimated MCIDs of NRS for pain (0.80) and NRS for discomfort (1.50) had wide CIs for AUCs. Among non-responders who did not achieve the MCID of NRS for pain, the coefficient of variation was very high for all outcomes other than the NRS for pain.

CONCLUSION

This study investigated the responsiveness of patients with TMD using a large and diverse patient sample. SCB in pain decrease can be used to assess the responsiveness of patients with TMD. Composite outcomes should be developed to estimate MCID.

Gate-bias stability of triple-gated feedback field-effect transistors with silicon nanosheet channels

In this study, we investigate the gate-bias stability of triple-gated feedback field-effect transistors (FBFETs) with Si nanosheet channels. The subthreshold swing (SS) of FBFETs increases from 0.3 mV dec-1to 60 and 80 mV dec-1inp- andn-channel modes, respectively, when a positive bias stress (PBS) is applied for 1000 s. In contrast, the SS value does not change even after a negative bias stress (NBS) is applied for 1000 s. The difference in the switching characteristics under PBS and NBS is attributed to the ability of the interface traps to readily gain electrons from the inversion layer. The switching characteristics deteriorated by PBS are completely recovered after annealing at 300 °C for 10 min, and the characteristics remain stable even after PBS is applied again for 1000 s.

Generation and Storage of Random Voltage Values via Ring Oscillators Comprising Feedback Field-Effect Transistors

In this study, we demonstrate the generation and storage of random voltage values using a ring oscillator consisting of feedback field-effect transistors (FBFETs). This innovative approach utilizes the logic-in-memory function of FBFETs to extract continuous output voltages from oscillatory cycles. The ring oscillator exhibited uniform probability distributions of 51.6% for logic 0 and 48.4% for logic 1. The generation of analog voltages provides binary random variables that are stored for over 5000 s. This demonstrates the potential of the ring oscillator in advanced physical functions and true random number generator technologies.

Binary and ternary logic-in-memory using nanosheet feedback field-effect transistors with triple-gated structure

In this study, we demonstrate binary and ternary logic-in-memory (LIM) operations of inverters and NAND and NOR gates comprising nanosheet (NS) feedback field-effect transistors (FBFETs) with a triple-gated structure. The NS FBFETs are reconfigured in p- or n-channel modes depending on the polarity of the gate bias voltage and exhibit steep switching characteristics with an extremely low subthreshold swing of 1.08 mV dec-1 and a high ON/OFF current ratio of approximately 107. Logic circuits consisting of NS FBFETs perform binary and ternary logic operations of the inverters and NAND and NOR gates in each circuit and store their outputs under zero-bias conditions. Therefore, NS FBFETs are promising components for next-generation LIM.

Development of Novel Focal Irradiation Tool for High-Precision Irradiation Using Clinical Brachytherapy System

PURPOSE/OBJECTIVE(S)

Several small animals, including mice, are used to conduct research on state-of-the-art radiation therapy techniques or treatment-related toxicity. However, it is difficult to conduct the focal irradiation to a shallow depth on small animals, because irradiation using LINAC has limitations in energy and field size. The purpose of this paper was to develop a focal irradiation tool for high-precision irradiation and to evaluate beam characteristics.

MATERIALS/METHODS

We designed the collimator of 1 mm diameter consisting of tungsten material for high-precision irradiation applied to the clinical brachytherapy system and the percent depth dose and horizontal profile were measured. We compared the depth dose and horizontal profile with 4 mm diameter SRS cone for 6 MV in LINAC. We measured the PDD and horizontal profile using EBT3 film for high-precision irradiation of 1 mm diameter using Ir-192 source. In case of 4 mm diameter, the beam was measured using edge detector. In addition, all measurements were compared with the results of planning tool simulation.

RESULTS

In case of focal irradiation tool, the maximum dose showed at the surface for both measurement and simulation, and 26% and 32% doses at 1 mm depth, respectively. In addition, FWHM at a 1 mm depth showed that high-precision irradiation was possible with measurement and simulation results of 1.86 and 1.28 mm. In case of LINAC, the maximum dose was showed at a depth of 1 cm and 0.8 cm in the measurement and simulation, respectively. Even if the smallest cone is used, the FWHM at a dmax depth was 4.0 mm in both simulation and measurement.

CONCLUSION

We overcame the limitation for energy and field size through the focal irradiation tool for high-precision irradiation. The focal irradiation tool enables high dose delivery to the shallow depth. In addition, small FWHM reduced dose delivery to the periphery at a specific depth and enabled accurate dose delivery. These results mean that the focal irradiation tool can be useful in small animal experiments that require accurate doses near the shallow depth.

A Photograph-Based Visualization and Prediction Framework for Radiation-Induced Dermatitis

PURPOSE/OBJECTIVE(S)

This study aimed to suggest a photograph-based prediction system for acute radiation-induced dermatitis (RID), which can be applied to notify patients about the risk of the development of skin discomfort during radiotherapy.

MATERIALS/METHODS

The proposed system compared the spatial dose distribution with the RID region using the following methods. Skin photographs of patients were taken using an RGB-depth camera to acquire the shape information of RID. The skin surface data measured from the camera was registered with the shape of the external body contour using an iterative closest point algorithm. Spatial dose distribution of skin was extracted from the external body contour to a depth of 2 mm and projected onto the plane of the skin photograph using a transformation matrix for skin depth data. To compare the spatial distribution of skin dose with the shape of RID, the region of RID in patients' skin was delineated on photographs into three toxicity symptoms referring to the CTCAE criteria grade 1 (skin redness), grade 2 (dry desquamation), and grade 3 (moist desquamation). The degree of overlap between the shape of each RID and skin dose distribution was evaluated using the dice similarity coefficient (DSC). Threshold doses for predicting RID occurrence were estimated by skin isodose lines with the highest DSC. The developed system was validated using data from 19 patients who received volumetric modulated arc therapy for head-neck cancer at a single institution.

RESULTS

Threshold doses for RID grades 1, 2, and 3 were estimated using 18, 18, and 2 individual RID labels delineated on skin photographs, respectively. Isodose lines with the highest DSC for RID grades 1, 2, and 3 were calculated as 26.0 Gy, 36.5 Gy, and 54.0 Gy, respectively. A strong overlap (average DSC > 0.6) was observed between isodose skin lines and the shape of RID labels in all RID grades.

CONCLUSION

Assessing the spatial information of skin dose can be helpful in predicting acute RID. The region of RID shows a strong similarity with the skin dose distribution in head-neck patients. Visualization of skin dose on the patient photograph is potent to patient education for preparing the cosmetic discomfort during radiotherapy, which may lead to the improvement of the patient satisfaction in treatment.

An interpretable and interactive deep learning algorithm for a clinically applicable retinal fundus diagnosis system by modelling finding-disease relationship

The identification of abnormal findings manifested in retinal fundus images and diagnosis of ophthalmic diseases are essential to the management of potentially vision-threatening eye conditions. Recently, deep learning-based computer-aided diagnosis systems (CADs) have demonstrated their potential to reduce reading time and discrepancy amongst readers. However, the obscure reasoning of deep neural networks (DNNs) has been the leading cause to reluctance in its clinical use as CAD systems. Here, we present a novel architectural and algorithmic design of DNNs to comprehensively identify 15 abnormal retinal findings and diagnose 8 major ophthalmic diseases from macula-centered fundus images with the accuracy comparable to experts. We then define a notion of counterfactual attribution ratio (CAR) which luminates the system's diagnostic reasoning, representing how each abnormal finding contributed to its diagnostic prediction. By using CAR, we show that both quantitative and qualitative interpretation and interactive adjustment of the CAD result can be achieved. A comparison of the model's CAR with experts' finding-disease diagnosis correlation confirms that the proposed model identifies the relationship between findings and diseases similarly as ophthalmologists do.

A New Approach to Quantify and Grade Radiation Dermatitis Using Deep-Learning Segmentation in Skin Photographs

AIMS

Objective evaluation of radiation dermatitis is important for analysing the correlation between the severity of radiation dermatitis and dose distribution in clinical practice and for reliable reporting in clinical trials. We developed a novel radiation dermatitis segmentation system based on convolutional neural networks (CNNs) to consistently evaluate radiation dermatitis.

MATERIALS AND METHODS

The radiation dermatitis segmentation system is designed to segment the radiation dermatitis occurrence area using skin photographs and skin-dose distribution. A CNN architecture with a dilated convolution layer and skip connection was designed to estimate the radiation dermatitis area. Seventy-three skin photographs obtained from patients undergoing radiotherapy were collected for training and testing. The ground truth of radiation dermatitis segmentation is manually delineated from the skin photograph by an experienced radiation oncologist and medical physicist. We converted the skin photographs to RGB (red-green-blue) and CIELAB (lightness (L^∗), red-green (a^∗) and blue-yellow (b^∗)) colour information and trained the network to segment faint and severe radiation dermatitis using three different input combinations: RGB, RGB + CIELAB (RGBLAB) and RGB + CIELAB + skin-dose distribution (RGBLAB_D). The proposed system was evaluated using the Dice similarity coefficient (DSC), sensitivity, specificity and normalised Matthews correlation coefficient (nMCC). A paired t-test was used to compare the results of different segmentation performances.

RESULTS

Optimal data composition was observed in the network trained for radiation dermatitis segmentation using skin photographs and skin-dose distribution. The average DSC, sensitivity, specificity and nMCC values of RGBLAB_D were 0.62, 0.61, 0.91 and 0.77, respectively, in faint radiation dermatitis, and 0.69, 0.78, 0.96 and 0.83, respectively, in severe radiation dermatitis.

CONCLUSION

Our study showed that CNN-based radiation dermatitis segmentation in skin photographs of patients undergoing radiotherapy can describe radiation dermatitis severity and pattern. Our study could aid in objectifying the radiation dermatitis grading and analysing the reliable correlation between dosimetric factors and the morphology of radiation dermatitis.

ADAM Challenge: Detecting Age-Related Macular Degeneration From Fundus Images

Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the ADAM challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the ADAM challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models.

Reconfiguration of operation modes in silicon nanowire field-effect transistors by electrostatic virtual doping

In this study, we perform reconfigurable n- and p-channel operations of a tri-top-gate field-effect transistor (FET) made of a p⁺-i-n⁺silicon nanowire (SiNW). In the reconfigurable FET (RFET), two polarity gates and one control gate induce virtual electrostatic doping in the SiNW channel. The polarity gates are electrically connected to each other and program the channel type, while the control gate modulates the flow of charge carriers in the SiNW channel. The SiNW RFET features simple device design, symmetrical electrical characteristics in the n- and p-channel operation modes using p⁺-i-n⁺diode characteristics, and both operation modes exhibit high ON/OFF ratios (∼10⁶) and high ON currents (∼1μAμm^-1). The proposed device is demonstrated experimentally using a fully CMOS-compatible top-down processes.

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)-a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.

Virtual object sizes for efficient and convenient mid-air manipulation

It has been taken for granted that the sizes of virtual objects affect the efficiency and convenience of mid-air manipulation in immersive virtual environments. If a virtual object is too small or too large, for example, manipulating it becomes a difficult task. Nevertheless, the virtual object sizes that are optimal and convenient have rarely been studied. In this paper, we select a virtual object with many distinct geometric features and conduct user studies via docking tasks. Through the user studies, the optimal and convenient sizes for mid-air manipulation are estimated. In order to verify the results, a proxy-based manipulation method is designed and implemented, where the proxy is created with the estimated optimal size. The test based on the method shows that the optimal-size proxy enables users to manipulate efficiently virtual objects and the estimated range of convenient sizes is also preferred by the users.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00371-022-02555-6.

DeepDRiD: Diabetic Retinopathy-Grading and Image Quality Estimation Challenge

We described a challenge named "Diabetic Retinopathy (DR)-Grading and Image Quality Estimation Challenge" in conjunction with ISBI 2020 to hold three sub-challenges and develop deep learning models for DR image assessment and grading. The scientific community responded positively to the challenge, with 34 submissions from 574 registrations. In the challenge, we provided the DeepDRiD dataset containing 2,000 regular DR images (500 patients) and 256 ultra-widefield images (128 patients), both having DR quality and grading annotations. We discussed details of the top 3 algorithms in each sub-challenges. The weighted kappa for DR grading ranged from 0.93 to 0.82, and the accuracy for image quality evaluation ranged from 0.70 to 0.65. The results showed that image quality assessment can be used as a further target for exploration. We also have released the DeepDRiD dataset on GitHub to help develop automatic systems and improve human judgment in DR screening and diagnosis.

HM-Chromanone Ameliorates Hyperglycemia and Dyslipidemia in Type 2 Diabetic Mice

The effects of (E)-5-hydroxy-7-methoxy-3-(2-hydroxybenzyl)-4-chromanone (HMC) on hyperglycemia and dyslipidemia were investigated in diabetic mice. Mice were separated into three groups: db/db, rosiglitazone and HMC. Blood glucose or glycosylated hemoglobin values in HMC-treated mice were significantly lower compared to db/db mice. Total cholesterol, LDL-cholesterol, and triglyceride values were lower, and HDL-C levels were higher, in the HMC group compared to the diabetic and rosiglitazone groups. HMC markedly increased IRS-1^Tyr612, Akt^Ser473 and PI3K levels and plasma membrane GLUT4 levels in skeletal muscle, suggesting improved insulin resistance. HMC also significantly stimulated AMPK^Thr172 and PPARα in the liver, and ameliorated dyslipidemia by inhibiting SREBP-1c and FAS. Consequently, HMC reduced hyperglycemia by improving the expression of insulin-resistance-related genes and improved dyslipidemia by regulating fatty acid synthase and oxidation-related genes in db/db mice. Therefore, HMC could ameliorate hyperglycemia and dyslipidemia in type 2 diabetic mice.

NAND and NOR logic-in-memory comprising silicon nanowire feedback field-effect transistors

The processing of large amounts of data requires a high energy efficiency and fast processing time for high-performance computing systems. However, conventional von Neumann computing systems have performance limitations because of bottlenecks in data movement between separated processing and memory hierarchy, which causes latency and high power consumption. To overcome this hindrance, logic-in-memory (LIM) has been proposed that performs both data processing and memory operations. Here, we present a NAND and NOR LIM composed of silicon nanowire feedback field-effect transistors, whose configuration resembles that of CMOS logic gate circuits. The LIM can perform memory operations to retain its output logic under zero-bias conditions as well as logic operations with a high processing speed of nanoseconds. The newly proposed dynamic voltage-transfer characteristics verify the operating principle of the LIM. This study demonstrates that the NAND and NOR LIM has promising potential to resolve power and processing speed issues.

One-transistor static random-access memory cell array comprising single-gated feedback field-effect transistors

In this study, we fabricated a 2 × 2 one-transistor static random-access memory (1T-SRAM) cell array comprising single-gated feedback field-effect transistors and examined their operation and memory characteristics. The individual 1T-SRAM cell had a retention time of over 900 s, nondestructive reading characteristics of 10,000 s, and an endurance of 10⁸ cycles. The standby power of the individual 1T-SRAM cell was estimated to be 0.7 pW for holding the "0" state and 6 nW for holding the "1" state. For a selected cell in the 2 × 2 1T-SRAM cell array, nondestructive reading of the memory was conducted without any disturbance in the half-selected cells. This immunity to disturbances validated the reliability of the 1T-SRAM cell array.

Effect of Ge Mole Fraction on Performance of Underlapped Gate-All-Around SiGe-Source Tunneling Field-Effect Transistors

In this paper, we propose the design optimization of underlapped Si_1-xGe_x-source tunneling field-effect transistors (TFETs) with a gate-all-around structure. The band-to-band tunneling rates, tunneling barrier widths, I-V transfer characteristics, threshold voltages, on/off current ratios, and subthreshold swings (SSs) were analyzed by varying the Ge mole fraction of the Si_1-xGe_x source using a commercial device simulator. In particular, a Si_0.2Ge_0.8-source TFET among our proposed TFETs exhibits an on/off current ratio of approximately 10¹³, and SS of 27.4 mV/dec.

Steep Switching Characteristics of Partially Gated p+-n+-i-n+ Silicon-Nanowire Transistors

In this study, we examine the electrical characteristics of p⁺-n⁺-i-n⁺ silicon-nanowire field-effect transistors with partially gated channels. The silicon-nanowire field-effect transistors operate with barrier height modulation through positive feedback loops of charge carriers triggered by impact ionization. Our field-effect transistors exhibit outstanding switching characteristics, with an on current of ˜10^-4 A, an on/off current ratio of ˜10⁶, and a point subthreshold swing of ˜23 mV/dec. Moreover, the devices inhibit ambipolar characteristics because of the use of the partially gated structure and feature the p-channel operation mode.

Inverting logic-in-memory cells comprising silicon nanowire feedback field-effect transistors

In this paper, we propose inverting logic-in-memory (LIM) cells comprising silicon nanowire feedback field-effect transistors with steep switching and holding characteristics. The timing diagrams of the proposed inverting LIM cells under dynamic and static conditions are investigated via mixed-mode technology computer-aided design simulation to verify the performance. The inverting LIM cells have an operating speed of the order of nanoseconds, an ultra-high voltage gain, and a longer retention time than that of conventional dynamic random access memory. The disturbance characteristics of half-selected cells within an inverting LIM array confirm the appropriate functioning of the random access memory array.

Predicting High Coronary Artery Calcium Score From Retinal Fundus Images With Deep Learning Algorithms

Purpose

To evaluate high accumulation of coronary artery calcium (CAC) from retinal fundus images with deep learning technologies as an inexpensive and radiation-free screening method.

Methods

Individuals who underwent bilateral retinal fundus imaging and CAC score (CACS) evaluation from coronary computed tomography scans on the same day were identified. With this database, performances of deep learning algorithms (inception-v3) to distinguish high CACS from CACS of 0 were evaluated at various thresholds for high CACS. Vessel-inpainted and fovea-inpainted images were also used as input to investigate areas of interest in determining CACS.

Results

A total of 44,184 images from 20,130 individuals were included. A deep learning algorithm for discrimination of no CAC from CACS >100 achieved area under receiver operating curve (AUROC) of 82.3% (79.5%–85.0%) and 83.2% (80.2%–86.3%) using unilateral and bilateral fundus images, respectively, under a 5-fold cross validation setting. AUROC increased as the criterion for high CACS was increased, showing a plateau at 100 and losing significant improvement thereafter. AUROC decreased when fovea was inpainted and decreased further when vessels were inpainted, whereas AUROC increased when bilateral images were used as input.

Conclusions

Visual patterns of retinal fundus images in subjects with CACS > 100 could be recognized by deep learning algorithms compared with those with no CAC. Exploiting bilateral images improves discrimination performance, and ablation studies removing retinal vasculature or fovea suggest that recognizable patterns reside mainly in these areas.

Translational Relevance

Retinal fundus images can be used by deep learning algorithms for prediction of high CACS.

Laterality Classification of Fundus Images Using Interpretable Deep Neural Network

In this paper, we aimed to understand and analyze the outputs of a convolutional neural network model that classifies the laterality of fundus images. Our model not only automatizes the classification process, which results in reducing the labors of clinicians, but also highlights the key regions in the image and evaluates the uncertainty for the decision with proper analytic tools. Our model was trained and tested with 25,911 fundus images (43.4% of macula-centered images and 28.3% each of superior and nasal retinal fundus images). Also, activation maps were generated to mark important regions in the image for the classification. Then, uncertainties were quantified to support explanations as to why certain images were incorrectly classified under the proposed model. Our model achieved a mean training accuracy of 99%, which is comparable to the performance of clinicians. Strong activations were detected at the location of optic disc and retinal blood vessels around the disc, which matches to the regions that clinicians attend when deciding the laterality. Uncertainty analysis discovered that misclassified images tend to accompany with high prediction uncertainties and are likely ungradable. We believe that visualization of informative regions and the estimation of uncertainty, along with presentation of the prediction result, would enhance the interpretability of neural network models in a way that clinicians can be benefitted from using the automatic classification system.

IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge

Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on "Diabetic Retinopathy - Segmentation and Grading" was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal sub-challenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.

Towards Accurate Segmentation of Retinal Vessels and the Optic Disc in Fundoscopic Images with Generative Adversarial Networks

Automatic segmentation of the retinal vasculature and the optic disc is a crucial task for accurate geometric analysis and reliable automated diagnosis. In recent years, Convolutional Neural Networks (CNN) have shown outstanding performance compared to the conventional approaches in the segmentation tasks. In this paper, we experimentally measure the performance gain for Generative Adversarial Networks (GAN) framework when applied to the segmentation tasks. We show that GAN achieves statistically significant improvement in area under the receiver operating characteristic (AU-ROC) and area under the precision and recall curve (AU-PR) on two public datasets (DRIVE, STARE) by segmenting fine vessels. Also, we found a model that surpassed the current state-of-the-art method by 0.2 - 1.0% in AU-ROC and 0.8 - 1.2% in AU-PR and 0.5 - 0.7% in dice coefficient. In contrast, significant improvements were not observed in the optic disc segmentation task on DRIONS-DB, RIM-ONE (r3) and Drishti-GS datasets in AU-ROC and AU-PR.

A Novel Fundus Image Reading Tool for Efficient Generation of a Multi-dimensional Categorical Image Database for Machine Learning Algorithm Training

BACKGROUND

We described a novel multi-step retinal fundus image reading system for providing high-quality large data for machine learning algorithms, and assessed the grader variability in the large-scale dataset generated with this system.

METHODS

A 5-step retinal fundus image reading tool was developed that rates image quality, presence of abnormality, findings with location information, diagnoses, and clinical significance. Each image was evaluated by 3 different graders. Agreements among graders for each decision were evaluated.

RESULTS

The 234,242 readings of 79,458 images were collected from 55 licensed ophthalmologists during 6 months. The 34,364 images were graded as abnormal by at-least one rater. Of these, all three raters agreed in 46.6% in abnormality, while 69.9% of the images were rated as abnormal by two or more raters. Agreement rate of at-least two raters on a certain finding was 26.7%-65.2%, and complete agreement rate of all-three raters was 5.7%-43.3%. As for diagnoses, agreement of at-least two raters was 35.6%-65.6%, and complete agreement rate was 11.0%-40.0%. Agreement of findings and diagnoses were higher when restricted to images with prior complete agreement on abnormality. Retinal/glaucoma specialists showed higher agreements on findings and diagnoses of their corresponding subspecialties.

CONCLUSION

This novel reading tool for retinal fundus images generated a large-scale dataset with high level of information, which can be utilized in future development of machine learning-based algorithms for automated identification of abnormal conditions and clinical decision supporting system. These results emphasize the importance of addressing grader variability in algorithm developments.

Long-term outcomes of laparoscopic large hiatus hernia repair with nonabsorbable mesh

The use of mesh to augment suture repair of large hiatus hernias remains controversial. Repair with mesh may help reduce the recurrence rate of primary repair, but concerns about the potential for serious complications, such as mesh erosion or stricturing, continue to limit its use. We aim to evaluate the long-term outcome of primary hiatus hernia repair with lightweight polypropylene mesh (TiMesh) specifically looking at rates of clinical recurrence, dysphagia, and mesh-related complications. From a prospectively maintained database, 50 consecutive patients who underwent elective primary laparoscopic hiatal hernia repair with TiMesh between January 2005 and December 2007 were identified. Case notes and postoperative endoscopy reports were reviewed. Clinical outcomes were evaluated using a structured questionnaire, including a validated dysphagia score. Of the 50 patients identified, 36 (72%) were contactable for follow-up. At a median follow-up of 9 years, the majority of patients (97%) regarded their surgery as successful. Twelve patients (33%) reported a recurrence of their symptoms, but only 4 (11%) reported that their symptoms were as severe as prior to the surgery. There was no significant difference between pre- and postoperative dysphagia scores. Postoperative endoscopy reports were available for 32 patients at a median time point of 4 years postoperatively, none of which revealed any mesh-related complications. One patient had undergone a revision procedure for a recurrent hernia at another institution. In this series, primary repair of large hiatus hernia with nonabsorbable mesh was not associated with any adverse effects over time. Patient satisfaction with symptomatic outcome remained high in the long term.

Patient-specific medial unicompartmental knee arthroplasty has a greater protective effect on articular cartilage in the lateral compartment: A Finite Element Analysis

OBJECTIVES

Patient-specific (PS) implantation surgical technology has been introduced in recent years and a gradual increase in the associated number of surgical cases has been observed. PS technology uses a patient's own geometry in designing a medical device to provide minimal bone resection with improvement in the prosthetic bone coverage. However, whether PS unicompartmental knee arthroplasty (UKA) provides a better biomechanical effect than standard off-the-shelf prostheses for UKA has not yet been determined, and still remains controversial in both biomechanical and clinical fields. Therefore, the aim of this study was to compare the biomechanical effect between PS and standard off-the-shelf prostheses for UKA.

METHODS

The contact stresses on the polyethylene (PE) insert, articular cartilage and lateral meniscus were evaluated in PS and standard off-the-shelf prostheses for UKA using a validated finite element model. Gait cycle loading was applied to evaluate the biomechanical effect in the PS and standard UKAs.

RESULTS

The contact stresses on the PE insert were similar for both the PS and standard UKAs. Compared with the standard UKA, the PS UKA did not show any biomechanical effect on the medial PE insert. However, the contact stresses on the articular cartilage and the meniscus in the lateral compartment following the PS UKA exhibited closer values to the healthy knee joint compared with the standard UKA.

CONCLUSION

The PS UKA provided mechanics closer to those of the normal knee joint. The decreased contact stress on the opposite compartment may reduce the overall risk of progressive osteoarthritis.Cite this article: K-T. Kang, J. Son, D-S. Suh, S. K. Kwon, O-R. Kwon, Y-G. Koh. Patient-specific medial unicompartmental knee arthroplasty has a greater protective effect on articular cartilage in the lateral compartment: A Finite Element Analysis. Bone Joint Res 2018;7:20-27. DOI: 10.1302/2046-3758.71.BJR-2017-0115.R2.

A computational simulation study to determine the biomechanical influence of posterior condylar offset and tibial slope in cruciate retaining total knee arthroplasty

Objectives

Posterior condylar offset (PCO) and posterior tibial slope (PTS) are critical factors in total knee arthroplasty (TKA). A computational simulation was performed to evaluate the biomechanical effect of PCO and PTS on cruciate retaining TKA.

Methods

We generated a subject-specific computational model followed by the development of ± 1 mm, ± 2 mm and ± 3 mm PCO models in the posterior direction, and -3°, 0°, 3° and 6° PTS models with each of the PCO models. Using a validated finite element (FE) model, we investigated the influence of the changes in PCO and PTS on the contact stress in the patellar button and the forces on the posterior cruciate ligament (PCL), patellar tendon and quadriceps muscles under the deep knee-bend loading conditions.

Results

Contact stress on the patellar button increased and decreased as PCO translated to the anterior and posterior directions, respectively. In addition, contact stress on the patellar button decreased as PTS increased. These trends were consistent in the FE models with altered PCO. Higher quadriceps muscle and patellar tendon force are required as PCO translated in the anterior direction with an equivalent flexion angle. However, as PTS increased, quadriceps muscle and patellar tendon force reduced in each PCO condition. The forces exerted on the PCL increased as PCO translated to the posterior direction and decreased as PTS increased.

Conclusion

The change in PCO alternatively provided positive and negative biomechanical effects, but it led to a reduction in a negative biomechanical effect as PTS increased.

Cite this article: K-T. Kang, Y-G. Koh, J. Son, O-R. Kwon, J-S. Lee, S. K. Kwon. A computational simulation study to determine the biomechanical influence of posterior condylar offset and tibial slope in cruciate retaining total knee arthroplasty. Bone Joint Res 2018;7:69–78. DOI: 10.1302/2046-3758.71.BJR-2017-0143.R1.

Spin-Orbit Coupling and Interband Transitions in the Optical Conductivity of Sr_{2}RhO_{4}

The prototypical correlated metal Sr_{2}RhO_{4} was studied using optical and photoemission spectroscopy. At low energies and temperatures, the optical data reveal a complex, multicomponent response that on the surface points to an unconventional metallic state in this material. Via a comparison with photoemission, the anomalous optical response may be attributed to an unexpectedly strong interband transition near 180 meV between spin-orbit coupled bands that are nearly parallel along ΓX. This spin-orbit coupling effect is shown to occur in a number of related metallic ruthenates and explains the previously puzzling optical properties reported for these materials.

Computational study on the effect of malalignment of the tibial component on the biomechanics of total knee arthroplasty: A Finite Element Analysis

Objectives

Malalignment of the tibial component could influence the long-term survival of a total knee arthroplasty (TKA). The object of this study was to investigate the biomechanical effect of varus and valgus malalignment on the tibial component under stance-phase gait cycle loading conditions.

Methods

Validated finite element models for varus and valgus malalignment by 3° and 5° were developed to evaluate the effect of malalignment on the tibial component in TKA. Maximum contact stress and contact area on a polyethylene insert, maximum contact stress on patellar button and the collateral ligament force were investigated.

Results

There was greater total contact stress in the varus alignment than in the valgus, with more marked difference on the medial side. An increase in ligament force was clearly demonstrated, especially in the valgus alignment and force exerted on the medial collateral ligament also increased.

Conclusion

These results highlight the importance of accurate surgical reconstruction of the coronal tibial alignment of the knee joint. Varus and valgus alignments will influence wear and ligament stability, respectively in TKA.

Cite this article: D-S. Suh, K-T. Kang, J. Son, O-R. Kwon, C. Baek, Y-G. Koh. Computational study on the effect of malalignment of the tibial component on the biomechanics of total knee arthroplasty: A Finite Element Analysis. Bone Joint Res 2017;6:623–630. DOI: 10.1302/2046-3758.611.BJR-2016-0088.R2.

Preservation of kinematics with posterior cruciate-, bicruciate- and patient-specific bicruciate-retaining prostheses in total knee arthroplasty by using computational simulation with normal knee model

Objectives

Preservation of both anterior and posterior cruciate ligaments in total knee arthroplasty (TKA) can lead to near-normal post-operative joint mechanics and improved knee function. We hypothesised that a patient-specific bicruciate-retaining prosthesis preserves near-normal kinematics better than standard off-the-shelf posterior cruciate-retaining and bicruciate-retaining prostheses in TKA.

Methods

We developed the validated models to evaluate the post-operative kinematics in patient-specific bicruciate-retaining, standard off-the-shelf bicruciate-retaining and posterior cruciate-retaining TKA under gait and deep knee bend loading conditions using numerical simulation.

Results

Tibial posterior translation and internal rotation in patient-specific bicruciate-retaining prostheses preserved near-normal kinematics better than other standard off-the-shelf prostheses under gait loading conditions. Differences from normal kinematics were minimised for femoral rollback and internal-external rotation in patient-specific bicruciate-retaining, followed by standard off-the-shelf bicruciate-retaining and posterior cruciate-retaining TKA under deep knee bend loading conditions. Moreover, the standard off-the-shelf posterior cruciate-retaining TKA in this study showed the most abnormal performance in kinematics under gait and deep knee bend loading conditions, whereas patient-specific bicruciate-retaining TKA led to near-normal kinematics.

Conclusion

This study showed that restoration of the normal geometry of the knee joint in patient-specific bicruciate-retaining TKA and preservation of the anterior cruciate ligament can lead to improvement in kinematics compared with the standard off-the-shelf posterior cruciate-retaining and bicruciate-retaining TKA.

Cite this article: Y-G. Koh, J. Son, S-K. Kwon, H-J. Kim, O-R. Kwon, K-T. Kang. Preservation of kinematics with posterior cruciate-, bicruciate- and patient-specific bicruciate-retaining prostheses in total knee arthroplasty by using computational simulation with normal knee model. Bone Joint Res 2017;6:557–565. DOI: 10.1302/2046-3758.69.BJR-2016-0250.R1.