Comparison of 1.5 T(Tesla) and 3.0 T(Tesla) Magnetic Resonance Imaging for Evaluating Local Extension of Endometrial Cancer

Magnetic resonance imaging (MRI) is an important means of evaluating local extension of endometrial cancer. The 3.0 Tesla (T) MRI system introduced in 2005 improved the diagnostic capabilities of this modality due to an increased signal to noise ratio; however, it was also susceptible to artifacts and debate remains regarding the clinical applicability of 3.0 T MRI in the pelvic region. A few reports have compared 1.5 T and 3.0 T MRI for determining the degree of progression of endometrial cancer. Therefore, we conducted a comparative study of the diagnostic capability of 1.5 T and 3.0 T MRI for the local extension of endometrial cancer. Over the 6 years and 8 months from 1 January 2008 to 30 August 2014, preoperative MRI has been conducted at our hospital including T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast-enhanced MRI for cases of endometrioid adenocarcinoma requiring surgery. We investigated 60 subjects after excluding cases for which the tumor could not be imaged and cases that underwent surgery 2 months or more after undergoing MRI. Two radiologists used magnetic resonance images taken preoperatively to determine local extension using T2-weighted, diffusion-weighted, and dynamic-study images. Results for local extension were compared with those of postoperative histopathology. Results indicated no significant difference in accurate diagnosis rates between 1.5 T and 3.0 T MRI for any of the imaging modalities examined by both radiologists

The Usefulness of Diffusion-weighted Imaging in Observing Localized Extension of Endometrial Cancer

Endometrial cancer is the seventh most common human malignancy and the most common form of cancer treated in women by obstetrics and gynecology departments. Until now, magnetic resonance imaging (MRI) has been used for pre-surgical evaluation of endometrial cancer and evaluating the depth of myometrial invasion, in addition to being a valuable diagnostic tool. Diffusion-weighted imaging (DWI) has been reported as useful in distinguishing between benign and malignant tumors when observing lesions in the endometrium. Subsequent reports suggest that DWI is also effective in identifying malignancy and diagnosing local extension in a range of tissues. Based on this, we implemented a study of the effectiveness of DWI in identifying local extension of endometrial cancer. This study enrolled patients undergoing surgery at this hospital for cancer of the uterine body during the six years from January 2008 to February 2014. Cases in which images were unclear or the lesions were too small to be described by MRI examination were excluded, leaving 61 patients in the study. Using the results from pre-surgical MRI, a sequence comprising a T2-weighted axial view alone and a T2-weighted axial view to which a diffusion-weighted axial view had been added was created for each patient. Two radiologists then independently examined the image sequence to determine localized extension. Following surgery, the pre-surgical assessment was compared to the localized extension determined by histopathology of post-surgical samples to evaluate the effectiveness of adding diffusion-weighted imaging to the process. The first radiographic interpreter\u27s rate of correct diagnosis using the T2-weighted axial view alone was 45 out of 55 cases (81.8%), while using the T2-weighted axial view to which a diffusion-weighted axial view had been added gave a correct diagnosis rate of 51 out of 55 cases (92.7%). The second radiographic interpreter\u27s rate of correct diagnosis using the T2-weighted axial view alone was 41 out of 55 cases (74.5%), while using the T2-weighted axial view with diffusion-weighted axial view added gave a correct diagnosis rate of 51 out of 55 cases (92.7%). These differences were statistically significant based on the McNemar testing. This study confirmed that DWI is an effective means of diagnosing localized extension from images. It is anticipated that DWI will be used in the future clinical workplace to provide more accurate pre-surgical diagnoses

Fuzzy Support System for Total Hip Arthroplasty Stem by Ultrasonic Intraoperative Measurement

This paper describes a fuzzy system of stem implantation on total hip arthroplasty by an ultrasonic device. The system can perform automatic and accurate assessment in the surgery. In this system, we employ a single ultrasonic probe whose center frequency is 1,000 Hz. We detect the acoustic signals when knocking the inserted stem with a hammer. We then have a correlation between the degree of tightening and the attenuation time of acoustic signal. That is, the higher tightened degree implies shorter attenuation period. The support system selects the most suitable stem size by fuzzy inference with respect to the attenuation time and its difference time from correct stem to one larger size stem which dynamically adapts to each patient. As a result, we successfully determined the suitable stem in comparison to the results of the practical surgery

Enhancing fracture diagnosis in pelvic X-rays by deep convolutional neural network with synthesized images from 3D-CT

Abstract Pelvic fractures pose significant challenges in medical diagnosis due to the complex structure of the pelvic bones. Timely diagnosis of pelvic fractures is critical to reduce complications and mortality rates. While computed tomography (CT) is highly accurate in detecting pelvic fractures, the initial diagnostic procedure usually involves pelvic X-rays (PXR). In recent years, many deep learning-based methods have been developed utilizing ImageNet-based transfer learning for diagnosing hip and pelvic fractures. However, the ImageNet dataset contains natural RGB images which are different than PXR. In this study, we proposed a two-step transfer learning approach that improved the diagnosis of pelvic fractures in PXR images. The first step involved training a deep convolutional neural network (DCNN) using synthesized PXR images derived from 3D-CT by digitally reconstructed radiographs (DRR). In the second step, the classification layers of the DCNN were fine-tuned using acquired PXR images. The performance of the proposed method was compared with the conventional ImageNet-based transfer learning method. Experimental results demonstrated that the proposed DRR-based method, using 20 synthesized PXR images for each CT, achieved superior performance with the area under the receiver operating characteristic curves (AUROCs) of 0.9327 and 0.8014 for visible and invisible fractures, respectively. The ImageNet-based method yields AUROCs of 0.8908 and 0.7308 for visible and invisible fractures, respectively

Rupture Prediction for Microscopic Oocyte Images of Piezo Intracytoplasmic Sperm Injection by Principal Component Analysis

Assisted reproductive technology (ART) has progressed rapidly, resulting in a great improvement in the clinical pregnancy ratio. When applying the protocol of piezo intracytoplasmic sperm injection (Piezo-ICSI), it is very important to puncture the zona pellucida and the oocyte cytoplasmic membrane without rupturing the oocyte cytoplasmic membrane. Previous studies have shown that the poor extensibility of the oocyte cytoplasmic membrane might be closely related to rupture. However, no consensus has been reached regarding how the quality of the oocyte for extensible ability or rupture possibility affects the surfaces of the oocyte on the microscopic frames. We conducted this study to provide evidence that artificial intelligence (AI) techniques are superior for predicting the tendency of oocyte rupture before puncturing on Piezo-ICSI. To inspect it, we provided a retrospective trial of 38 rupture oocytes and 55 nonruptured oocytes. This study marked the highest accuracy of 91.4% for predicting oocytes rupture using the support-vector machine method of machine learning. We conclude that AI technologies might serve an important role and provide a significant benefit to ART

Inappropriate Timing of Swallow in the Respiratory Cycle Causes Breathing–Swallowing Discoordination

Rationale: Swallowing during inspiration and swallowing immediately followed by inspiration increase the chances of aspiration and may cause disease exacerbation. However, the mechanisms by which such breathing-swallowing discoordination occurs are not well-understood. Objectives: We hypothesized that breathing-swallowing discoordination occurs when the timing of the swallow in the respiratory cycle is inappropriate. To test this hypothesis, we monitored respiration and swallowing activity in healthy subjects and in patients with dysphagia using a non-invasive swallowing monitoring system. Measurements and Main Results: The parameters measured included the timing of swallow in the respiratory cycle, swallowing latency (interval between the onset of respiratory pause and the onset of swallow), pause duration (duration of respiratory pause for swallowing), and the breathing-swallowing coordination pattern. We classified swallows that closely follow inspiration (I) as I-SW, whereas those that precede I as SW-I pattern. Patients with dysphagia had prolonged swallowing latency and pause duration, and tended to have I-SWor SW-I patterns reflecting breathing-swallows discoordination. Conclusions: We conclude that swallows at inappropriate timing in the respiratory cycle cause breathing-swallowing discoordination, and the prolongation of swallowing latency leads to delayed timing of the swallow, and results in an increase in the SW-I pattern in patients with dysphagia

Singing Experience Influences RSST Scores

It has recently been shown that the aging population is refractory to the maintenance of swallowing function, which can seriously affect quality of life. Singing and vocal training contribute to mastication, swallowing and respiratory function. Previous studies have shown that singers have better vocal cord health. No consensus has been reached as to how vocal training affects swallowing ability. Our study was designed to establish evidence that singers are statistically superior at inducing the swallowing reflex. To test our hypothesis, we undertook a clinical trial on 55 singers and 141 non-singers (mean age: 60.1 ± 11.7 years). This cross-sectional study with propensity score matching resulted in significant differences in a repetitive saliva swallowing test among singers: 7.1 ± 2.4, n = 53 vs. non-singers: 5.9 ± 1.9, n = 53, p < 0.05. We conclude that singing can serve an important role in stabilizing the impact of voluntary swallowing on speech

Ultrasound Frequency-Based Monitoring for Bone Healing

Correct assessment of the bone healing process is required for the management of limb immobilization during the treatment of bone injuries, including fractures and defects. Although the monitoring of bone healing using ultrasound poses several advantages regarding cost and ionizing radiation exposure compared with other dominant imaging methods, such as radiography and computed tomography (CT), traditional ultrasound B-mode imaging lacks reliability and objectivity. However, the body structures can be quantitatively observed by ultrasound frequency-based methods, and therefore, the disadvantages of B-mode imaging can be overcome. In this study, we created a femoral bone hole model of a rat and observed the bone healing process using the quantitative ultrasound method and micro-CT, which provides a reliable assessment of the tissue microstructure of the bone. This study analyzed the correlation between these two assessments. The results revealed that the quantitative ultrasound measurements correlated with the CT measurements for rat bone healing. This ultrasound frequency-based method could have the potential to serve as a novel modality for quantitative monitoring of bone healing with the advantages of being less invasive and easily accessible. Impact statement Bone healing monitoring with ultrasound is advantageous as it is less invasive and easily accessible; however, the traditional B-mode method lacks reliability and objectivity. This study demonstrated that the proposed ultrasound frequency-based monitoring method can quantitatively observe bone healing and strongly correlates with the computed tomography measurements for rat bone healing. This method has the potential to become a reliable modality for monitoring bone healing