Rhabdomyosarcoma is the most frequent cancer involving the trigone of the bladder, the proximal urethra, vagina and paratesticular tissue in children, and 15 percent of rhabdomyosarcoma in children arise in the urogenital tract. Rhabdomyosarcoma is highly malignant neoplasm with a tendency toward early infiltration of adjacent structures and metastases to regional lymphnodes and distant organs. We report tow cases of paratesticular rhabdomyosarcoma with brief review of literatures.
Child ; Humans ; Neoplasm Metastasis ; Rhabdomyosarcoma* ; Testis ; Urethra ; Urinary Bladder ; Vagina

In the article, Methods of Hematoxylin and Erosin Image Information Acquisition and Optimization in Confocal Microscopy, there was a typographical error in the title.

Deep learning has recently become one of the most actively researched technologies in the field of medical imaging. The availability of sufficient data and the latest advances in algorithms are important factors that influence the development of deep learning models. However, several other factors should be considered in developing an optimal generalized deep learning model. All the steps, including data collection, labeling, and pre-processing and model training, validation, and complexity can affect the performance of deep learning models. Therefore, appropriate optimization methods should be considered for each step during the development of a deep learning model. In this review, we discuss the important factors to be considered for the optimal development of deep learning models.

Objectives: As endoscopic, laparoscopic, and robotic surgical procedures become more common, surgical videos are increasingly being treated as records and serving as important data sources for education, research, and developing new solutions with recent advances in artificial intelligence (AI). However, most hospitals do not have a system that can store and manage such videos systematically. This study aimed to develop a system to help doctors manage surgical videos and turn them into content and data. Methods: We developed a video archiving and communication system (VACS) to systematically process surgical videos. The VACS consists of a video capture device called SurgBox and a video archiving system called SurgStory. SurgBox automatically transfers surgical videos recorded in the operating room to SurgStory. SurgStory then analyzes the surgical videos and indexes important sections or video frames to provide AI reports. It allows doctors to annotate classified indexing frames, “data-ize” surgical information, create educational content, and communicate with team members. Results: The VACS collects surgical and procedural videos, and helps users manage archived videos. The accuracy of a convolutional neural network learning model trained to detect the top five surgical instruments reached 96%. Conclusions With the advent of the VACS, the informational value of medical videos has increased. It is possible to improve the efficiency of doctors’ continuing education by making video-based online learning more active and supporting research using data from medical videos. The VACS is expected to promote the development of new AI-based products and services in surgical and procedural fields.

Objectives: As endoscopic, laparoscopic, and robotic surgical procedures become more common, surgical videos are increasingly being treated as records and serving as important data sources for education, research, and developing new solutions with recent advances in artificial intelligence (AI). However, most hospitals do not have a system that can store and manage such videos systematically. This study aimed to develop a system to help doctors manage surgical videos and turn them into content and data. Methods: We developed a video archiving and communication system (VACS) to systematically process surgical videos. The VACS consists of a video capture device called SurgBox and a video archiving system called SurgStory. SurgBox automatically transfers surgical videos recorded in the operating room to SurgStory. SurgStory then analyzes the surgical videos and indexes important sections or video frames to provide AI reports. It allows doctors to annotate classified indexing frames, “data-ize” surgical information, create educational content, and communicate with team members. Results: The VACS collects surgical and procedural videos, and helps users manage archived videos. The accuracy of a convolutional neural network learning model trained to detect the top five surgical instruments reached 96%. Conclusions With the advent of the VACS, the informational value of medical videos has increased. It is possible to improve the efficiency of doctors’ continuing education by making video-based online learning more active and supporting research using data from medical videos. The VACS is expected to promote the development of new AI-based products and services in surgical and procedural fields.

Deep learning has recently become one of the most actively researched technologies in the field of medical imaging. The availability of sufficient data and the latest advances in algorithms are important factors that influence the development of deep learning models. However, several other factors should be considered in developing an optimal generalized deep learning model. All the steps, including data collection, labeling, and pre-processing and model training, validation, and complexity can affect the performance of deep learning models. Therefore, appropriate optimization methods should be considered for each step during the development of a deep learning model. In this review, we discuss the important factors to be considered for the optimal development of deep learning models.

The authors analysed the interpupillary distance and the distance between optical centers in 200 spectacles wearers. The results were as follows; 1. Among the 200 glasses wearers, the cases of hospital prescriptions were 44 persons and the cases of optical shops prescriptions were 156 persons. 2. Hyperopic glasses wearers were 26 persons and myopic glasses wearers were 174 persons. 3. The distance between optical centers was coincided to the interpupillary distance in 26 persons and discrepant in 174 persons. 4. In 115 eyes the prismatic effects were more than 0.5 prism diopter, and maximal prismatic effect was 5.78 prism diopters. 5. The evaluation of the induced horizontal phoria showed that 119 persons had the induced esophoria and 55 persons had the induced exophoria. In 58 persons induced phoria were more than 1 prism diopter. Maximal induced esophoria was 11.55 prism diopters and maximal induced exophoria was 4.30 prism diopters.
Esotropia ; Exotropia ; Eyeglasses* ; Glass ; Humans ; Prescriptions ; Strabismus

No abstract available.

No abstract available.
Female ; Pregnancy ; Pregnancy, Ectopic* ; Sterilization, Tubal*

The Dandy-Walker syndrome is a developmental disorders of the brain characterized by cystic deformity of the 4th ventricle and agensis of the cerebellar vermis. Other systemic anomalies and chromosomal abnormalities are associated with this syndrome. We are experienced a case in a 9 months old male infant who presented initially with frequent vomiting, low birth weight, On the physical examination, a prominent occiput, palpable mass below the right upper quadrant, pulmonary valve stenosis, congenital dislocation of the hips, ventral flexion of fingers, clubfoots and the rocker-bottom deformities of feet were present. On the chromosomal study, there were chromosomal polymorphisms in a thickened C-band of chromosome No. 1 by C-banding method. The brain CT revealed a large, thin-walled, low density mass of CSF without enhancement in the posterior fossa, showing upward displacement of cerebellar hemisphere with absent inferior vermis(or associated with dysplastic cerebellar hemisphere). A brief review of the related literatures were included in this report.
Brain ; Chromosome Aberrations* ; Clubfoot ; Congenital Abnormalities ; Dandy-Walker Syndrome* ; Dislocations ; Fingers ; Foot ; Hip ; Humans ; Infant ; Infant, Low Birth Weight ; Infant, Newborn ; Male ; Physical Examination ; Pulmonary Valve Stenosis ; Vomiting