Effect of early nutritional initiation on post-cerebral infarction discharge destination: A propensity-matched analysis using machine learning.

AIM: Malnutrition is associated with poor outcomes in cerebral infarction patients, with research indicating that early nutritional initiation may improve the short-term prognosis of patients. However, evidence supported by big data is lacking. Here, to determine the effect of nutritional initiation during the first 3 days after hospital admission on home discharge rates, propensity score matching was conducted in patients with acute cerebral infarction. METHODS: This retrospective observational study, using the Diagnosis Procedure Combination anonymised database in Japan, included 41 477 ischaemic cerebral infarction patients hospitalised between 2016 and 2019. The patients were divided into two groups: those who received oral or enteral nutrition during the first 3 days of hospital admission (early nutrition group, n = 37 318) and those who did not (control group, n = 4159). One-to-one pair-matching was performed using propensity scores calculated via extreme gradient boosting to limit the confounding variables of the two groups. RESULTS: After propensity score matching, 3541 pairs of patients were selected. The dependence of home discharge rates on early nutrition was significant (p < 0.05), and the effectiveness of early nutrition for home discharge showed an odds ratio of 1.79 (95% confidence interval of 1.59-2.03 in Fisher's exact test). CONCLUSIONS: Our findings revealed that early nutritional initiation during the first 3 days of admission resulted in higher home discharge rates.

Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach.

BACKGROUND: Automatic surgical workflow recognition is a key component for developing the context-aware computer-assisted surgery (CA-CAS) systems. However, automatic surgical phase recognition focused on colorectal surgery has not been reported. We aimed to develop a deep learning model for automatic surgical phase recognition based on laparoscopic sigmoidectomy (Lap-S) videos, which could be used for real-time phase recognition, and to clarify the accuracies of the automatic surgical phase and action recognitions using visual information. METHODS: The dataset used contained 71 cases of Lap-S. The video data were divided into frame units every 1/30 s as static images. Every Lap-S video was manually divided into 11 surgical phases (Phases 0-10) and manually annotated for each surgical action on every frame. The model was generated based on the training data. Validation of the model was performed on a set of unseen test data. Convolutional neural network (CNN)-based deep learning was also used. RESULTS: The average surgical time was 175 min (± 43 min SD), with the individual surgical phases also showing high variations in the duration between cases. Each surgery started in the first phase (Phase 0) and ended in the last phase (Phase 10), and phase transitions occurred 14 (± 2 SD) times per procedure on an average. The accuracy of the automatic surgical phase recognition was 91.9% and those for the automatic surgical action recognition of extracorporeal action and irrigation were 89.4% and 82.5%, respectively. Moreover, this system could perform real-time automatic surgical phase recognition at 32 fps. CONCLUSIONS: The CNN-based deep learning approach enabled the recognition of surgical phases and actions in 71 Lap-S cases based on manually annotated data. This system could perform automatic surgical phase recognition and automatic target surgical action recognition with high accuracy. Moreover, this study showed the feasibility of real-time automatic surgical phase recognition with high frame rate.

Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging: a proof of concept study.

OBJECTIVES: To evaluate the diagnostic performance of deep learning with the convolutional neural networks (CNN) to distinguish each representative parkinsonian disorder using MRI. METHODS: This clinical retrospective study was approved by the institutional review board, and the requirement for written informed consent was waived. Midsagittal T1-weighted MRI of a total of 419 subjects (125 Parkinson's disease (PD), 98 progressive supranuclear palsy (PSP), and 54 multiple system atrophy with predominant parkinsonian features (MSA-P) patients, and 142 normal subjects) between January 2012 and April 2016 was retrospectively assessed. To deal with the overfitting problem of deep learning, all subjects were randomly divided into training (85%) and validation (15%) data sets with the same proportions of each disease and normal subjects. We trained the CNN to distinguish each parkinsonian disorder using single midsagittal T1-weighted MRI with a training group to minimize the differences between predicted output probabilities and the clinical diagnoses; then, we adopted the trained CNN to the validation data set. Subjects were classified into each parkinsonian disorder or normal condition according to the final diagnosis of the trained CNN, and we assessed the diagnostic performance of the CNN. RESULTS: The accuracies of diagnostic performances regarding PD, PSP, MSA-P, and normal subjects were 96.8, 93.7, 95.2, and 98.4%, respectively. The areas under the receiver operating characteristic curves for distinguishing each condition from others (PD, PSP, MSA-P, and normal subjects) were 0.995, 0.982, 0.990, and 1.000, respectively. CONCLUSION: Deep learning with CNN enables highly accurate discrimination of parkinsonian disorders using MRI. KEY POINTS: • Deep learning convolution neural network achieves differential diagnosis of PD, PSP, MSA-P, and normal controls with an accuracy of 96.8, 93.7, 95.2, and 98.4%, respectively. • The areas under the curves for distinguishing between PD, PSP, MSA-P, and normality were 0.995, 0.982, 0.990, and 1.000, respectively. • CNN may learn important features that humans not notice, and has a possibility to perform previously impossible diagnoses.

Effects of alfacalcidol on back extensor strength gained through back extensor exercise in postmenopausal women with osteoporosis.

OBJECTIVE: The aim of this study was to investigate the additive effect of the active form of vitamin D3 on the gain in back extensor strength through a back extensor exercise. DESIGN: A total of 107 postmenopausal women with osteoporosis were randomly divided into two groups: the D3 group and the control group. Both groups were treated with calcium and alendronate and undertook the back extensor exercise. Alfacalcidol was prescribed only to the D3 group. RESULTS: There was no significant difference in the demographic data between the two groups. Ninety-four participants who completed a 4-mo intervention were subjected to per-protocol analysis. There was no significant difference in the improvement in back extensor strength between two the groups (P = 0.349). All subjects were further categorized into two subgroups by age. In the older subgroup (≥68 yrs), no significant difference was found in the improvement in back extensor strength (P = 0.316). In the younger subgroup (<68 yrs), the back extensor strength in the D3 group was significantly more improved than in the control group (P = 0.034). CONCLUSIONS: The results of this study suggest that the administration of the active form of vitamin D3 enhances the beneficial effects of the back extensor exercise in patients younger than those in their late 60s.

Synovial sarcoma with massive ossification--a case report.

Synovial sarcoma with extensive osteoid production is rare. We report a case of synovial sarcoma of monophasic type with massive ossification. The diagnosis was confirmed by reverse-transcripitase polymerase chain reaction (RT-PCR). The patient was an-81-year-old woman with recurrent synovial sarcoma in her right knee. The tumor was primarily excised in 1989. It recurred and was removed again in 1996. However, in 1999 a painful mass appeared in the same site. Preoperative plain radiography and computed tomography revealed a 5 x 5 cm soft tissue mass with extensive ossification in the medial side of the right knee joint. The tumor was widely excised in 2000. Soft X-ray examination revealed a trabecular pattern of ossification in the excised tumor. Microscopically the tumor was composed of hypercellular spindle cells with fascicular arrangement with prominent ossification but no epithelial component. The tumor cells were positive for vimentin and focally positive for cytokeratin. The tumor expressed a sequence of SYT-SSX1 fusion gene transcript demonstrated by RT-PCR. Twelve years long survival of the present case without metastasis in spite of repeated recurrence suggests a better prognosis of synovial sarcomas with ossification.