A deep convolutional neural network to predict the curve progression of adolescent idiopathic scoliosis: a pilot study.

BACKGROUND: Adolescent idiopathic scoliosis (AIS) is a three-dimensional spinal deformity that predominantly occurs in girls. While skeletal growth and maturation influence the development of AIS, accurate prediction of curve progression remains difficult because the prognosis for deformity differs among individuals. The purpose of this study is to develop a new diagnostic platform using a deep convolutional neural network (DCNN) that can predict the risk of scoliosis progression in patients with AIS. METHODS: Fifty-eight patients with AIS (49 females and 9 males; mean age: 12.5 ± 1.4 years) and a Cobb angle between 10 and 25 degrees (mean angle: 18.7 ± 4.5) were divided into two groups: those whose Cobb angle increased by more than 10 degrees within two years (progression group, 28 patients) and those whose Cobb angle changed by less than 5 degrees (non-progression group, 30 patients). The X-ray images of three regions of interest (ROIs) (lung [ROI1], abdomen [ROI2], and total spine [ROI3]), were used as the source data for learning and prediction. Five spine surgeons also predicted the progression of scoliosis by reading the X-rays in a blinded manner. RESULTS: The prediction performance of the DCNN for AIS curve progression showed an accuracy of 69% and an area under the receiver-operating characteristic curve of 0.70 using ROI3 images, whereas the diagnostic performance of the spine surgeons showed inferior at 47%. Transfer learning with a pretrained DCNN contributed to improved prediction accuracy. CONCLUSION: Our developed method to predict the risk of scoliosis progression in AIS by using a DCNN could be a valuable tool in decision-making for therapeutic interventions for AIS.

Inorganic polyphosphate enhances radio-sensitivity in a human non-small cell lung cancer cell line, H1299.

Inorganic polyphosphate is a linear polymer containing tens to hundreds of orthophosphate residues linked by high-energy phosphoanhydride bonds. Polyphosphate has been recognized as a potent anti-metastasis reagent. However, the molecular mechanism underlying polyphosphate action on cancer cells is poorly understood. In this study, we investigated the involvement of polyphosphate in radio-sensitivity using a human non-small cell lung cancer cell line, H1299. We found that polyphosphate treatment decreases cellular adenosine triphosphate levels, suggesting a disruption of energy metabolism. We also found that the induction of DNA double-strand breaks was enhanced in polyphosphate-treated cells after X-ray irradiation and colony formation assay revealed that cell survival decreased compared with that of the control groups. These findings suggest that polyphosphate is a promising radio-sensitizer for cancer cells. Therefore, we hypothesized that polyphosphate treatment disrupts adenosine triphosphate-mediated energy transfer for cellular survival and DNA repair, thereby reducing the cellular capability to resist X-ray irradiation.

Deep learning-based classification of adequate sonographic images for self-diagnosing deep vein thrombosis.

BACKGROUND: Pulmonary thromboembolism is a serious disease that often occurs in disaster victims evacuated to shelters. Deep vein thrombosis is the most common reason for pulmonary thromboembolism, and early prevention is important. Medical technicians often perform ultrasonography as part of mobile medical screenings of disaster victims but reaching all isolated and scattered shelters is difficult. Therefore, deep vein thrombosis medical screening methods that can be easily performed by anyone are needed. The purpose of this study was to develop a method to automatically identify cross-sectional images suitable for deep vein thrombosis diagnosis so disaster victims can self-assess their risk of deep vein thrombosis. METHODS: Ultrasonographic images of the popliteal vein were acquired in 20 subjects using stationary and portable ultrasound diagnostic equipment. Images were obtained by frame split from video. Images were classified as "Satisfactory," "Moderately satisfactory," and "Unsatisfactory" according to the level of popliteal vein visualization. Fine-tuning and classification were performed using ResNet101, a deep learning model. RESULTS: Acquiring images with portable ultrasound diagnostic equipment resulted in a classification accuracy of 0.76 and an area under the receiver operating characteristic curve of 0.89. Acquiring images with stationary ultrasound diagnostic equipment resulted in a classification accuracy of 0.73 and an area under the receiver operating characteristic curve of 0.88. CONCLUSION: A method for automatically identifying appropriate diagnostic cross-sectional ultrasonographic images of the popliteal vein was developed. This elemental technology is sufficiently accurate to automatically self-assess the risk of deep vein thrombosis by disaster victims.