We have studied the phase separation of a binary mixture of a polymer and a nematic liquid crystal at a dilute polymer concentration. Various types of regular self-assemblies of polymer droplets such as branched chains and zigzag chains have been observed in addition to the previously reported straight chains. The kinetic process of the self-assembly is dominated by the transformations of a topological defect accompanied by a droplet in addition to the simple growth of the droplet. The spontaneous transformation from a ring defect (Saturn-ring defect) to a point defect (dipole) is an essential process in forming a straight chain composed of droplets with the same size. We also find that the transformations are induced by a nearby droplet with a point defect. This leads to a wide size distribution of droplets in a chain cluster and results in a branched chain. The difference in the chaining mechanisms is discussed using an electrostatic analogy. We also clarify that the symmetry breaking in the assembly is governed by the direction in which the cell containing the binary mixture is rubbed.
Although cervical alignment is important for evaluating spine disorders, manual measurement is time-consuming and burdensome. We aimed to validate the usefulness of artificial intelligence (AI) in the form of convolutional neural networks for automated measurement of lordosis on lateral cervical x-rays. We included 4546 cervical x-rays from 1674 patients. For all x-rays, a well-experienced spine surgeon labeled the caudal endplates of C2 and C7, the data for which were used as ground truth. The accuracy of AI measurements was tested by 5-fold cross-validation and by comparison with measurements obtained by 2 surgeons. The mean absolute error (MAE) of the AI model in 5-fold cross-validation was 3.6° ± 5.5° at the C2–C7 angle, and the model took 206 seconds to measure 4546 x-rays. The MAE for measurement of 416 radiographs of 168 randomly selected patients was 3.3° ± 3.8° for the AI model, 3.9° ± 3.4° for Surgeon 1, and 3.8° ± 4.7° for Surgeon 2. Thus, the AI model had a significantly smaller error than Surgeon 1, and its error was not significantly different from that of Surgeon 2.In conclusion, AI can assist in routine medical care and can be helpful in research that measures large numbers of images.
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