Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.

Study DesignA prospective radiographic study of cervical spine with congenital monosegment fusion.PurposeTo evaluate the effect of cervical synostosis on adjacent segments and the vertebral morphology.Overview of LiteratureThere are numerous clinical studies of adjacent segment disease (ASD) after monosegment surgical fusion. However, there was no report on ASD in the cervical spine with congenital monosegment synostosis.MethodsRadiograms of 52 patients, aged 5 to 90 years, with congenital monosegment synostosis (CMS) between C2 and C6, who complained of neck/shoulder discomfort or pain were studied. 51 were normally aligned and one was kyphotically aligned.ResultsSpondylosis was not found in the patients below 35 years of age. Only 12 out of 24 patients with normally aligned C2-3 synostosis had spondylosis in 19 more caudal segments, and only one at C3-4. A patient with kyphotic C2-3 had spondylolysis at C3-4. In 8 patients with C3-4 synostosis, spondylosis was found in only 9 caudal segments (4 at C4-5, 4 at C5-6, and 1 at C6-7). The caudate C4-5 disc was the most liable to degenerate in comparison with other caudate segments. Caudal corporal flaring and inwaisting of the synostotic vertebra were the features that were the most evident. In 2 of 9 C4-5 and 7 out of 10 C5-6 synostosis patients, spondylosis was found at the two adjacent cephalad and caudate segments, respectively. Only corporal inwaisting without flaring was found. In all cases, spondylosis was confined to the adjacent segments. More advanced spondylosis was found in the immediate caudal segment than the cephalad one.ConclusionsIt is concluded that spondylosis at the mobile segments in a synostotic spine is thought to be a fusion-related pathology rather than solely age-related disc degeneration. Those data suggested that CMS definitely precipitated the disc degeneration in the adjacent segments.

Background Cortical deposition of β-amyloid (Aβ) plaque is one of the main hallmarks of Alzheimer’s disease (AD). While Aβ positivity has been the main concern so far, predicting whether Aβ (−) individuals will convert to Aβ (+) has become crucial in clinical and research aspects. In this study, we aimed to develop a classifier that predicts the conversion from Aβ (−) to Aβ (+) using artificial intelligence. Methods Data were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort regarding patients who were initially Aβ (−). We developed an artificial neural network-based classifier with baseline age, gender, APOE ε4 genotype, and global and regional standardized uptake value ratios (SUVRs) from positron emission tomography. Ten times repeated 10-fold cross-validation was performed for model measurement, and the feature importance was assessed. To validate the prediction model, we recruited subjects at the Samsung Medical Center (SMC). Results A total of 229 participants (53 converters) from the ADNI dataset and a total of 40 subjects (10 converters) from the SMC dataset were included. The average area under the receiver operating characteristic values of three developed models are as follows: Model 1 (age, gender, APOE ε4) of 0.674, Model 2 (age, gender, APOE ε4, global SUVR) of 0.814, and Model 3 (age, gender, APOE ε4, global and regional SUVR) of 0.841. External validation result showed an AUROC of 0.900. Conclusion We developed prediction models regarding Aβ positivity conversion. With the growing recognition of the need for earlier intervention in AD, the results of this study are expected to contribute to the screening of early treatment candidates.

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This paper presents the integrated motion control method for an electric vehicle (EV) equipped with a front/rear steer-by-wire (SbW) system and four in-wheel motor (IWM). The proposed integrated motion control method aims to maintain stable cornering. To maintain vehicle agility and stability, the lateral force and yaw rate commands of the vehicle are generated by referring to the neutral steering characteristics. The driver’s driving force command, the lateral force command based on the bicycle model, and the yaw moment generated by the high-level controller are distributed into the driving force of each wheel and the lateral force of the front and rear wheels by the yaw moment distribution. Finally, the distributed forces are directly controlled by a low-level controller. To directly control the forces, a driving force observer and a lateral force observer were introduced via driving force estimation in the IWMs and rack force estimation in the SbW system. The control performance is verified through computer simulations.