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J Med Internet Res ; 22(5): e16896, 2020 05 26.
Artigo em Inglês | MEDLINE | ID: mdl-32452807


BACKGROUND: Patient follow-up is an essential part of hospital ward management. With the development of deep learning algorithms, individual follow-up assignments might be completed by artificial intelligence (AI). We developed an AI-assisted follow-up conversational agent that can simulate the human voice and select an appropriate follow-up time for quantitative, automatic, and personalized patient follow-up. Patient feedback and voice information could be collected and converted into text data automatically. OBJECTIVE: The primary objective of this study was to compare the cost-effectiveness of AI-assisted follow-up to manual follow-up of patients after surgery. The secondary objective was to compare the feedback from AI-assisted follow-up to feedback from manual follow-up. METHODS: The AI-assisted follow-up system was adopted in the Orthopedic Department of Peking Union Medical College Hospital in April 2019. A total of 270 patients were followed up through this system. Prior to that, 2656 patients were followed up by phone calls manually. Patient characteristics, telephone connection rate, follow-up rate, feedback collection rate, time spent, and feedback composition were compared between the two groups of patients. RESULTS: There was no statistically significant difference in age, gender, or disease between the two groups. There was no significant difference in telephone connection rate (manual: 2478/2656, 93.3%; AI-assisted: 249/270, 92.2%; P=.50) or successful follow-up rate (manual: 2301/2478, 92.9%; AI-assisted: 231/249, 92.8%; P=.96) between the two groups. The time spent on 100 patients in the manual follow-up group was about 9.3 hours. In contrast, the time spent on the AI-assisted follow-up was close to 0 hours. The feedback rate in the AI-assisted follow-up group was higher than that in the manual follow-up group (manual: 68/2656, 2.5%; AI-assisted: 28/270, 10.3%; P<.001). The composition of feedback was different in the two groups. Feedback from the AI-assisted follow-up group mainly included nursing, health education, and hospital environment content, while feedback from the manual follow-up group mostly included medical consultation content. CONCLUSIONS: The effectiveness of AI-assisted follow-up was not inferior to that of manual follow-up. Human resource costs are saved by AI. AI can help obtain comprehensive feedback from patients, although its depth and pertinence of communication need to be improved.

Inteligência Artificial/normas , Ortopedia/métodos , Estudos de Avaliação como Assunto , Seguimentos , Humanos , Período Pós-Operatório , Pesquisa Qualitativa
Med Sci Monit ; 26: e921611, 2020 Mar 16.
Artigo em Inglês | MEDLINE | ID: mdl-32218412


BACKGROUND Adolescent idiopathic scoliosis (AIS) is the most common spinal deformity, but its etiology is unclear. Multiple genetic mutations have been reported to be associated with AIS. MATERIAL AND METHODS We enrolled a cohort of 113 surgically treated AIS patients with available parental subjects from the Peking Union Medical College Hospital. We performed whole-exome sequencing in 10 trio families and whole-genome sequencing in 103 singleton patients. Luciferase assay was used to detect the functional alterations of candidate ESR1 and ESR2 variants. RESULTS Using a de novo strategy, a missense variant in ESR1 (c.868A>G) was selected as a candidate gene for AIS. The main Cobb angle of this patient was 41° (T6-T10). Another potential pathogenic variant in ESR2 (c.236T>C) was identified. The main curve of the patient was 45° at T10-L3. The transactivation capacities of the mutated ESR1 and ESR2 protein were both significantly decreased (p=0.026 and 0.014, respectively). CONCLUSIONS Potential pathogenic variants in ESR1 and ESR2 were identified in 113 AIS patients, suggesting that genetic mutations in ESR1/2 were associated with the risk of AIS.

J Hum Genet ; 65(3): 221-230, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31827250


Congenital scoliosis (CS) is a form of scoliosis caused by congenital vertebral malformations. Genetic predisposition has been demonstrated in CS. We previously reported that TBX6 loss-of-function causes CS in a compound heterozygous model; however, this model can explain only 10% of CS. Many monogenic and polygenic CS genes remain to be elucidated. In this study, we analyzed exome sequencing (ES) data of 615 Chinese CS from the Deciphering Disorders Involving Scoliosis and COmorbidities (DISCO) project. Cosegregation studies for 103 familial CS identified a novel heterozygous nonsense variant, c.2649G>A (p.Trp883Ter) in FBN1. The association between FBN1 and CS was then analyzed by extracting FBN1 variants from ES data of 574 sporadic CS and 828 controls; 30 novel variants were identified and prioritized for further analyses. A mutational burden test showed that the deleterious FBN1 variants were significantly enriched in CS subjects (OR = 3.9, P = 0.03 by Fisher's exact test). One missense variant, c.2613A>C (p.Leu871Phe) was recurrent in two unrelated CS subjects, and in vitro functional experiments for the variant suggest that FBN1 may contribute to CS by upregulating the transforming growth factor beta (TGF-ß) signaling. Our study expanded the phenotypic spectrum of FBN1, and provided nove insights into the genetic etiology of CS.

Anormalidades Congênitas/genética , Fibrilina-1/genética , Predisposição Genética para Doença , Escoliose/genética , Criança , Pré-Escolar , Códon sem Sentido/genética , Anormalidades Congênitas/diagnóstico por imagem , Anormalidades Congênitas/fisiopatologia , Exoma/genética , Feminino , Estudos de Associação Genética , Humanos , Lactente , Masculino , Mutação , Mutação de Sentido Incorreto/genética , Linhagem , Escoliose/diagnóstico por imagem , Escoliose/fisiopatologia , Coluna Vertebral/diagnóstico por imagem , Coluna Vertebral/fisiopatologia , Fator de Crescimento Transformador beta/genética