Methods: The ensemble AI model is designed and implemented by the Hadoop ecosystem with Kafka/NiFi, Spark/Hive, HUE, and ELK (Elasticsearch, Logstash, Kibana). Results The functional evaluation shows that the main function of this SAP architecture was operated successfully.DiscussionThe proposed model is confirmed to work well with safety mobility gateways to provide some safety applications.

Methods: The ensemble AI model is designed and implemented by the Hadoop ecosystem with Kafka/NiFi, Spark/Hive, HUE, and ELK (Elasticsearch, Logstash, Kibana). Results The functional evaluation shows that the main function of this SAP architecture was operated successfully.DiscussionThe proposed model is confirmed to work well with safety mobility gateways to provide some safety applications.

Methods: The ensemble AI model is designed and implemented by the Hadoop ecosystem with Kafka/NiFi, Spark/Hive, HUE, and ELK (Elasticsearch, Logstash, Kibana). Results The functional evaluation shows that the main function of this SAP architecture was operated successfully.DiscussionThe proposed model is confirmed to work well with safety mobility gateways to provide some safety applications.

Methods: The ensemble AI model is designed and implemented by the Hadoop ecosystem with Kafka/NiFi, Spark/Hive, HUE, and ELK (Elasticsearch, Logstash, Kibana). Results The functional evaluation shows that the main function of this SAP architecture was operated successfully.DiscussionThe proposed model is confirmed to work well with safety mobility gateways to provide some safety applications.

Tumor localization is challenging in the context of ductal carcinoma in situ (DCIS) treated with breast-conserving surgery. Conventional localization methods are generally performed under the guidance of ultrasonography or mammography and are rarely performed with magnetic resonance imaging (MRI), which is more sensitive than the aforementioned modalities in detecting DCIS. Here, we report the application of MRI-based individualized 3-dimensional (3D)-printed breast surgical guides (BSGs) for patients with breast cancer.We successfully resected indeterminate and suspicious lesions that were only detected using preoperative MRI, and the final histopathologic results confirmed DCIS with clear resection margins. MRI guidance combined with 3D-printed BSGs can be used for DCIS localization, especially for lesions easily detectable using MRI only.

Tumor localization is challenging in the context of ductal carcinoma in situ (DCIS) treated with breast-conserving surgery. Conventional localization methods are generally performed under the guidance of ultrasonography or mammography and are rarely performed with magnetic resonance imaging (MRI), which is more sensitive than the aforementioned modalities in detecting DCIS. Here, we report the application of MRI-based individualized 3-dimensional (3D)-printed breast surgical guides (BSGs) for patients with breast cancer.We successfully resected indeterminate and suspicious lesions that were only detected using preoperative MRI, and the final histopathologic results confirmed DCIS with clear resection margins. MRI guidance combined with 3D-printed BSGs can be used for DCIS localization, especially for lesions easily detectable using MRI only.