AIMC Topic: Japan

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Machine learning-based mortality prediction model for heat-related illness.

Scientific reports
In this study, we aimed to develop and validate a machine learning-based mortality prediction model for hospitalized heat-related illness patients. After 2393 hospitalized patients were extracted from a multicentered heat-related illness registry in ...

Histopathological characteristics and artificial intelligence for predicting tumor mutational burden-high colorectal cancer.

Journal of gastroenterology
BACKGROUND: Tumor mutational burden-high (TMB-H), which is detected with gene panel testing, is a promising biomarker for immune checkpoint inhibitors (ICIs) in colorectal cancer (CRC). However, in clinical practice, not every patient is tested for T...

Supervised machine learning-based prediction for in-hospital pressure injury development using electronic health records: A retrospective observational cohort study in a university hospital in Japan.

International journal of nursing studies
BACKGROUND: In hospitals, nurses are responsible for pressure injury risk assessment using several kinds of risk assessment scales. However, their predictive validity is insufficient to initiate targeted preventive strategy for each patient. The use ...

Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study.

Scientific reports
Monitoring the pain intensity in critically ill patients is crucial because intense pain can cause adverse events, including poor survival rates; however, continuous pain evaluation is difficult. Vital signs have traditionally been considered ineffec...

Surgical training model and safe implementation of robotic pancreatoduodenectomy in Japan: a technical note.

World journal of surgical oncology
BACKGROUND: Growing evidence for the advantages of robotic pancreatoduodenectomy (RPD) has been demonstrated internationally. However, there has been no structured training program for RPD in Japan. Herein, we present the surgical training model of R...

Clinical practice vs. state-of-the-art research and future visions: Report on the 4D treatment planning workshop for particle therapy - Edition 2018 and 2019.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
The 4D Treatment Planning Workshop for Particle Therapy, a workshop dedicated to the treatment of moving targets with scanned particle beams, started in 2009 and since then has been organized annually. The mission of the workshop is to create an info...

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan.

Medical image analysis
The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using RT-PCR for testing. As a complimentary tool with diagnostic imaging, ches...

An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Timely intervention in growing individuals, such as brace treatment, relies on early detection of adolescent idiopathic scoliosis (AIS). To this end, several screening methods have been implemented. However, these methods have lim...

Robot-assisted partial nephrectomy versus standard laparoscopic partial nephrectomy for renal hilar tumor: A prospective multi-institutional study.

International journal of urology : official journal of the Japanese Urological Association
OBJECTIVE: To investigate whether robot-assisted partial nephrectomy compared with laparoscopic partial nephrectomy is effective for renal hilar tumor removal.

Identifying the vegetation type in Google Earth images using a convolutional neural network: a case study for Japanese bamboo forests.

BMC ecology
BACKGROUND: Classifying and mapping vegetation are crucial tasks in environmental science and natural resource management. However, these tasks are difficult because conventional methods such as field surveys are highly labor-intensive. Identificatio...