Background: Conventional machine learning (ML) models for predicting surgical outcomes have limitations in generalizability. We explored large language models (LLMs) as scalable alternatives to conventional ML models in predicting postoperative outcomes, including in-hospital 30-day mortality, intensive care unit (ICU) admission, and acute kidney injury (AKI). Methods: This study utilized the Informative Surgical Patient for Innovative Research Environment (INSPIRE) dataset (n = 80 985) from South Korea for model development and internal validation, and the Medical Informatics Operating Room Vitals and Events Repository (MOVER) dataset (n = 6265) from the United States for external validation. The study compared three different LLMs—Generative Pre-trained Transformer [GPT]-4o, Llama-3-70B, and OpenBioLLM-70B—against MLs using various prompt engineering approaches. LLMs were evaluated with different model parameter quantizations (4-bit normalized floating point vs. 16-bit brain floating point). Results: OpenBioLLM-70B was comparable to eXtreme Gradient Boosting (XGBoost) across all tasks (in-hospital 30-day mortality: area under receiver operating characteristic curve [AUROC] 0.782 [95% CI, 0.748–0.813] vs. 0.791 [95% CI, 0.753–0.825]; ICU admission: AUROC 0.595 [95% CI, 0.581–0.609] vs. 0.594 [95% CI, 0.580–0.608]; AKI: AUROC 0.830 [95% CI, 0.802–0.855] vs. 0.823 [95% CI, 0.792–0.851]) during external validation. Open-source LLMs maintained performance with 4-bit quantization, reducing computational requirements by 75%. Conclusions The findings support the versatility and efficiency of LLMs for clinical decision support through on-premises compatibility, addressing data privacy. Further validation with diverse datasets is needed to ensure their reliability and applicability across different perioperative settings.

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

Objective: We aimed to create an efficient and valid predicting model which can estimate individuals’ brain age by quantifying their regional brain volumes. Methods: A total of 2,560 structural brain magnetic resonance imaging (MRI) scans, along with demographic and clinical data, were obtained. Pretrained deep-learning models were employed to automatically segment the MRI data, which enabled fast calculation of regional brain volumes. Brain age gaps for each subject were estimated using volumetric values from predefined 12 regions of interest (ROIs): bilateral frontal, parietal, occipital, and temporal lobes, as well as bilateral hippocampus and lateral ventricles. A larger weight was given to the ROIs having a larger mean volumetric difference between the cognitively unimpaired (CU) and cognitively impaired group including mild cognitive impairment (MCI), and dementia groups. The brain age was predicted by adding or subtracting the brain age gap to the chronological age according to the presence or absence of the atrophy region. Results: The study showed significant differences in brain age gaps among CU, MCI, and dementia groups. Furthermore, the brain age gaps exhibited significant correlations with education level and measures of cognitive function, including the clinical dementia rating sum-of-boxes and the Korean version of the Mini-Mental State Examination. Conclusion The brain age that we developed enabled fast and efficient brain age calculations, and it also reflected individual’s cognitive function and cognitive reserve. Thus, our study suggested that the brain age might be an important marker of brain health that can be used effectively in real clinical settings.

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

Background: Use of endotracheal tubes (ETTs) with appropriate size and depth can help minimize intubation-related complications in pediatric patients. Existing age-based formulae for selecting the optimal ETT size present several inaccuracies. We developed a machine learning model that predicts the optimal size and depth of ETTs in pediatric patients using demographic data, enabling clinical applications. Methods: Data from 37,057 patients younger than 12 years who underwent general anesthesia with endotracheal intubation were retrospectively analyzed. Gradient boosted regression tree (GBRT) model was developed and compared with traditional age-based formulae. Results: The GBRT model demonstrated the highest macro-averaged F1 scores of 0.502 (95% CI 0.486, 0.568) and 0.669 (95% CI 0.640, 0.694) for predicting the uncuffed and cuffed ETT size (internal diameter [ID]), outperforming the age-based formulae that yielded 0.163 (95% CI 0.140, 0.196, P < 0.001) and 0.392 (95% CI 0.378, 0.406, P < 0.001), respectively. In predicting the ETT depth (distance from tip to lip corner), the GBRT model showed the lowest mean absolute error (MAE) of 0.71 cm (95% CI 0.69, 0.72) and 0.72 cm (95% CI 0.70, 0.74) compared to the age-based formulae that showed an error of 1.18 cm (95% CI 1.16, 1.20, P < 0.001) and 1.34 cm (95% CI 1.31, 1.38, P < 0.001) for uncuffed and cuffed ETT, respectively. Conclusions The GBRT model using only demographic data accurately predicted the ETT size and depth. If these results are validated, the model may be practical for predicting optimal ETT size and depth for pediatric patients.