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oncology Oncology

DeepGeni: deep generalized interpretable autoencoder elucidates gut microbiota for better cancer immunotherapy.

In Scientific reports ; h5-index 158.0

Recent studies revealed that gut microbiota modulates the response to cancer immunotherapy and fecal microbiota transplantation has clinical benefits in melanoma patients during treatment. Understanding how microbiota affects individual responses is crucial for precision oncology. However, it is challenging to identify key microbial taxa with limited data as statistical and machine learning models often lose their generalizability. In this study, DeepGeni, a deep generalized interpretable autoencoder, is proposed to improve the generalizability and interpretability of microbiome profiles by augmenting data and by introducing interpretable links in the autoencoder. DeepGeni-based machine learning classifier outperforms state-of-the-art classifier in the microbiome-driven prediction of responsiveness of melanoma patients treated with immune checkpoint inhibitors. Moreover, the interpretable links of DeepGeni elucidate the most informative microbiota associated with cancer immunotherapy response. DeepGeni not only improves microbiome-driven prediction of immune checkpoint inhibitor responsiveness but also suggests potential microbial targets for fecal microbiota transplant or probiotics improving the outcome of cancer immunotherapy.

Oh Min, Zhang Liqing

2023-Mar-21

Radiology Radiology

Ultra-low-dose CT lung screening with artificial intelligence iterative reconstruction: evaluation via automatic nodule-detection software.

In Clinical radiology

AIM : To test the feasibility of ultra-low-dose (ULD) computed tomography (CT) combined with an artificial intelligence iterative reconstruction (AIIR) algorithm for screening pulmonary nodules using computer-assisted diagnosis (CAD).

MATERIALS AND METHODS : A chest phantom with artificial pulmonary nodules was first scanned using the routine protocol and the ULD protocol (3.28 versus 0.18 mSv) to compare the image quality and to test the acceptability of the ULD CT protocol. Next, 147 lung-screening patients were enrolled prospectively, undergoing an additional ULD CT immediately after their routine CT examination for clinical validation. Images were reconstructed with filtered back-projection (FBP), hybrid iterative reconstruction (HIR), the AIIR, and were imported to the CAD software for preliminary nodule detection. Subjective image quality on the phantom was scored using a five-point scale and compared using the Mann-Whitney U-test. Nodule detection using CAD was evaluated for ULD HIR and AIIR images using the routine dose image as reference.

RESULTS : Higher image quality was scored for AIIR than for FBP and HIR at ULD (p<0.001). As reported by CAD, 107 patients were presented with more than five nodules on routine dose images and were chosen to represent the challenging cases at an early stage of pulmonary disease. Among such, the performance of nodule detection by CAD on ULD HIR and AIIR images was 75.2% and 92.2% of the routine dose image, respectively.

CONCLUSION : Combined with AIIR, it was feasible to use an ULD CT protocol with 95% dose reduction for CAD-based screening of pulmonary nodules.

Yang L, Liu H, Han J, Xu S, Zhang G, Wang Q, Du Y, Yang F, Zhao X, Shi G

2023-Feb-03

Surgery Surgery

Using the Field Artificial Intelligence Triage (FAIT) tool to predict hospital critical care resource utilization in patients with truncal gunshot wounds.

In American journal of surgery

BACKGROUND : Tiered trauma triage systems have resulted in a significant mortality reduction, but models have remained unchanged. The aim of this study was to develop and test an artificial intelligence algorithm to predict critical care resource utilization.

METHODS : We queried the ACS-TQIP 2017-18 database for truncal gunshot wounds(GSW). An information-aware deep neural network (DNN-IAD) model was trained to predict ICU admission and need for mechanical ventilation (MV). Input variables included demographics, comorbidities, vital signs, and external injuries. The model's performance was assessed using the area under receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC).

RESULTS : For the ICU admission analysis, we included 39,916 patients. For the MV need analysis, 39,591 patients were included. Median (IQR) age was 27 (22,36). AUROC and AUPRC for predicting ICU need were 84.8 ± 0.5 and 75.4 ± 0.5, and the AUROC and AUPRC for MV need were 86.8 ± 0.5 and 72.5 ± 0.6.

CONCLUSIONS : Our model predicts hospital utilization outcomes in patients with truncal GSW with high accuracy, allowing early resource mobilization and rapid triage decisions in hospitals with capacity issues and austere environments.

Alser Osaid, Dorken-Gallastegi Ander, Proaño-Zamudio Jefferson A, Nederpelt Charlie, Mokhtari Ava K, Mashbari Hassan, Tsiligkaridis Theodoros, Saillant Noelle N

2023-Mar-17

AI, Gunshot, Machine learning, Resource utilization, Triage

General General

High-performance pediatric surgical risk calculator: A novel algorithm based on machine learning and pediatric NSQIP data.

In American journal of surgery

BACKGROUNDS : New methods such as machine learning could provide accurate predictions with little statistical assumptions. We seek to develop prediction model of pediatric surgical complications based on pediatric National Surgical Quality Improvement Program(NSQIP).

METHODS : All 2012-2018 pediatric-NSQIP procedures were reviewed. Primary outcome was defined as 30-day post-operative morbidity/mortality. Morbidity was further classified as any, major and minor. Models were developed using 2012-2017 data. 2018 data was used as independent performance evaluation.

RESULTS : 431,148 patients were included in the 2012-2017 training and 108,604 were included in the 2018 testing set. Our prediction models had high performance in mortality prediction at 0.94 AUC in testing set. Our models outperformed ACS-NSQIP Calculator in all categories for morbidity (0.90 AUC for major, 0.86 AUC for any, 0.69 AUC in minor complications).

CONCLUSIONS : We developed a high-performing pediatric surgical risk prediction model. This powerful tool could potentially be used to improve the surgical care quality.

Bertsimas Dimitris, Li Michael, Zhang Nova, Estrada Carlos, Scott Wang Hsin-Hsiao

2023-Mar-13

Machine learning, Pediatric surgical risk, Personalized care, Prediction model

Pathology Pathology

Artificial Intelligence in Pediatric Endoscopy: Current Status and Future Applications.

In Gastrointestinal endoscopy clinics of North America

The application of artificial intelligence (AI) has great promise for improving pediatric endoscopy. The majority of preclinical studies have been undertaken in adults, with the greatest progress being made in the context of colorectal cancer screening and surveillance. This development has only been possible with advances in deep learning, like the convolutional neural network model, which has enabled real-time detection of pathology. Comparatively, the majority of deep learning systems developed in inflammatory bowel disease have focused on predicting disease severity and were developed using still images rather than videos. The application of AI to pediatric endoscopy is in its infancy, thus providing an opportunity to develop clinically meaningful and fair systems that do not perpetuate societal biases. In this review, we provide an overview of AI, summarize the advances of AI in endoscopy, and describe its potential application to pediatric endoscopic practice and education.

Dhaliwal Jasbir, Walsh Catharine M

2023-Apr

Artificial intelligence, Artificial neural networks, CADe, CADx, Computer-aided diagnosis, Convolutional neural network, Deep learning, Pediatric gastrointestinal endoscopy

Surgery Surgery

Interpretation and Use of Applied/Operational Machine Learning and Artificial Intelligence in Surgery.

In The Surgical clinics of North America

Applications for artificial intelligence (AI) and machine learning in surgery include image interpretation, data summarization, automated narrative construction, trajectory and risk prediction, and operative navigation and robotics. The pace of development has been exponential, and some AI applications are working well. However, demonstrations of clinical utility, validity, and equity have lagged algorithm development and limited widespread adoption of AI into clinical practice. Outdated computing infrastructure and regulatory challenges which promote data silos are key barriers. Multidisciplinary teams will be needed to address these challenges and to build AI systems that are relevant, equitable, and dynamic.

Douglas Molly J, Callcut Rachel, Celi Leo Anthony, Merchant Nirav

2023-Apr

Artificial intelligence (AI), Augmented reality (AR), Computer vision, Computer-aided diagnosis, Deep learning, Machine learning (ML), Prediction, Surgery