Browsing by Subject "Machine learning (ML)"

Now showing 1 - 4 of 4
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    A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images
    (SpringerLink, 2021-08-17) Kathiravelu, Pradeeban; Sharma, Puneet; Sharma, Ashish; Banerjee, Imon; Trivedi, Hari; Purkayastha, Saptarshi; Sinha, Priyanshu; Cadrin‑Chenevert, Alexandre; Safdar, Nabile; Wawira Gichoya, Judy; BioHealth Informatics, School of Informatics and Computing
    Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Communications in Medicine (DICOM) framework that enables ML and processing pipelines in research clusters by efficiently retrieving images from the hospitals’ PACS and extracting the metadata from the images. We deployed Niffler at our institution (Emory Healthcare, the largest healthcare network in the state of Georgia) and retrieved data from 715 scanners spanning 12 sites, up to 350 GB/day continuously in real-time as a DICOM data stream over the past 2 years. We also used Niffler to retrieve images bulk on-demand based on user-provided filters to facilitate several research projects. This paper presents the architecture and three such use cases of Niffler. First, we executed an IVC filter detection and segmentation pipeline on abdominal radiographs in real-time, which was able to classify 989 test images with an accuracy of 96.0%. Second, we applied the Niffler Metadata Extractor to understand the operational efficiency of individual MRI systems based on calculated metrics. We benchmarked the accuracy of the calculated exam time windows by comparing Niffler against the Clinical Data Warehouse (CDW). Niffler accurately identified the scanners’ examination timeframes and idling times, whereas CDW falsely depicted several exam overlaps due to human errors. Third, with metadata extracted from the images by Niffler, we identified scanners with misconfigured time and reconfigured five scanners. Our evaluations highlight how Niffler enables real-time ML and processing pipelines in a research cluster.
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    Gradient boosting reveals spatially diverse cholesterol gene signatures in colon cancer
    (Frontiers Media, 2024-11-29) Yang, Xiuxiu; Chatterjee, Debolina; Couetil, Justin L.; Liu, Ziyu; Ardon, Valerie D.; Chen, Chao; Zhang, Jie; Huang, Kun; Johnson, Travis S.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Colon cancer (CC) is the second most common cause of cancer deaths and the fourth most prevalent cancer in the United States. Recently cholesterol metabolism has been identified as a potential therapeutic avenue due to its consistent association with tumor treatment effects and overall prognosis. We conducted differential gene analysis and KEGG pathway analysis on paired tumor and adjacent-normal samples from the TCGA Colon Adenocarcinoma project, identifying that bile secretion was the only significantly downregulated pathway. To evaluate the relationship between cholesterol metabolism and CC prognosis, we used the genes from this pathway in several statistical models like Cox proportional Hazard (CPH), Random Forest (RF), Lasso Regression (LR), and the eXtreme Gradient Boosting (XGBoost) to identify the genes which contributed highly to the predictive ability of all models, ADCY5, and SLC2A1. We demonstrate that using cholesterol metabolism genes with XGBoost models improves stratification of CC patients into low and high-risk groups compared with traditional CPH, RF and LR models. Spatial transcriptomics (ST) revealed that SLC2A1 (glucose transporter 1, GLUT1) colocalized with small blood vessels. ADCY5 localized to stromal regions in both the ST and protein immunohistochemistry. Interestingly, both these significant genes are expressed in tissues other than the tumor itself, highlighting the complex interplay between the tumor and microenvironment, and that druggable targets may be found in the ability to modify how "normal" tissue interacts with tumors.
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    Machine Learning and Artificial Intelligence in Surgical Fields
    (Springer, 2020-12) Egert, Melissa; Steward, James E.; Sundaram, Chandru P.; Urology, School of Medicine
    Artificial intelligence (AI) and machine learning (ML) have the potential to improve multiple facets of medical practice, including diagnosis of disease, surgical training, clinical outcomes, and access to healthcare. There have been various applications of this technology to surgical fields. AI and ML have been used to evaluate a surgeon's technical skill. These technologies can detect instrument motion, recognize patterns in video recordings, and track the physical motion, eye movements, and cognitive function of the surgeon. These modalities also aid in the advancement of robotic surgical training. The da Vinci Standard Surgical System developed a recording and playback system to help trainees receive tactical feedback to acquire more precision when operating. ML has shown promise in recognizing and classifying complex patterns on diagnostic images and within pathologic tissue analysis. This allows for more accurate and efficient diagnosis and treatment. Artificial neural networks are able to analyze sets of symptoms in conjunction with labs, imaging, and exam findings to determine the likelihood of a diagnosis or outcome. Telemedicine is another use of ML and AI that uses technology such as voice recognition to deliver health care remotely. Limitations include the need for large data sets to program computers to create the algorithms. There is also the potential for misclassification of data points that do not follow the typical patterns learned by the machine. As more applications of AI and ML are developed for the surgical field, further studies are needed to determine feasibility, efficacy, and cost.
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    Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations
    (Royal Society of Chemistry, 2023-02-03) Yao, Songyuan; Van, Richard; Pan, Xiaoliang; Park, Ji Hwan; Mao, Yuezhi; Pu, Jingzhi; Mei, Ye; Shao, Yihan; Chemistry and Chemical Biology, School of Science
    Inspired by the recent work from Noé and coworkers on the development of machine learning based implicit solvent model for the simulation of solvated peptides [Chen et al., J. Chem. Phys., 2021, 155, 084101], here we report another investigation of the possibility of using machine learning (ML) techniques to "derive" an implicit solvent model directly from explicit solvent molecular dynamics (MD) simulations. For alanine dipeptide, a machine learning potential (MLP) based on the DeepPot-SE representation of the molecule was trained to capture its interactions with its average solvent environment configuration (ASEC). The predicted forces on the solute deviated only by an RMSD of 0.4 kcal mol-1 Å-1 from the reference values, and the MLP-based free energy surface differed from that obtained from explicit solvent MD simulations by an RMSD of less than 0.9 kcal mol-1. Our MLP training protocol could also accurately reproduce combined quantum mechanical molecular mechanical (QM/MM) forces on the quantum mechanical (QM) solute in ASEC environment, thus enabling the development of accurate ML-based implicit solvent models for ab initio-QM MD simulations. Such ML-based implicit solvent models for QM calculations are cost-effective in both the training stage, where the use of ASEC reduces the number of data points to be labelled, and the inference stage, where the MLP can be evaluated at a relatively small additional cost on top of the QM calculation of the solute.