Stanford aimi

Stanford aimi

Our projects span education, clinical informatics, and mobile computing in medicine. No matter what we do, we aim to innovate user-centered solutions to healthcare IT. We're physicians. We are passionate about medicine at the intersection of emerging health information technologies. We seek to create IT solutions to improve medical education, healthcare practice, and the engagement of patients in their own care. Medicine X is a catalyst for new ideas about the future of medicine and health care. We are interested in how the design of medical information influences the way people use medical checklists in patient care. We believe that design matters. We believe that good design can improve adoption and use of …. Healthcare professionals are required to handle medical emergencies and crises. Yet, many of these healthcare providers are not always knowledgeable about teamwork and evidence-based techniques for managing a medical crisis. This web- and tablet-based course will …. Stanford StanMed is an iPad app designed to be used by Stanford medical students, residents, fellows and faculty. We intend StanMed to be used in the classroom and at the bedside. Stanford StanMed will provide …. The ACGME has recommended that anesthesia residency programs integrate the internship year with the 3-year anesthesia training period. Because not all of our Stanford Anesthesia interns participate in an integrated clinical base …. The Stanford AIM lab seeks to produce generalizable new knowledge about how best to use health information technologies to improve medical education, clinical care and research. You can find a list of our recent publications …. Learnly is an anesthesia learning ecosystem. It is a collection of courses and resources for anesthesia learning. STARTprep was an extension …. Anesthesia Illustrated was conceived as a global open access initiative to disseminate high quality educational content and learning objects to anesthesia educators around the world. Our focus is on high-quality multimedia-based visual educational content. Our …. My first time rotating through cardiac anesthesia, I realized I had entered a world quite different than my other rotations in anesthesia. While it …. Challenges and opportunities for innovation and digital disruption. The volume of new medical knowledge is outpacing …. The Stanford AIM Lab offers one of the only anesthesia informatics programs in the country and provides a flexible path to train physicians to lead healthcare innovation. More information here. Educational technology pathway. Technology innovation pathway. The AIM lab consists of a multidisciplinary group of individuals that bring unique talents and skills to our projects. This group of diverse individuals are crucial to the success of our projects. Chu studies how information technologies can be used to improve medical education and collaborates with researchers in simulation and computer science at Stanford to study how cognitive aids can improve health care outcomes. Kyle Harrison is a founding core faculty member of the AIM lab. Bassam Kadry is a core faculty member in the AIM lab. His interest in informatics centers on the use of user-obvious tools to empower users to gain insight from real-time analytics of clinical data streams in order to improve healthcare practice and quality.

Stanford aimi symposium

The people and programs comprising Stanford Radiology are world-renowned. Stanford Radiology continues to develop improved and more targeted methods for least-invasive, and compassionate cancer patient care. We push the boundaries of innovation in physics and engineering to develop cutting-edge methods for enhanced anatomic and functional imaging. Our diverse multidisciplinary teams of scientists, together with industry collaborators, are creating new methods for the early detection of cancer using molecular imaging, nanotechnology, systems biology, and artificial intelligence AI. Stanford Radiology plans to play a very important role in the development of personalized medicine by translating advances from the laboratories to the clinic for improved patient-centric care. We are recruiting outstanding faculty for the open positions listed below. We welcome your application, and we thank you for your interest! Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. The major criteria for appointment for faculty in the Clinician Educator Line shall be excellence in clinical care and teaching, as well as institutional service appropriate to the programmatic need the individual is expected to fulfill. Faculty rank will be determined by the qualifications and experience of the successful candidate. Stanford Hospitals and Clinics performs breast imaging at two diagnostic sites and at associated screening locations. The diagnostic site on Stanford University campus has recently completed expansion to create a state-of-the-art breast imaging patient environment and allow for clinical growth. Stanford is an NCI-designated Cancer Center, recognized for cancer scientific leadership, resources and research. The new facilities, to be completed inwill feature individual patient rooms, an enlarged Level-1 trauma center and Emergency Department, and new surgical, diagnostic and treatment rooms. The position will be in the clinical, teaching and service activities of the Breast Imaging Division. Candidates must be American Board of Radiology certified or eligible, have completed a minimum 6-month fellowship in Breast Imaging, be credentialed in mammography as required by MQSA, and have a California Medical license prior to appointment start date. The successful candidate must have the ability to interpret digital breast tomosynthesis DBT screening and diagnostic mammograms, breast ultrasounds and breast MRI examinations, and be able to perform core biopsies and preoperative needle localizations using x-ray, ultrasound and MRI guidance. The position typically requires a small component of general radiology coverage. Stanford Healthcare and the Department of Radiology at Stanford University are seeking a full-time radiologist for coverage of a community hospital system in the East Bay. The position will be open rank in the Clinician Educator line. Faculty rank will be determined by the qualification and experience of the successful candidate. The major criteria for appointment for Clinician Educators is excellence in the overall mix of clinical care and clinical teaching appropriate to the programmatic need the individual is expected to fulfill. This position will primarily consist of clinical duties with limited opportunities in teaching. The applicant will have an M. Fellowship training in Body Imaging or Neuroradiology is preferred. The position will be providing imaging services to a community hospital in the East Bay Area. The position will have opportunities to receive additional training in the Department of Radiology. The department subspecialty divisions will also be available for consultation. The ideal candidate will be energetic, have excellent interpersonal skills and have an interest in integrating into the referring community. Please click on the Apply Now link below to submit your curriculum vitae and a candidate statement no longer than two pages describing your clinical, teaching, and research activities and interests, if applicable. Stanford Healthcare and the Department of Radiology at Stanford University are seeking a full-time breast imaging and general radiologist for coverage of an outpatient center and a community hospital located in the East Bay. This faculty position is open rank in the Clinician Educator Line. The major criteria for appointment in the Clinician Educator Line is excellence in the overall mix of clinical care and clinical teaching appropriate to the programmatic need the individual is expected to fulfill. The majority of clinical duties will be in breast imaging with a smaller component in general radiology including participation in a general radiology call pool. Occasional presentations at tumor boards may be required. In addition to Nuclear Medicine, there will be a smaller component of general radiology with participation in on-call activities.

Stanford ai internship

It will be held at 3 p. The AIMI Center is focused on empowering interdisciplinary artificial intelligence AI research that optimizes how clinical data and images are used to promote health. The Center has over 90 affiliated faculty and works to bring technical and clinical experts together to solve clinically relevant problems by developing, evaluating, and disseminating novel computer vision methods to improve the lives of patients at Stanford Medicine and around the world. The AIMI Center initiated one of the first prospective multi-center clinical trials of an AI image analysis model in clinical practice, and more multi-center trials are in the works. One notable project is the development and validation of a deep learning algorithm, called CheXNetthat classifies clinically important abnormalities in chest radiographs at a performance level comparable to practicing radiologists. The Center is also establishing support for a development-to-implementation pipeline for AI algorithms in healthcare, which includes continuous validation of AI models in practice. Inside the Research Informatics Center. New software system saves time in compiling data tables for NIH training grants. Redesigned RMG website features a powerful new database for exploring grants and fellowships. Artificial Intelligence Town Hall on Oct. Recommended Reading. Inside the Research Informatics Center New software system saves time in compiling data tables for NIH training grants Redesigned RMG website features a powerful new database for exploring grants and fellowships.

Medical artificial intelligence course

Gastrointestinal and pancreatobiliary pathology, with major emphasis on GI and pancreatic neoplasia, inflammatory bowel disease, biodesign innovation, and the application of machine learning to digital pathology. Artificial intelligence AI algorithms continue to rival human performance on a variety of clinical tasks, while their actual impact on human diagnosticians, when incorporated into clinical workflows, remains relatively unexplored. In this study, we developed a deep learning-based assistant to help pathologists differentiate between two subtypes of primary liver cancer, hepatocellular carcinoma and cholangiocarcinoma, on hematoxylin and eosin-stained whole-slide images WSIand evaluated its effect on the diagnostic performance of 11 pathologists with varying levels of expertise. Our model achieved accuracies of 0. In the assisted state, model accuracy significantly impacted the diagnostic decisions of all 11 pathologists. Our results highlight the challenges of translating AI models into the clinical setting, and emphasize the importance of taking into account potential unintended negative consequences of model assistance when designing and testing medical AI-assistance tools. View details for DOI View details for PubMedID Understanding the molecular mechanisms that drive PDAC formation may lead to novel therapies. Concurrent Kras activation accelerated the development of cysts that resembled intraductal papillary mucinous neoplasm. Lineage-specific Arid1a deletion confirmed compartment-specific tumour-suppressive effects. RNA-seq showed that Arid1a loss induced gene networks associated with Myc activity and protein translation. In duct cells, this process appears to be associated with MYC-facilitated protein synthesis. Previous approaches to defining subtypes of colorectal carcinoma CRC and other cancers based on transcriptomes have assumed the existence of discrete subtypes. We analyze gene expression patterns of colorectal tumors from a large number of patients to test this assumption and propose an approach to identify potentially a continuum of subtypes that are present across independent studies and cohorts. Using a meta-analysis approach to identify co-expression patterns present in multiple datasets, we identify and define robust, continuously varying subtype scores to represent CRC transcriptomes. The subtype scores are consistent with established subtypes including microsatellite instability and previously proposed discrete transcriptome subtypesbut better represent overall transcriptional activity than do discrete subtypes. The scores are also better predictors of tumor location, stage, grade, and times of disease-free survival than discrete subtypes. Gene set enrichment analysis reveals that the subtype scores characterize T-cell function, inflammation response, and cyclin-dependent kinase regulation of DNA replication. We find no evidence to support discrete subtypes of the CRC transcriptome and instead propose two validated scores to better characterize a continuity of CRC transcriptomes. View details for Web of Science ID There is evidence that some cancers in patients with inflammatory bowel disease IBD develop via the serrated pathway of carcinogenesis. This study examined the clinicopathological features and outcome of IBD patients 65 with ulcerative colitis, 50 with Crohn diseaseall with at least 1 serrated polyp at endoscopy or colon resection, including the presence of synchronous and metachronous conventional neoplastic lesions dysplasia or adenocarcinomaover an average follow-up period of Conventional neoplasia was categorized as flat dysplasia low or high gradesporadic adenoma, adenoma-like dysplasia-associated lesion or mass, or adenocarcinoma. These results suggest that IBD patients both ulcerative colitis and Crohn disease patients with HP have a very low risk of developing a conventional neoplastic lesion flat dysplasia or adenocarcinoma that would warrant surgical resection. Hermansky-Pudlak syndrome HPS is a rare autosomal recessive disorder characterized by oculocutaneous hypopigmentation, platelet dysfunction, and in many cases, life-threatening pulmonary fibrosis. We report the clinical course, imaging, and postmortem findings of a year-old female with HPS-related progressive pulmonary fibrosis, highlighting the role of imaging in assessment of disease severity and prognosis. Baseline characteristics and treatment outcomes were compared between patients with and without BRAF mutations. Twenty-nine of 36 patients with BRAF mutations were smokers. There were no distinguishing clinical features between BRAF-mutant and wild-type patients. Within the BRAF cohort, patients with VE-mutated tumors had a shorter PFS to platinum-based chemotherapy compared with those with non-VE mutations, although this did not reach statistical significance 4.

Stanford echocardiography database

This call for proposals aims to stimulate and support the creation of innovative and high-impact ideas that will advance the field of medical imaging. Ideal seed grant projects can be completed in one year, are distinct from existing sponsored research, and are intended to form a basis for larger grant applications. Each proposal must have an eligible principal investigator see below who is an affiliate faculty of the AIMI Center. Co-PI's must meet the PI criteria listed below. Investigators needing assistance procuring data sets or building algorithms are encouraged to contact the AIMI Center to discuss options for staff support and identify School of Medicine resources. The research must relate directly to clinical imaging, and the objectives of the project should include an outcome that will benefit patients. Awardees must be willing to present progress of their project at AIMI meetings and events, e. Awardees are expected to acknowledge and inform the AIMI Center regarding any papers, presentations, and grants that involve the funded project. Awardees must also allow AIMI to promote the project through its website, social media, and other communication channels. A mid-year progress report will be due 6 months after the project start date, and a final progress report and presentation of results will be due at the end of the project period. The Review Committee will review applications in April Applicants progressing to the finalist round will be invited to meet with the Review Committee in mid-May to present and discuss their proposal in-person. Based on the submitted materials and interactive discussions, the Review Committee will make recommendations on funding priority. Applicants will be notified in June for funding to begin on July 1, The release of funds will occur in two phases, the first half by the project start date and the second half after review of the mid-year progress report. Initial release of funds will be contingent upon timely receipt of all required documents and verification of research regulatory approvals. Funds may be used for salary and tuition support of faculty, graduate students and other research staff, operating supplies, minor equipment items, prototyping expenses, imaging time, and travel directly associated with the research activity. The grants will not support general staff or administrative support. No indirect costs will be charged. Skip to content Skip to navigation About Opportunities. Log In. Program priorities. Online application Research proposal - no longer than 2 pages, single-spaced, 11 point type, 1 inch margins Budget and justification - use provided template in "Additional Resources" section below RMG budget is not needed at this time Biosketches for each member of the project team - use 5-page, NIH format. Maximum funding amount:. How can the funds be used? Additional resources:. Learn More About This Opportunity. Opportunity Type:. Contact: Johanna Kim, Executive Director.

Stanford medical dataset

Artificial Intelligence AI provides an unprecedented opportunity to revolutionize the practice of medicine. The collaboration aims to create smarter imaging devices that will provide more consistent, efficient, and detailed diagnostic information. This call for proposals aims to stimulate and support the creation of innovative and high-impact ideas that will advance the field of "upstream" medical imaging AI, which encompasses imaging exam selection, ordering, protocoling, exam workflow, imaging data acquisition, image reconstruction, and image processing. Each proposal must have a Principal Investigator who has a primary faculty appointment in Radiology. An optional Co-PI may be from Radiology or another department. Research associates, post-doctoral fellows, residents, clinical fellows, and graduate students are not PI-eligible but may participate in more than one application. The research must relate directly to clinical imaging, and the objectives of the project should include an outcome that will benefit patients. Awardees must be willing to present progress of their project at AIMI meetings and other departmental meetings, e. Awardees are expected to acknowledge and inform the AIMI Center regarding any papers, presentations, and grants that involve the funded project. Awardees must also allow the AIMI Center and the department to promote the project through its website, social media, and other communication channels. A mid-year progress report will be due 6 months after the project start date, and a first progress report and presentation of results will be due at the end of the first project period. Similarly, if the project continues in the second year, an 18 month and final 24 month report will be due. Projects that gain final approval will be formalized into work statements under the Stanford-GE Comprehensive Research Agreement. Note that the Internal Review Committee members are eligible to apply for the funds in this mechanism. During review, committee members who apply will be recused from review of any proposal in which they are a participant. Up to 5 new research projects may be funded for up to 2 years. Selected projects will be funded for 1 year, with an additional year of support contingent on adequate progress. Skip to content Skip to navigation About Opportunities. Log In. Program priorities. Specific Aims: Concisely state the hypothesis and the specific aims of the proposed research study. Technical Approach: Describe the experimental design, technical approach and methods, anticipated results, and potential problems and alternative approaches. Preliminary data that support the conceptual framework of the study are not required but may be included, if available. Timeline and Deliverables: Provide an outline of anticipated major milestones and deliverables, including abstracts and publications, of the proposed 2-year study. Adherence to milestones will be a key aspect during award administration, especially for continued support in the second year.

Stanford medical imagenet

Langlotz's laboratory investigates the use of deep neural networks and other machine learning technologies to help radiologists detect disease and eliminate diagnostic errors. Raised in St. Paul, Minnesota, Dr. He is a recipient of the Lee B. He has founded three healthcare information technology companies, most recently Montage Healthcare Solutions, which was acquired by Nuance Communications in My laboratory employs deep neural networks and other machine learning technologies to design algorithms that detect and classify disease on medical images. We also develop natural language processing methods that use narrative radiology reports to create large annotated image training sets for supervised machine learning experiments. The resulting systems provide real-time decision support for radiologists to improve accuracy and reduce errors. We are committed to enabling the clinical use of ideas conceived in the laboratory. When our results show potential, we evaluate their utility in the reading room or the clinic and disseminate them as open source or commercial software. The purpose of this study is to understand the effects of using a Artificial Intelligence algorithm for skeletal age estimation as a computer-aided diagnosis CADx system. In this prospective real-time study, the investigators will send de-identified hand radiographs to the Artificial Intelligence algorithm and surface the output of this algorithm to the radiologist, who will incorporate this information with their normal workflows to make a diagnosis of the patient's bone age. All radiologists involved in the study will be trained to recognize the surfaced prediction to be the output of the Artificial Intelligence algorithm. The radiologists' diagnosis will be final and considered independent to the output of the algorithm. Stanford is currently not accepting patients for this trial. For more information, please contact Safwan Halabi, M. View full details. View details for DOI View details for Web of Science ID The development of deep learning algorithms for complex tasks in digital medicine has relied on the availability of large labeled training datasets, usually containing hundreds of thousands of examples. The purpose of this study was to develop a 3D deep learning model, AppendiXNet, to detect appendicitis, one of the most common life-threatening abdominal emergencies, using a small training dataset of less than training CT exams. We explored whether pretraining the model on a large collection of natural videos would improve the performance of the model over training the model from scratch. AppendiXNet was pretrained on a large collection of YouTube videos called Kinetics, consisting of approximatelyvideo clips and annotated for one of human action classes, and then fine-tuned on a small dataset of CT scans annotated for appendicitis. We found that pretraining the 3D model on natural videos significantly improved the performance of the model from an AUC of 0. The application of deep learning to detect abnormalities on CT examinations using video pretraining could generalize effectively to other challenging cross-sectional medical imaging tasks when training data is limited. View details for PubMedID Artificial intelligence AI algorithms continue to rival human performance on a variety of clinical tasks, while their actual impact on human diagnosticians, when incorporated into clinical workflows, remains relatively unexplored. In this study, we developed a deep learning-based assistant to help pathologists differentiate between two subtypes of primary liver cancer, hepatocellular carcinoma and cholangiocarcinoma, on hematoxylin and eosin-stained whole-slide images WSIand evaluated its effect on the diagnostic performance of 11 pathologists with varying levels of expertise. Our model achieved accuracies of 0. In the assisted state, model accuracy significantly impacted the diagnostic decisions of all 11 pathologists. Our results highlight the challenges of translating AI models into the clinical setting, and emphasize the importance of taking into account potential unintended negative consequences of model assistance when designing and testing medical AI-assistance tools. In this article, the authors propose an ethical framework for using and sharing clinical data for the development of artificial intelligence AI applications. The philosophical premise is as follows: when clinical data are used to provide care, the primary purpose for acquiring the data is fulfilled. At that point, clinical data should be treated as a form of public good, to be used for the benefit of future patients. In their article, Faden et al argued that all who participate in the health care system, including patients, have a moral obligation to contribute to improving that system. The authors extend that framework to questions surrounding the secondary use of clinical data for AI applications. Specifically, the authors propose that all individuals and entities with access to clinical data become data stewards, with fiduciary or trust responsibilities to patients to carefully safeguard patient privacy, and to the public to ensure that the data are made widely available for the development of knowledge and tools to benefit future patients.

Stanford ai healthcare

The main objective of this study is to evaluate the efficacy of SRP and SRP compared to placebo in Duchenne muscular dystrophy DMD patients with out-of-frame deletion mutations amenable to skipping exon 45 and exon 53, respectively. View full details. Artificial intelligence AI continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling eg, annotations. Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability. View details for DOI View details for PubMedID View details for Web of Science ID This paper explores cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system - PEFinder and traditional machine learning methods - SVM and Adaboost. We proposed two distinct deep learning models - i CNN Word - Glove, and ii Domain phrase attention-based hierarchical recurrent neural network DPA-HNNfor synthesizing information on pulmonary emboli PE from over clinical thoracic computed tomography CT free-text radiology reports collected from four major healthcare centers. Our proposed DPA-HNN model encodes domain-dependent phrases into an attention mechanism and represents a radiology report through a hierarchical RNN structure composed of word-level, sentence-level and document-level representations. Experimental results suggest that the performance of the deep learning models that are trained on a single institutional dataset, are better than rule-based PEFinder on our multi-institutional test sets. The best F1 score for the presence of PE in an adult patient population was 0. Our work suggests feasibility of broader usage of neural network models in automated classification of multi-institutional imaging text reports for a variety of applications including evaluation of imaging utilization, imaging yield, clinical decision support tools, and as part of automated classification of large corpus for medical imaging deep learning work. Purpose To assess the ability of convolutional neural networks CNNs to enable high-performance automated binary classification of chest radiographs. Materials and Methods In a retrospective study, frontal chest radiographs obtained between and were procured, along with associated text reports and a prospective label from the attending radiologist. This data set was used to train CNNs to classify chest radiographs as normal or abnormal before evaluation on a held-out set of images hand-labeled by expert radiologists. The effects of development set size, training set size, initialization strategy, and network architecture on end performance were assessed by using standard binary classification metrics; detailed error analysis, including visualization of CNN activations, was also performed. Results Average area under the receiver operating characteristic curve AUC was 0. Averaging the CNN output score with the binary prospective label yielded the best-performing classifier, with an AUC of 0. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses. Deep learning methods, in being able to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed a deep learning model for detecting general abnormalities and specific diagnoses anterior cruciate ligament [ACL] tears and meniscal tears on knee MRI exams. We then measured the effect of providing the model's predictions to clinical experts during interpretation. The majority vote of 3 musculoskeletal radiologists established reference standard labels on an internal validation set of exams. We developed MRNet, a convolutional neural network for classifying MRI series and combined predictions from 3 series per exam using logistic regression. In detecting abnormalities, ACL tears, and meniscal tears, this model achieved area under the receiver operating characteristic curve AUC values of 0. We additionally measured the specificity, sensitivity, and accuracy of 9 clinical experts 7 board-certified general radiologists and 2 orthopedic surgeons on the internal validation set both with and without model assistance. Using a 2-sided Pearson's chi-squared test with adjustment for multiple comparisons, we found no significant differences between the performance of the model and that of unassisted general radiologists in detecting abnormalities. Using a 1-tailed t test on the change in performance metrics, we found that providing model predictions significantly increased clinical experts' specificity in identifying ACL tears p-value View details for PubMedID This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists. CheXNeXt was trained and internally validated on the ChestX-ray8 dataset, with a held-out validation set consisting of images, sampled to contain at least 50 cases of each of the original pathology labels.

Stanford machine learning internship

Accounting for data variability in multi-institutional distributed deep learning for medical imaging. J Am Med Inform Assoc. Whole slide images refelect DNA methylation patterns of human tumors. NPJ Genom Med. Automated abnormality detection in lower extremity radiographs using deep learning. Nat Mach Intell 1, — doi Human—machine partnership with artificial intelligence for chest radiograph diagnosis. J Am Coll Radiol. Cross-type biomedical named entity recognition with deep multi-task learning. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet. PLoS Med. Comparative effectiveness of convolutional neural network CNN and recurrent neural network RNN architectures for radiology text report classification. Artif Intell Med. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. Probabilistic prognostic estimates of survival in metastatic cancer patients PPES-Met utilizing free-text clinical narratives. Sci Rep Jul 3;8 1 Journal of Biomedical Informatics. Radiolog y. Performance of a deep learning neural network model in assessing skeletal maturity on pediatric hand radiographs. J Digit Imaging. Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation. Med Image Anal. A multi-view deep convolutional neural networks for lung nodule segmentation. Automated intraretinal segmentation of SD-OCT images in normal and age-related macular degeneration eyes. Biomed Opt Express. Hassanpour, S. Information extraction from multi-institutional radiology reports. Artif Intell Med Jan; Skip to content Skip to navigation. Search form Search. Stanford, CA Campus Map. Connect Twitter. STANFORD AIMI Machine Learning Mini Lecture: Confusion Matrices - Dr. Hugh Harvey, MD