https://profiles.stanford.edu/bao-do

APPLICATIONS:

	Hand bone age AI Predict hand bone age. In a small test of 129 random Stanford clinical cases, AI predicted age within 12 months of the Greulich and Pyle atlas in 99.2% of cases (128/129), similar to 16bit.ai (96.9%, 125/129). Free, educational use. Charles Fang, Saif Baig, David Larson, Michael Fadell, Bao Do Try it
	Stanford Musculoskeletal MRI atlas MSK anatomy atlas, educational talks, and cases ~1.7M pageviews since 2011 Try it
	clariPACS.com Web “zero footprint” PACS for education & research Univ of Calgary Stroke, Stanford MSK & Neuroradiology Try it
	Leg length AI Automatic measurement of leg lengths and angles. Limited generalizability, trained only @Stanford. N. Larson, C. Nguyen, BH. Do, A. Kaul, A. Larson, S. Wang, E. Wang, E. Bultman, K. Stevens, J. Pai, A. Ha, R. Boutin, M. Fredericson, L. Do, C. Fang Publication. Try it
	Scoliosis AI Automatic measurement of spine curves. Limited generalizability, trained only @Stanford. Audrey Y. Ha, Bao H. Do, Adam L. Bartret, Charles X. Fang, Albert Hsiao, Amelie M. Lutz, Imon Banerjee, Geoffrey M. Riley, Daniel L. Rubin, Kathryn J. Stevens, Erin Wang, S.W., CF. Beaulieu, Brian Hurt (publication) Try it
	Automatically write essays One of my first computer programs !

PROJECTS:

	nediser GPT LLM speech to speech Radiology AI assistant. Leverage retrieval augmented generation to include Fleischner and VHA Directives. Meta AI llama3.1:8B w/ Ollama and VOSK for local, 100% offline. Charles Fang, Bao Do
	Building a VA QC AI System for Detection of Incidental Findings on Non Contrast Lung CA Screening CT QC project w/ VA WOC Radiology AI team to segment left atrium and extract max AP dimension for opportunistic detection of potential paroxysmal atrial fibrillation on lung CA screening CT. Charles Fang, Evan Takahashi, Noah Massaband, Andrew Warden, Carson Yang, Shannon Liu, Acacia Yoon, Henry Guo, Bao Do.
	Deep Learning for Automated Classification of Hip Hardware on Radiographs Classify hip status as no hardware, hardware + type, or post-infectious hip. Accuracy 97%, non-inferior to 4/5 radiologists and outperformed 1 radiologist. Ma Y, Bauer JL, Yoon AH, Beaulieu CF, Yoon L, Do BH, Fang CX. https://pubmed.ncbi.nlm.nih.gov/39266912.
	Pediatric Acetabular Index and Pelvic Positioning AI Landmark detection for estimating pitch and yaw asymmetry in positioning with automatic pAI measurements: classic acatebular and ischium methods, center edge angle, and migration index. David Larson, Charles Fang, Alaa Yousef, Elaine Liu, Shannon Liu, Michael Fadell, Amelie Lutz, Conner Lusk, Matt Van Leeuwen, YongJin Lee, Bao Do.
	Nediser QC AI for workflow efficiency, eg pre-draft reports, auto comparison. Charles Fang, Bao Do Learn
	V21 OPPE Administrator Generate case list for Professional practice evaluation for VHA. Audrey Ha, VHA WOC researcher, Charles Fang, Bao Do
	Keypoint Annotator Tool for fast annotation of keypoints for training neural networks to learn angles and distances btwn points in an image (see below, “Automatic Diagnosis of Knee Patella Malalignment…” AMIA 2020 Annual Symposium). Aryan Kaul, Bao Do.
	Report Miner VA search engine indexing VistA radiology reports at VA Palo Alto for education and clinical work. Charles Fang, Bao Do. Original Report miner article
	Expert level detection of slipped capital femoral epiphysis using artificial intelligence AI for detection of SCFE in 3 grades, using only the AP view (no frog leg). Internal test, AI 99% accurate (103/104 cases). Research only, not recommended for clinical use. Andrew Campion, Audrey Ha, Bao Do, Charles Fang, Kevin Shea, Michael Fadell. International Pediatric Radiology Congress 2021, Rome, Oct 11-15, 2021 (ipr2021.org)
	Automatic Extraction of Skeletal Maturity from Whole Body Pediatric Scoliosis X-rays. Skeletal maturity assessment plays an important role in the management of pediatric orthopedic conditions such as scoliosis, slipped capital femoral epiphysis (SCFE), and pectus. The most common methods to estimate bone age are the use of hand, shoulder, and/or pelvis x-ray. Can whole body x-rays be helpful for extracting skeletal maturity estimates (ie, Oxford stage) ? Audrey Ha, John Vorhies, Andrew Campion, Charles Fang, Michael Fadell II, Steve Dou, Safwan Halabi, David Larson, Emily Wang, YongJin Lee, Joanna Langner, Japsimran Kaur, Bao Do. Short paper publication. IEEE BIBM Conference 2020.
	Automatic Diagnosis of Knee Patella Malalignment on X-ray Using Artificial Intelligence. Measurement of the Install-Salvati, modified Install-Salvati, and Caton-Deschamps Index on knee xray exams. In use @ VA Palo Alto. Aryan Kaul, Jason Pai, Charles Fang, Ed Boas, Kathryn Stevens, Jason Saleh, Vananh Nguyen, Constance Chu, Jamie Schroder, Michelle Nguyen, Joshua Reicher, Woon Teck Yap, Amelie Lutz, Bao Do. AMIA 2020 Annual Symposium.
	Automatic Detection of Aortic Aneurysm on CT Exams Using Deep Convolutional Neural Networks. Audrey Ha, Charles Fang, Saif Baig, Bao Do. AMIA 2020 Annual Symposium, November, 2020.
	Automatic detetion of thigh sarcoma on whole body PET/CT Brief experiment for classification on PET images. Limited test 14 (9 tumors + 5 normals), AI 92.8% accurate, F1 score 0.94. Lays foundation for AI based detection, interpretatation, and reporting of PET and CT/MRI exams, research only. Not in clinical use.
	Precision diagnosis of bone tumors on radiography using deep learning and bayesian network demo AI for generating ddx of bone tumors on xray using computer vision and a bayesian probabilistic network
	Radiology mangement software for the Veteran’s Health Administration. Joshua Reicher, Payam Massaband, B. Do link License link
	Automatic Detection of Perilunate and Lunate Dislocations on Wrist Radiographs Using Deep Learning. Pridgen B, von Rabenau L, Luan A, Gu AJ, Wang DS, Langlotz C, Chang J, Do B. https://pubmed.ncbi.nlm.nih.gov/37467052/
	“Expert” level perception of ACL trauma on knee xray exams using sparse data. A.I. automatic diagnosis of a subtle x-ray finding (Segond fracture, less than 0.04% of entire area of an image) using contextual interpretation, like a radiologist. Machine was 100% accurate (F-score 1.0) in our limited 20 test samples. Science poster presentation RSNA 2018.
	Detection and characterization of peri-articular calcification using deep learning Potentially for automatic quantification and calcinosis staging.
	Automatic chondroid bone tumor detection using deep learning Can machines identify tumors such as chondroid mass ? This is a simple patch based CNN that was slow and not particularly sensitive or specific.
	Machine learning decision trees in radiology Experiment using A.I. to automatically build expert decision trees from radiology observations to partition tumor classes. 100% accuracy in limited testing but broad application (classify 10+ tumor dxs, 15+ observations per dx) was plagued w/ over-fitting due to small sample size at this time.
	IVC filter machine learning Teach computer to recognize IVC filters on x-ray images [ article ].
	Chondroid machine Adapted Stanford Bone Bayes engine to discriminate benign (enchondroma) VS malignant (chondrosarcoma) chondroid bone tumors. Preliminary performance exceeded >85% of radiologists. Do, BH, Beaulieu CF, Human VS Machine: Distinguishing Enchondroma from Chondrosarcoma with a Bayesian Network, Society of Skeletal Radiology meeting, 3/2018. Rec’d SSR “Man Vs Machine” Award, 2018.
	DeepBone Deep convolutional neural net to identify bone joints of the human body. Foundation for PACS comparison protocols, automated decision systems such bone tumor diagnosis, information retrieval, QA, etc. Dr. Vossler rec’d a Student Travel Award for Young Investigators at the RSNA 2017 Scientific Meeting.
	Stanford Bone Bayes A “learning” Bayesian network that models clinical and radiographic inputs to compute diagnosis, differential diagnosis, and probabilities. link publication
	OCAD Community of radiologists promoting the exchange of high level musculoskeletal imaging expertise. Searchable gallery of common and rare musculoskeletal disorders for decision support, teaching. Founded by Philip Tirman MD (2003). Served > 1 million images since 2010.
	Stanford Atlas of Common MSK Measurements RadLex-enabled atlas of common MSK measurements. Designed to leverage RadLex ID interoperability for automation systems. RSNA Scientific Meeting, 2010.
	ACGME Analytics To eliminate the labor intensive task of counting Radiology residency workloads at VA centers by mining VistA to generate an ACGME compliant summary of workload by CPTs. The Accreditation Council for Graduate Medical Education (ACGME) requires bi-annual case log statistical reports by Residency programs for all medical specialties. The Residency Review Committee (RRC) for Radiology recognizes 90 CPT codes in 11 categories: chest radiographs, CT body, CT angiography, image guided biopsy and drain, mammography, MRI body, MRI brain, MRI knee, PET/CT, ultrasound pelvis, and MRI spine.
	Hedge Detector Automatic extraction of uncertainty and recommendation concepts from unstructured radiology reports. Applications include for QA, follow-up, peer review, disease surveillance. Rec’d RSNA Research Trainee Prize for Scientific Paper, RSNA Scientific Meeting, 2009.
	VA Radiology Analytics Mine and summarize operations metrics for VA Hospitals using VistA radiology data. Common metrics to help identify bottlenecks in radiology clinical operations at Veteran hospitals.
	Stanford MSK MRI Atlas Free MRI atlas of musculoskeletal radiology. Has served > 600,000 pages & > 20 million+ images to users from 100+ countries since 2011. RSNA Scientific Meeting, 2010.
	RadTF Radiology report search engine, originally indexed nearly 1,000,000 cases in Stanford radiology. Shared for free to numerous academic institutions, hospitals, and individuals around the world, including deployment at Hospital de Pediatria J. P. Garrahan, a children’s hospital in Buenos Aires, Argentina (Darío Filippo MD), where it enables search of over 250,000 radiology, pathology, and surgery notes. Interesting behaviors in medical imaging search. RSNA Scientific Meeting, 2009.
	iKeyNote Audience response system w/ MCQ style interactive polling for Stanford radiology lectures.
	NLP to structure MSK MRI knee reporting Auto structured knee MRI reports from free dictation in real time. RSNA Scientific Meeting, 2010.
	NLP to detect missing concepts in MSK tumor reporting May augment self learning systems via extraction of ground truth correlation of RIS/PATHOLOGY. RSNA Scientific Meeting, 2012.
	NLP to automate quality assurance using RIS reports - Fractures Fully automatic extraction of accuracy statistics from unstructured radiology report data using NLP to compare knee x-ray reads against the follow-up MRI/CT reports as ground truth. QA, education, teaching, peer review. RSNA Scientific Meeting, 2011.
	NLP to automate quality assurance using RIS reports - Osteoporosis Automatically extract accuracy metrics for osteoporosis from radiology reports in the RIS, using NLP to compare against the follow-up DEXA reports, respectively, as ground truth. RSNA Scientific Meeting, 2011.
	NLP to derive central line usage from unstructured ICU x-ray reports Using NLP to build an ICU patient consensus and central line indwelling estimate from unstructured chest x-ray reports.
	Feedback NLP of fractures in unstructured reports of ED studies Real-time retrieval of fracture classifications and clinical pearls extracted from free dictation for decision support. Rec’d RSNA Research Trainee Prize for Scientific Paper, RSNA Scientific Meeting, 2007.
	Characterizing radiology search patterns using web analytics System to analyze how radiologists review studies using heat maps of mouse movement and magnification patterns, deployed completely via the web. Note how experienced, attending radiologists (LEFT) require fewer mouse movements and magnification compared to trainee radiology residents (RIGHT). Deployed in online simulator of call cases for residents. RSNA Scientific Meeting, 2008.
SELECTED PUBLICATIONS: Do, B. H., Wu, A. S., Maley, J., & Biswal, S. (2012). Automatic Retrieval of Bone Fracture Knowledge Using Natural Language Processing. Journal of Digital Imaging, 26(4), 709–713. http://doi.org/10.1007/s10278-012-9531-1 Do, BH, Langlotz, C, Beaulieu, CF. Bone Tumor Diagnosis Using a Naïve Bayesian Model of Demographic and Radiographic Features. Journal of Digitial Imaging. 2017 Jul 27. doi: 10.1007/s10278-017-0001-7. Ni JC, Shpanskaya K, Han M, Lee EH, Do BH, Kuo WT, Yeom KW, Wang DS. Deep-Learning for Automated Classification of Inferior Vena Cava Filter Types on Radiographs. J Vasc Interv Radiol. 2019 Sep 18. pii: S1051-0443(19)30536-6. doi: 10.1016/j.jvir.2019.05.026. Enamandram, S. Sandhu, E. Do, BH, Reicher, JJ., Beaulieu, CF. Artificial Intelligence and Machine Learning Applications in Musculoskeletal Imaging. Advances in Clinical Radiology. 28 May 2020. https://doi.org/10.1016/j.yacr.2020.05.005 Audrey Ha, John Vorhies, Andrew Campion, Charles Fang, Michael Fadell II, Steve Dou, Safwan Halabi, David Larson, Emily Wang, YongJin Lee, Joanna Langner, Japsimran Kaur, Bao Do. Automatic Extraction of Skeletal Maturity from Whole Body Pediatric Scoliosis X-rays Using Regional Proposal and Compound Scaling Convolutional Neural Networks. Short paperpublication. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 12/2020. Ha, AY., Do, BH, Bartret, AL, Fang, CX, Hsiao, A., Lutz, AM, Banerjee, I., Riley, GM., Rubin, DL., Stevens, KJ, Wang, E., Wang, W., Beaulieu, CF., Hurt, B. Automating scoliosis measurements in radiographic studies with machine learning: Comparing artificial intelligence and clinical reports. Journal of Digital Imaging, Feb 2022. https://link.springer.com/article/10.1007/s10278-022-00595-x Larson, N., Nguyen, C., Do, B., Kaul, A., Larson, A., Wang, S., Wang, E., Bultman, E., Stevens, K., Pai, J., Ha, A., Boutin, R., Fredericson, M., Do, L., Fang, C. Artificial Intelligence System for Automatic Quantitative Analysis and Radiology Reporting of Leg Length Radiographs. J Digit Imaging July 2022. https://doi.org/10.1007/s10278-022-00671-2. Pridgen B, von Rabenau L, Luan A, Gu AJ, Wang DS, Langlotz C, Chang J, Do B. Automatic Detection of Perilunate and Lunate Dislocations on Wrist Radiographs Using Deep Learning. Plast Reconstr Surg. 2024 Jun 1;153(6):1138e-1141e. doi: 10.1097/PRS.0000000000010928. Epub 2023 Jul 17. PMID: 37467052. Ma Y, Bauer JL, Yoon AH, Beaulieu CF, Yoon L, Do BH, Fang CX. Deep Learning for Automated Classification of Hip Hardware on Radiographs. J Imaging Inform Med. 2024 Sep 12. doi: 10.1007/s10278-024-01263-y. Epub ahead of print. PMID: 39266912.