Kolachalama laboratory research areas:
There is an urgent need to develop tools for early and accurate detection of neurodegenerative diseases. We build advanced machine learning frameworks that allow us to process multimodal data to identify precise patient-specific signatures of various disorders such as Alzheimer’s disease. We have experience dealing with large data cohorts such as the Framingham Heart Study and established several computational pipelines to efficiently process volumetric images of the brain and other modes of data and use them for further analysis.
Machine learning and image processing algorithms are transforming the way by which medical images are analyzed to uncover hidden patterns and facilitate patient diagnosis as well as to improve the delivery and effectiveness of patient care. This is especially the case in the field of digital pathology where several researchers are employing these powerful techniques to address specific questions in a spectrum of disease scenarios. We develop computational frameworks to assist the pathologist.
Our goal is to develop advanced machine learning frameworks to bring efficiency to the analysis of large-scale population studies such as Osteoarthritis Initiative (OAI) and Multicenter osteoarthritis (MOST) study. We are particularly interested to quantify structures that are responsible for pain and factors that contribute to the progression of degenerative musculoskeletal diseases.
Devices, drugs and interfaces:
We use a multidisciplinary approach to quantify device-artery interactions and the interfacial mechanisms driving the performance of endovascular devices. Previous studies by us and our collaborators have explained the role of physiologic factors in modulating spatiotemporal arterial distribution patterns for drug-eluting devices as a function of intrinsic device design, relative device position and pulsatile nature of blood flow. We have extended models simulating idealized settings of physiology to real world issues and further examine arterial tissue response that varies due to procedural settings, device composition, arterial wall ultrastructure and disease, physiologic changes within complex vascular anatomies, vascular injury and the mode of drug delivery.
- Member, Whitaker Cardiovascular Institute, Boston University
- Member, Evans Center for Interdisciplinary Biomedical Research, Boston University
- Founding Assistant Professor, Computing & Data Sciences Administration, Boston University School of Medicine, Graduate Medical Sciences
- Indian Institute of Technology, Kharagpur, India, BS
- University of Southampton, UK, PhD
- Published on 12/1/2020
Chang GH, Felson DT, Qiu S, Guermazi A, Capellini TD, Kolachalama VB. Correction to: Assessment of knee pain from MR imaging using a convolutional Siamese network. Eur Radiol. 2020 Dec; 30(12):6968. PMID: 32700018.
- Published on 7/28/2020
Xu D, Zhou F, Sun W, Chen L, Lan L, Li H, Xiao F, Li Y, Kolachalama VB, Li Y, Wang X, Xu H. Relationship Between serum SARS-CoV-2 nucleic acid(RNAemia) and Organ Damage in COVID-19 Patients: A Cohort Study. Clin Infect Dis. 2020 Jul 28. PMID: 32720678.
- Published on 6/1/2020
Qiu S, Joshi PS, Miller MI, Xue C, Zhou X, Karjadi C, Chang GH, Joshi AS, Dwyer B, Zhu S, Kaku M, Zhou Y, Alderazi YJ, Swaminathan A, Kedar S, Saint-Hilaire MH, Auerbach SH, Yuan J, Sartor EA, Au R, Kolachalama VB. Development and validation of an interpretable deep learning framework for Alzheimer's disease classification. Brain. 2020 06 01; 143(6):1920-1933. PMID: 32357201.
- Published on 4/21/2020
Azar D, Lott JT, Jabbarzadeh E, Shazly T, Kolachalama VB. Surface Modification Using Ultraviolet-Ozone Treatment Enhances Acute Drug Transfer in Drug-Coated Balloon Therapy. Langmuir. 2020 05 05; 36(17):4645-4653. PMID: 32271583.
- Published on 3/26/2020
Richard D, Liu Z, Cao J, Kiapour AM, Willen J, Yarlagadda S, Jagoda E, Kolachalama VB, Sieker JT, Chang GH, Muthuirulan P, Young M, Masson A, Konrad J, Hosseinzadeh S, Maridas DE, Rosen V, Krawetz R, Roach N, Capellini TD. Evolutionary Selection and Constraint on Human Knee Chondrocyte Regulation Impacts Osteoarthritis Risk. Cell. 2020 04 16; 181(2):362-381.e28. PMID: 32220312.
- Published on 2/13/2020
Chang GH, Felson DT, Qiu S, Guermazi A, Capellini TD, Kolachalama VB. Assessment of knee pain from MR imaging using a convolutional Siamese network. Eur Radiol. 2020 Jun; 30(6):3538-3548. PMID: 32055951.
- Published on 12/31/2019
Wollacott AM, Xue C, Qin Q, Hua J, Bohnuud T, Viswanathan K, Kolachalama VB. Quantifying the nativeness of antibody sequences using long short-term memory networks. Protein Eng Des Sel. 2019 12 31; 32(7):347-354. PMID: 31504835.
- Published on 12/28/2019
Joshi PS, Heydari M, Kannan S, Alvin Ang TF, Qin Q, Liu X, Mez J, Devine S, Au R, Kolachalama VB. Temporal association of neuropsychological test performance using unsupervised learning reveals a distinct signature of Alzheimer's disease status. Alzheimers Dement (N Y). 2019; 5:964-973. PMID: 31921970.
- Published on 10/30/2019
Walker JA, Richards S, Belghasem ME, Arinze N, Yoo SB, Tashjian JY, Whelan SA, Lee N, Kolachalama VB, Francis J, Ravid K, Sherr D, Chitalia VC. Temporal and tissue-specific activation of aryl hydrocarbon receptor in discrete mouse models of kidney disease. Kidney Int. 2020 03; 97(3):538-550. PMID: 31932072.
- Published on 9/5/2019
Azar D, Torres WM, Davis LA, Shaw T, Eberth JF, Kolachalama VB, Lessner SM, Shazly T. Geometric determinants of local hemodynamics in severe carotid artery stenosis. Comput Biol Med. 2019 11; 114:103436. PMID: 31521900.
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