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Deep learning enables genetic analysis of the human thoracic aorta.

Nature genetics

Pirruccello JP, Chaffin MD, Chou EL, Fleming SJ, Lin H, Nekoui M, Khurshid S, Friedman SF, Bick AG, Arduini A, Weng LC, Choi SH, Akkad AD, Batra P, Tucker NR, Hall AW, Roselli C, Benjamin EJ, Vellarikkal SK, Gupta RM, Stegmann CM, Juric D, Stone JR, Vasan RS, Ho JE, Hoffmann U, Lubitz SA, Philippakis AA, Lindsay ME, Ellinor PT.
PMID: 34837083
Nat Genet. 2022 Jan;54(1):40-51. doi: 10.1038/s41588-021-00962-4. Epub 2021 Nov 26.

Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images...

Cite
Pirruccello JP, Chaffin MD, Chou EL, et al. Deep learning enables genetic analysis of the human thoracic aorta. Nat Genet. 2021;54(1):40-51doi: 10.1038/s41588-021-00962-4.
Pirruccello, J. P., Chaffin, M. D., Chou, E. L., Fleming, S. J., Lin, H., Nekoui, M., Khurshid, S., Friedman, S. F., Bick, A. G., Arduini, A., Weng, L. C., Choi, S. H., Akkad, A. D., Batra, P., Tucker, N. R., Hall, A. W., Roselli, C., Benjamin, E. J., Vellarikkal, S. K., Gupta, R. M., Stegmann, C. M., Juric, D., Stone, J. R., Vasan, R. S., Ho, J. E., Hoffmann, U., Lubitz, S. A., Philippakis, A. A., Lindsay, M. E., & Ellinor, P. T. (2022). Deep learning enables genetic analysis of the human thoracic aorta. Nature genetics, 54(1), 40-51. https://doi.org/10.1038/s41588-021-00962-4
Pirruccello, James P, et al. "Deep learning enables genetic analysis of the human thoracic aorta." Nature genetics vol. 54,1 (2022): 40-51. doi: https://doi.org/10.1038/s41588-021-00962-4
Pirruccello JP, Chaffin MD, Chou EL, Fleming SJ, Lin H, Nekoui M, Khurshid S, Friedman SF, Bick AG, Arduini A, Weng LC, Choi SH, Akkad AD, Batra P, Tucker NR, Hall AW, Roselli C, Benjamin EJ, Vellarikkal SK, Gupta RM, Stegmann CM, Juric D, Stone JR, Vasan RS, Ho JE, Hoffmann U, Lubitz SA, Philippakis AA, Lindsay ME, Ellinor PT. Deep learning enables genetic analysis of the human thoracic aorta. Nat Genet. 2022 Jan;54(1):40-51. doi: 10.1038/s41588-021-00962-4. Epub 2021 Nov 26. PMID: 34837083; PMCID: PMC8758523.
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Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction.

Patterns (New York, N.Y.)

Agrawal S, Klarqvist MDR, Emdin C, Patel AP, Paranjpe MD, Ellinor PT, Philippakis A, Ng K, Batra P, Khera AV.
PMID: 34950898
Patterns (N Y). 2021 Oct 04;2(12):100364. doi: 10.1016/j.patter.2021.100364. eCollection 2021 Dec 10.

Current cardiovascular risk assessment tools use a small number of predictors. Here, we study how machine learning might: (1) enable principled selection from a large multimodal set of candidate variables and (2) improve prediction of incident coronary artery disease...

Cite
Agrawal S, Klarqvist MDR, Emdin C, et al. Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction. Patterns (N Y). 2021;2(12):100364doi: 10.1016/j.patter.2021.100364.
Agrawal, S., Klarqvist, M. D. R., Emdin, C., Patel, A. P., Paranjpe, M. D., Ellinor, P. T., Philippakis, A., Ng, K., Batra, P., & Khera, A. V. (2021). Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction. Patterns (New York, N.Y.), 2(12), 100364. https://doi.org/10.1016/j.patter.2021.100364
Agrawal, Saaket, et al. "Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction." Patterns (New York, N.Y.) vol. 2,12 (2021): 100364. doi: https://doi.org/10.1016/j.patter.2021.100364
Agrawal S, Klarqvist MDR, Emdin C, Patel AP, Paranjpe MD, Ellinor PT, Philippakis A, Ng K, Batra P, Khera AV. Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction. Patterns (N Y). 2021 Oct 04;2(12):100364. doi: 10.1016/j.patter.2021.100364. eCollection 2021 Dec 10. PMID: 34950898; PMCID: PMC8672148.
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Phylogenetic analysis of SARS-CoV-2 in the Boston area highlights the role of recurrent importation and superspreading events.

medRxiv : the preprint server for health sciences

Lemieux JE, Siddle KJ, Shaw BM, Loreth C, Schaffner SF, Gladden-Young A, Adams G, Fink T, Tomkins-Tinch CH, Krasilnikova LA, DeRuff KC, Rudy M, Bauer MR, Lagerborg KA, Normandin E, Chapman SB, Reilly SK, Anahtar MN, Lin AE, Carter A, Myhrvold C, Kemball ME, Chaluvadi S, Cusick C, Flowers K, Neumann A, Cerrato F, Farhat M, Slater D, Harris JB, Branda J, Hooper D, Gaeta JM, Baggett TP, O'Connell J, Gnirke A, Lieberman TD, Philippakis A, Burns M, Brown CM, Luban J, Ryan ET, Turbett SE, LaRocque RC, Hanage WP, Gallagher GR, Madoff LC, Smole S, Pierce VM, Rosenberg E, Sabeti PC, Park DJ, Maclnnis BL.
PMID: 32869040
medRxiv. 2020 Aug 25; doi: 10.1101/2020.08.23.20178236.

SARS-CoV-2 has caused a severe, ongoing outbreak of COVID-19 in Massachusetts with 111,070 confirmed cases and 8,433 deaths as of August 1, 2020. To investigate the introduction, spread, and epidemiology of COVID-19 in the Boston area, we sequenced and...

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Lemieux JE, Siddle KJ, Shaw BM, et al. Phylogenetic analysis of SARS-CoV-2 in the Boston area highlights the role of recurrent importation and superspreading events. medRxiv. 2020;doi: 10.1101/2020.08.23.20178236.
Lemieux, J. E., Siddle, K. J., Shaw, B. M., Loreth, C., Schaffner, S. F., Gladden-Young, A., Adams, G., Fink, T., Tomkins-Tinch, C. H., Krasilnikova, L. A., DeRuff, K. C., Rudy, M., Bauer, M. R., Lagerborg, K. A., Normandin, E., Chapman, S. B., Reilly, S. K., Anahtar, M. N., Lin, A. E., Carter, A., Myhrvold, C., Kemball, M. E., Chaluvadi, S., Cusick, C., Flowers, K., Neumann, A., Cerrato, F., Farhat, M., Slater, D., Harris, J. B., Branda, J., Hooper, D., Gaeta, J. M., Baggett, T. P., O'Connell, J., Gnirke, A., Lieberman, T. D., Philippakis, A., Burns, M., Brown, C. M., Luban, J., Ryan, E. T., Turbett, S. E., LaRocque, R. C., Hanage, W. P., Gallagher, G. R., Madoff, L. C., Smole, S., Pierce, V. M., Rosenberg, E., Sabeti, P. C., Park, D. J., & Maclnnis, B. L. (2020). Phylogenetic analysis of SARS-CoV-2 in the Boston area highlights the role of recurrent importation and superspreading events. medRxiv : the preprint server for health sciences, . https://doi.org/10.1101/2020.08.23.20178236
Lemieux, Jacob E, et al. "Phylogenetic analysis of SARS-CoV-2 in the Boston area highlights the role of recurrent importation and superspreading events." medRxiv : the preprint server for health sciences vol. (2020). doi: https://doi.org/10.1101/2020.08.23.20178236
Lemieux JE, Siddle KJ, Shaw BM, Loreth C, Schaffner SF, Gladden-Young A, Adams G, Fink T, Tomkins-Tinch CH, Krasilnikova LA, DeRuff KC, Rudy M, Bauer MR, Lagerborg KA, Normandin E, Chapman SB, Reilly SK, Anahtar MN, Lin AE, Carter A, Myhrvold C, Kemball ME, Chaluvadi S, Cusick C, Flowers K, Neumann A, Cerrato F, Farhat M, Slater D, Harris JB, Branda J, Hooper D, Gaeta JM, Baggett TP, O'Connell J, Gnirke A, Lieberman TD, Philippakis A, Burns M, Brown CM, Luban J, Ryan ET, Turbett SE, LaRocque RC, Hanage WP, Gallagher GR, Madoff LC, Smole S, Pierce VM, Rosenberg E, Sabeti PC, Park DJ, Maclnnis BL. Phylogenetic analysis of SARS-CoV-2 in the Boston area highlights the role of recurrent importation and superspreading events. medRxiv. 2020 Aug 25; doi: 10.1101/2020.08.23.20178236. PMID: 32869040; PMCID: PMC7457619.
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A rare case of a giant cavernous lymphangioma of the chest wall and axilla in an adult patient.

International journal of surgery case reports

Philippakis GE, Manoloudakis N, Marinakis A.
PMID: 23276757
Int J Surg Case Rep. 2013;4(2):164-6. doi: 10.1016/j.ijscr.2012.11.009. Epub 2012 Nov 28.

INTRODUCTION: Lymphangiomas are benign lesions that are most commonly encountered in the neck of small infants, but are quite uncommon in the adult population. Their removal can be quite difficult, when they reach enormous dimensions or they develop in...

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Philippakis GE, Manoloudakis N, Marinakis A. A rare case of a giant cavernous lymphangioma of the chest wall and axilla in an adult patient. Int J Surg Case Rep. 2012;4(2):164-6doi: 10.1016/j.ijscr.2012.11.009.
Philippakis, G. E., Manoloudakis, N., & Marinakis, A. (2013). A rare case of a giant cavernous lymphangioma of the chest wall and axilla in an adult patient. International journal of surgery case reports, 4(2), 164-6. https://doi.org/10.1016/j.ijscr.2012.11.009
Philippakis, George E, et al. "A rare case of a giant cavernous lymphangioma of the chest wall and axilla in an adult patient." International journal of surgery case reports vol. 4,2 (2013): 164-6. doi: https://doi.org/10.1016/j.ijscr.2012.11.009
Philippakis GE, Manoloudakis N, Marinakis A. A rare case of a giant cavernous lymphangioma of the chest wall and axilla in an adult patient. Int J Surg Case Rep. 2013;4(2):164-6. doi: 10.1016/j.ijscr.2012.11.009. Epub 2012 Nov 28. PMID: 23276757; PMCID: PMC3540229.
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Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction.

Patterns (New York, N.Y.)

Agrawal S, Klarqvist MDR, Emdin C, Patel AP, Paranjpe MD, Ellinor PT, Philippakis A, Ng K, Batra P, Khera AV.
PMID: 34950898
Patterns (N Y). 2021 Oct 04;2(12):100364. doi: 10.1016/j.patter.2021.100364. eCollection 2021 Dec 10.

Current cardiovascular risk assessment tools use a small number of predictors. Here, we study how machine learning might: (1) enable principled selection from a large multimodal set of candidate variables and (2) improve prediction of incident coronary artery disease...

Cite
Agrawal S, Klarqvist MDR, Emdin C, et al. Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction. Patterns (N Y). 2021;2(12):100364doi: 10.1016/j.patter.2021.100364.
Agrawal, S., Klarqvist, M. D. R., Emdin, C., Patel, A. P., Paranjpe, M. D., Ellinor, P. T., Philippakis, A., Ng, K., Batra, P., & Khera, A. V. (2021). Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction. Patterns (New York, N.Y.), 2(12), 100364. https://doi.org/10.1016/j.patter.2021.100364
Agrawal, Saaket, et al. "Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction." Patterns (New York, N.Y.) vol. 2,12 (2021): 100364. doi: https://doi.org/10.1016/j.patter.2021.100364
Agrawal S, Klarqvist MDR, Emdin C, Patel AP, Paranjpe MD, Ellinor PT, Philippakis A, Ng K, Batra P, Khera AV. Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction. Patterns (N Y). 2021 Oct 04;2(12):100364. doi: 10.1016/j.patter.2021.100364. eCollection 2021 Dec 10. PMID: 34950898; PMCID: PMC8672148.
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Choroidal thickness and vessel pattern in myopic eyes with dome-shaped macula.

The British journal of ophthalmology

Negrier P, Couturier A, Gaucher D, Touhami S, Le Guern G, Tadayoni R, Gaudric A, Philippakis E.
PMID: 34183323
Br J Ophthalmol. 2021 Jun 28; doi: 10.1136/bjophthalmol-2021-319066. Epub 2021 Jun 28.

AIM: To analyse the choroidal thickness (CT) and vessel pattern of myopic patients with dome-shaped macula (DSM) and their association with the DSM axis and serous retinal detachment (SRD).METHODS: Retrospective study. The CT and vessel pattern were assessed on...

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Negrier P, Couturier A, Gaucher D, et al. Choroidal thickness and vessel pattern in myopic eyes with dome-shaped macula. Br J Ophthalmol. 2021;doi: 10.1136/bjophthalmol-2021-319066.
Negrier, P., Couturier, A., Gaucher, D., Touhami, S., Le Guern, G., Tadayoni, R., Gaudric, A., & Philippakis, E. (2021). Choroidal thickness and vessel pattern in myopic eyes with dome-shaped macula. The British journal of ophthalmology, . https://doi.org/10.1136/bjophthalmol-2021-319066
Negrier, Pierre, et al. "Choroidal thickness and vessel pattern in myopic eyes with dome-shaped macula." The British journal of ophthalmology vol. (2021). doi: https://doi.org/10.1136/bjophthalmol-2021-319066
Negrier P, Couturier A, Gaucher D, Touhami S, Le Guern G, Tadayoni R, Gaudric A, Philippakis E. Choroidal thickness and vessel pattern in myopic eyes with dome-shaped macula. Br J Ophthalmol. 2021 Jun 28; doi: 10.1136/bjophthalmol-2021-319066. Epub 2021 Jun 28. PMID: 34183323.
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ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation.

Circulation

Khurshid S, Friedman S, Reeder C, Di Achille P, Diamant N, Singh P, Harrington LX, Wang X, Al-Alusi MA, Sarma G, Foulkes AS, Ellinor PT, Anderson CD, Ho JE, Philippakis AA, Batra P, Lubitz SA.
PMID: 34743566
Circulation. 2022 Jan 11;145(2):122-133. doi: 10.1161/CIRCULATIONAHA.121.057480. Epub 2021 Nov 08.

BACKGROUND: Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF.METHODS: We...

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Khurshid S, Friedman S, Reeder C, et al. ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation. 2021;145(2):122-133doi: 10.1161/CIRCULATIONAHA.121.057480.
Khurshid, S., Friedman, S., Reeder, C., Di Achille, P., Diamant, N., Singh, P., Harrington, L. X., Wang, X., Al-Alusi, M. A., Sarma, G., Foulkes, A. S., Ellinor, P. T., Anderson, C. D., Ho, J. E., Philippakis, A. A., Batra, P., & Lubitz, S. A. (2022). ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation, 145(2), 122-133. https://doi.org/10.1161/CIRCULATIONAHA.121.057480
Khurshid, Shaan, et al. "ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation." Circulation vol. 145,2 (2022): 122-133. doi: https://doi.org/10.1161/CIRCULATIONAHA.121.057480
Khurshid S, Friedman S, Reeder C, Di Achille P, Diamant N, Singh P, Harrington LX, Wang X, Al-Alusi MA, Sarma G, Foulkes AS, Ellinor PT, Anderson CD, Ho JE, Philippakis AA, Batra P, Lubitz SA. ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation. 2022 Jan 11;145(2):122-133. doi: 10.1161/CIRCULATIONAHA.121.057480. Epub 2021 Nov 08. PMID: 34743566; PMCID: PMC8748400.
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Deep learning enables genetic analysis of the human thoracic aorta.

Nature genetics

Pirruccello JP, Chaffin MD, Chou EL, Fleming SJ, Lin H, Nekoui M, Khurshid S, Friedman SF, Bick AG, Arduini A, Weng LC, Choi SH, Akkad AD, Batra P, Tucker NR, Hall AW, Roselli C, Benjamin EJ, Vellarikkal SK, Gupta RM, Stegmann CM, Juric D, Stone JR, Vasan RS, Ho JE, Hoffmann U, Lubitz SA, Philippakis AA, Lindsay ME, Ellinor PT.
PMID: 34837083
Nat Genet. 2022 Jan;54(1):40-51. doi: 10.1038/s41588-021-00962-4. Epub 2021 Nov 26.

Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images...

Cite
Pirruccello JP, Chaffin MD, Chou EL, et al. Deep learning enables genetic analysis of the human thoracic aorta. Nat Genet. 2021;54(1):40-51doi: 10.1038/s41588-021-00962-4.
Pirruccello, J. P., Chaffin, M. D., Chou, E. L., Fleming, S. J., Lin, H., Nekoui, M., Khurshid, S., Friedman, S. F., Bick, A. G., Arduini, A., Weng, L. C., Choi, S. H., Akkad, A. D., Batra, P., Tucker, N. R., Hall, A. W., Roselli, C., Benjamin, E. J., Vellarikkal, S. K., Gupta, R. M., Stegmann, C. M., Juric, D., Stone, J. R., Vasan, R. S., Ho, J. E., Hoffmann, U., Lubitz, S. A., Philippakis, A. A., Lindsay, M. E., & Ellinor, P. T. (2022). Deep learning enables genetic analysis of the human thoracic aorta. Nature genetics, 54(1), 40-51. https://doi.org/10.1038/s41588-021-00962-4
Pirruccello, James P, et al. "Deep learning enables genetic analysis of the human thoracic aorta." Nature genetics vol. 54,1 (2022): 40-51. doi: https://doi.org/10.1038/s41588-021-00962-4
Pirruccello JP, Chaffin MD, Chou EL, Fleming SJ, Lin H, Nekoui M, Khurshid S, Friedman SF, Bick AG, Arduini A, Weng LC, Choi SH, Akkad AD, Batra P, Tucker NR, Hall AW, Roselli C, Benjamin EJ, Vellarikkal SK, Gupta RM, Stegmann CM, Juric D, Stone JR, Vasan RS, Ho JE, Hoffmann U, Lubitz SA, Philippakis AA, Lindsay ME, Ellinor PT. Deep learning enables genetic analysis of the human thoracic aorta. Nat Genet. 2022 Jan;54(1):40-51. doi: 10.1038/s41588-021-00962-4. Epub 2021 Nov 26. PMID: 34837083.
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Preoperative imaging optimized for epiretinal membrane surgery.

International journal of retina and vitreous

Philippakis E, Thouvenin R, Gattoussi S, Couturier A, Tadayoni R.
PMID: 33849642
Int J Retina Vitreous. 2021 Apr 13;7(1):32. doi: 10.1186/s40942-021-00304-w.

BACKGROUND: To compare imaging modalities for visualizing primary epiretinal membrane (ERM) with each other and with intraoperative digital images (IDI) after blue staining.METHODS: The records of consecutive patients operated for primary ERM over a 12-month period were retrospectively reviewed....

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Philippakis E, Thouvenin R, Gattoussi S, et al. Preoperative imaging optimized for epiretinal membrane surgery. Int J Retina Vitreous. 2021;7(1):32doi: 10.1186/s40942-021-00304-w.
Philippakis, E., Thouvenin, R., Gattoussi, S., Couturier, A., & Tadayoni, R. (2021). Preoperative imaging optimized for epiretinal membrane surgery. International journal of retina and vitreous, 7(1), 32. https://doi.org/10.1186/s40942-021-00304-w
Philippakis, Elise, et al. "Preoperative imaging optimized for epiretinal membrane surgery." International journal of retina and vitreous vol. 7,1 (2021): 32. doi: https://doi.org/10.1186/s40942-021-00304-w
Philippakis E, Thouvenin R, Gattoussi S, Couturier A, Tadayoni R. Preoperative imaging optimized for epiretinal membrane surgery. Int J Retina Vitreous. 2021 Apr 13;7(1):32. doi: 10.1186/s40942-021-00304-w. PMID: 33849642; PMCID: PMC8045200.
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Deep learning enables genetic analysis of the human thoracic aorta.

Nature genetics

Pirruccello JP, Chaffin MD, Chou EL, Fleming SJ, Lin H, Nekoui M, Khurshid S, Friedman SF, Bick AG, Arduini A, Weng LC, Choi SH, Akkad AD, Batra P, Tucker NR, Hall AW, Roselli C, Benjamin EJ, Vellarikkal SK, Gupta RM, Stegmann CM, Juric D, Stone JR, Vasan RS, Ho JE, Hoffmann U, Lubitz SA, Philippakis AA, Lindsay ME, Ellinor PT.
PMID: 34837083
Nat Genet. 2022 Jan;54(1):40-51. doi: 10.1038/s41588-021-00962-4. Epub 2021 Nov 26.

Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images...

Cite
Pirruccello JP, Chaffin MD, Chou EL, et al. Deep learning enables genetic analysis of the human thoracic aorta. Nat Genet. 2021;54(1):40-51doi: 10.1038/s41588-021-00962-4.
Pirruccello, J. P., Chaffin, M. D., Chou, E. L., Fleming, S. J., Lin, H., Nekoui, M., Khurshid, S., Friedman, S. F., Bick, A. G., Arduini, A., Weng, L. C., Choi, S. H., Akkad, A. D., Batra, P., Tucker, N. R., Hall, A. W., Roselli, C., Benjamin, E. J., Vellarikkal, S. K., Gupta, R. M., Stegmann, C. M., Juric, D., Stone, J. R., Vasan, R. S., Ho, J. E., Hoffmann, U., Lubitz, S. A., Philippakis, A. A., Lindsay, M. E., & Ellinor, P. T. (2022). Deep learning enables genetic analysis of the human thoracic aorta. Nature genetics, 54(1), 40-51. https://doi.org/10.1038/s41588-021-00962-4
Pirruccello, James P, et al. "Deep learning enables genetic analysis of the human thoracic aorta." Nature genetics vol. 54,1 (2022): 40-51. doi: https://doi.org/10.1038/s41588-021-00962-4
Pirruccello JP, Chaffin MD, Chou EL, Fleming SJ, Lin H, Nekoui M, Khurshid S, Friedman SF, Bick AG, Arduini A, Weng LC, Choi SH, Akkad AD, Batra P, Tucker NR, Hall AW, Roselli C, Benjamin EJ, Vellarikkal SK, Gupta RM, Stegmann CM, Juric D, Stone JR, Vasan RS, Ho JE, Hoffmann U, Lubitz SA, Philippakis AA, Lindsay ME, Ellinor PT. Deep learning enables genetic analysis of the human thoracic aorta. Nat Genet. 2022 Jan;54(1):40-51. doi: 10.1038/s41588-021-00962-4. Epub 2021 Nov 26. PMID: 34837083.
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Deep learning enables genetic analysis of the human thoracic aorta.

Nature genetics

Pirruccello JP, Chaffin MD, Chou EL, Fleming SJ, Lin H, Nekoui M, Khurshid S, Friedman SF, Bick AG, Arduini A, Weng LC, Choi SH, Akkad AD, Batra P, Tucker NR, Hall AW, Roselli C, Benjamin EJ, Vellarikkal SK, Gupta RM, Stegmann CM, Juric D, Stone JR, Vasan RS, Ho JE, Hoffmann U, Lubitz SA, Philippakis AA, Lindsay ME, Ellinor PT.
PMID: 34837083
Nat Genet. 2021 Nov 26; doi: 10.1038/s41588-021-00962-4. Epub 2021 Nov 26.

Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images...

Cite
Pirruccello JP, Chaffin MD, Chou EL, et al. Deep learning enables genetic analysis of the human thoracic aorta. Nat Genet. 2021;doi: 10.1038/s41588-021-00962-4.
Pirruccello, J. P., Chaffin, M. D., Chou, E. L., Fleming, S. J., Lin, H., Nekoui, M., Khurshid, S., Friedman, S. F., Bick, A. G., Arduini, A., Weng, L. C., Choi, S. H., Akkad, A. D., Batra, P., Tucker, N. R., Hall, A. W., Roselli, C., Benjamin, E. J., Vellarikkal, S. K., Gupta, R. M., Stegmann, C. M., Juric, D., Stone, J. R., Vasan, R. S., Ho, J. E., Hoffmann, U., Lubitz, S. A., Philippakis, A. A., Lindsay, M. E., & Ellinor, P. T. (2021). Deep learning enables genetic analysis of the human thoracic aorta. Nature genetics, . https://doi.org/10.1038/s41588-021-00962-4
Pirruccello, James P, et al. "Deep learning enables genetic analysis of the human thoracic aorta." Nature genetics vol. (2021). doi: https://doi.org/10.1038/s41588-021-00962-4
Pirruccello JP, Chaffin MD, Chou EL, Fleming SJ, Lin H, Nekoui M, Khurshid S, Friedman SF, Bick AG, Arduini A, Weng LC, Choi SH, Akkad AD, Batra P, Tucker NR, Hall AW, Roselli C, Benjamin EJ, Vellarikkal SK, Gupta RM, Stegmann CM, Juric D, Stone JR, Vasan RS, Ho JE, Hoffmann U, Lubitz SA, Philippakis AA, Lindsay ME, Ellinor PT. Deep learning enables genetic analysis of the human thoracic aorta. Nat Genet. 2021 Nov 26; doi: 10.1038/s41588-021-00962-4. Epub 2021 Nov 26. PMID: 34837083.
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Managing the Intervention Costs of Musculoskeletal Disorders in the Hospital Workplace.

IISE transactions on occupational ergonomics and human factors

Koklonis K, Sarafidis M, Vastardi M, Philippakis S, Koutsouris D.
PMID: 34569435
IISE Trans Occup Ergon Hum Factors. 2021 Jan-Mar;9(1):33-48. Epub 2021 Oct 13.

OCCUPATIONAL APPLICATIONSThis study demonstrates the applicability of multicriteria tools to solve occupational health and safety (OSH) problems related to cost management in the healthcare sector. To the best of our knowledge, this is the first work in which linear...

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Koklonis K, Sarafidis M, Vastardi M, et al. Managing the Intervention Costs of Musculoskeletal Disorders in the Hospital Workplace. IISE Trans Occup Ergon Hum Factors. 2021;9(1):33-48
Koklonis, K., Sarafidis, M., Vastardi, M., Philippakis, S., & Koutsouris, D. (2021). Managing the Intervention Costs of Musculoskeletal Disorders in the Hospital Workplace. IISE transactions on occupational ergonomics and human factors, 9(1), 33-48.
Koklonis, Kyriakos, et al. "Managing the Intervention Costs of Musculoskeletal Disorders in the Hospital Workplace." IISE transactions on occupational ergonomics and human factors vol. 9,1 (2021): 33-48.
Koklonis K, Sarafidis M, Vastardi M, Philippakis S, Koutsouris D. Managing the Intervention Costs of Musculoskeletal Disorders in the Hospital Workplace. IISE Trans Occup Ergon Hum Factors. 2021 Jan-Mar;9(1):33-48. Epub 2021 Oct 13. PMID: 34569435.
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