US 12,424,327 B2
System and method for pulmonary embolism detection from the electrocardiogram using deep learning
Sulaiman Somani, New York, NY (US); Benjamin Glicksberg, New York, NY (US); and Girish Nadkarni, New York, NY (US)
Assigned to Icahn School of Medicine at Mount Sinai, New York, NY (US)
Filed by Icahn School of Medicine at Mount Sinai, New York, NY (US)
Filed on May 17, 2022, as Appl. No. 17/746,463.
Prior Publication US 2023/0377751 A1, Nov. 23, 2023
Int. Cl. G16H 50/30 (2018.01); G16H 50/20 (2018.01)
CPC G16H 50/30 (2018.01) [G16H 50/20 (2018.01)] 14 Claims
OG exemplary drawing
 
1. A method for generating and providing an output associated with a determined likelihood of a patient having a pulmonary embolism, the method comprising:
performing, by one or more computing devices, operations comprising:
accessing discrete patient data, including clinical and demographic information associated with the patient;
accessing electrocardiograph (ECG) waveform data obtained from examination of the patient;
computing first ECG waveform features by applying a deep learning neural network (DNN) model to the accessed ECG waveform data;
deriving second ECG waveform features by reducing a dimensionality of the first ECG waveform features;
providing the second ECG waveform features and at least some of the accessed discrete patient data as input to a multimodal fusion model;
receiving in response, from the multimodal fusion model, fusion model output representing a likelihood of the patient having a pulmonary embolism;
generating, based on the likelihood of the patient having the pulmonary embolism, an output representing a recommendation whether to order a computed tomography pulmonary angiography (CTPA) scan; and
providing the output representing the recommendation.