Enlitic, Inc.

Other

Kevin Lyman

Li Yao

Anthony Upton

Ben Covington

Eric C Poblenz

Jordan Prosky

Devon Bernard

Lionel Lints

Brian Basham

Chris Croswhite

Keith Lui

Diogo Almeida

Jeremy Howard

Alan Liu

John R Zedlewski

Rewon Child

Shankar Rao

Jordan Francis

David Di Domenico

Alexander Rhodes

Ashwin Jadhav

Scott McKinney

Vicky Li

A medical scan quality assurance system is operable to utilize artificial intelligence to train at least one computer vision model based on a training set of medical scans. A set of medical scans are received. Quality assurance data is generated for the set of medical scans utilizing artificial intelligence by performing at least one quality assurance function on the set of medical scans by utilizing the at least one computer vision model. A first medical scan is identified in the set of medical scans to include an artifact, detected by performing the at least one quality assurance function, that is determined to obscure at least a threshold percentage of a key anatomical part based on the quality assurance data. An artifact obstruction notification indicating the first medical scan is generated for transmission to a client device for display.

Artifact detection via a medical scan quality assurance system

An inference process visualization system is configured to generate inference process visualization data for a medical scan indicating an inference process flow of plurality of sub-models applied to the medical scan and further indicating a plurality of inference data for the medical scan generated by applying the plurality of sub-models in accordance with the inference process flow. The inference process visualization system is further configured to facilitate display of the inference process visualization data via an interactive interface.

Inference process visualization system for medical scans

A multi-model medical scan analysis system is operable to generate a plurality of training sets from a plurality of medical scans. Each of a set of sub-models is generated by performing a training step on a corresponding one of the plurality of training sets of the plurality of medical scans. A set of abnormality data is generated by applying a subset of a set of inference functions on a new medical scan. The subset of the set of inference functions utilize the subset of the set of sub-models, and each of the set of abnormality data is generated as output of performing one of the subset of the set of inference functions. The multi-model medical scan analysis system is further operable to generate final abnormality data that includes a global probability indicating a probability that any abnormality is present based on the set of abnormality data.

Generating final abnormality data for medical scans based on utilizing a set of sub-models

A medical scan header standardization system is operable to determine a plurality of counts for a plurality of entries of at least one of a standard set of fields for headers of a plurality of medical images. A standard set of header entries is determined for at least one of the standard set of fields based on including ones of the entries for the each of the standard set of fields with counts of the plurality of counts that compare favorably to a threshold. One of the standard set of header entries is selected to replace an entry of a field of a header of a medical image. A computer vision model is trained utilizing a training set of images that includes the medical image and the selected one of the standard set of header entries. Inference data for at least one new medical scan is generated based on utilizing the computer vision model.

Medical scan header standardization system and methods for use therewith

A location-based medical scan analysis system is operable to generate a generic model by performing a training step on image data of a plurality of medical scans. Location-based subsets of the plurality of medical scans are generated by including ones of the plurality of medical scans with originating locations that compare favorably to location grouping criteria for the each location-based subset. A plurality of location-based models are generated by performing a fine-tuning step on the generic model, utilizing a corresponding one of the plurality of location-based subsets. Inference data is generated for a new medical scan by utilizing one of the location-based models on the new medical scan, where an originating location associated with the new medical scan compares favorably to location grouping criteria for the location-based subset utilized to generate the location-based model. The inference data is transmitted to a client device for display via a display device.

Location-based medical scan analysis system

An automatic patient recruitment system is operable generate abnormality data for medical scans by performing at least one inference function on image data of each medical scans by utilizing a computer vision model trained on a training set of medical scans. A subset of a plurality of patients is identified to be eligible for a pharmaceutical study by identifying medical scans having abnormality data that compares favorably to abnormality criteria of the pharmaceutical study. A size of the subset is compared to a minimum participant count requirement. A notification indicating the subset of the plurality of patients is transmitted based on the size of the subset comparing favorably to the minimum participant count requirement.

Automatic patient recruitment system and methods for use therewith

A model-assisted annotating system is operable to receive a first set of annotation data, corresponding to a broad type of annotation data output. A first training step is performed to train a computer vision model using the first set of annotation data. A second set of annotation data corresponding to the broad type of annotation data output is generated performing an inference function utilizing the computer vision model on medical scans. Additional annotation data further specifies the broad type of annotation data output is received. A second training step is performed to generate an updated computer vision model using set of additional annotation data. A third set of annotation data corresponding to the specified type of annotation data output is generated by performing an updated inference function utilizing the updated computer vision model on medical scans.

Model-assisted annotating system and methods for use therewith

A medical scan report labeling system is operable to transmit a medical report that includes natural language text to a first client device for display. Identified medical condition term data is received from the first client device in response. An alias mapping pair in a medical label alias database is identified by determining that a medical condition term of the alias mapping pair compares favorably to the identified medical condition term data. A medical code that corresponds to the alias mapping pair and a medical scan that corresponds to the medical report are transmitted to a second client device of an expert user for display, and accuracy data is received from the second client device in response. The medical code is mapped to the first medical scan in the medical scan database when the accuracy data indicates that the medical code compares favorably to the medical scan.

Medical report labeling system and method for use therewith

A multi-model medical scan analysis system is operable to generate a generic model by performing a training step on image data of a plurality of medical scans and corresponding labeling data. A plurality of fine-tuned models are generated by performing a fine-tuning step on the generic model. Abnormality detection data is generated for a new medical scan by utilizing the generic model. A first one of the plurality of abnormality types that is detected in the new medical scan is determined based on a corresponding one of the plurality of probability values. Additional abnormality data is generated by performing a fine-tuned inference function on the image data of the new medical scan that utilizes one of the plurality of fine-tuned models that corresponds to the first one of the plurality of abnormality types. The additional abnormality data is transmitted for display.

Multi-model medical scan analysis system using fine-tuned models

A medical scan system is operable to receive a set of labeling data corresponding to a set of medical scans from each of a set of client devices corresponding to a set of users. The set of medical scans and each set of labeling data is transmitted to an expert client device associated with an expert user, and a set of golden labeling data and a plurality of sets of correction data are received from the expert client device. A set of performance score data is generated based on the plurality of sets of correction data, and each performance score data of the set of performance score data is assigned to a corresponding one of the set of users. An updated training set that includes the set of golden labeling data is generated, and a medical scan analysis function is retrained based on the updated training set.

Medical scan labeling quality assurance system and methods for use therewith

A method includes generating a longitudinal lesion model by performing a training step on a plurality of sets of longitudinal data. Dates of medical scans of different ones of the plurality of sets of longitudinal data have relative time differences corresponding to different time spans, and each set of the plurality of sets of longitudinal data corresponds to one of a plurality of different patients. The longitudinal lesion model is utilized to perform an inference step on a received medical scan to generate, for a lesion detected in the received medical scan, a plurality of lesion change prediction data for a corresponding plurality of different projected time spans ending after the current date. At least one of the plurality of lesion change prediction data is transmitted for display.

Generating lesion change prediction data for medical scans

A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.

Re-training a model for abnormality detection in medical scans based on a re-contrasted training set

A peer-review flagging system is operable to train a computer vision model and to generate automated assessment data by performing an inference function on a first medical scan by utilizing the computer vision model. Human assessment data is generated based on a first medical report written by a medical professional in conjunction with review of the first medical scan. First consensus data is generated based on the automated assessment data, the human assessment data, and a first threshold, and the first medical scan is determined to be flagged based on the first consensus data. A second threshold is selected use in generating second consensus data for a second medical scan and a second medical report written by the medical professional in conjunction with review of the second medical scan, and is selected to be stricter than the first threshold based on determining to flag the first medical scan.

Peer-review flagging system and methods for use therewith

A label generating system operates to generate an artificial intelligence model by: training on a training data set that includes the plurality of medical scans with the corresponding global labels; generating testing global probability data by performing an inference function that utilizes the artificial intelligence model on the plurality of medical scans with the corresponding global labels, wherein the testing global probability data indicates a testing set of global probability values corresponding to the set of abnormality classes, and wherein each of the testing set of global probability values indicates a probability that a corresponding one of the set of abnormality classes is present in each of the plurality of medical scans with the corresponding global labels; comparing the testing set of global probability values to a corresponding confidence threshold for each of the plurality of medical scans selected based on the corresponding one of the global labels; generating an updated training data set by correcting ones of the plurality of medical scans having a corresponding one of the testing set of global probability values that compares unfavorably to the corresponding confidence threshold; and retraining the artificial intelligence model based on the updated training set.

AI-based label generating system and methods for use therewith

An accession number correction system is operable to determine that an accession number of a DICOM image does not link to any corresponding one of a plurality of medical reports. Medical report criteria is generated based on the DICOM image, and a set of medical reports are identified based on the medical report criteria. A computer vision model is trained from a training set of DICOM images, and inference data is generated for the DICOM image by performing at least one inference function utilizing the computer vision model. A selected one of the set of medical reports that corresponds to the DICOM image is determined based on comparing the inference data for the DICOM image to text included in at least one of the set of medical reports. Updated report header data for the selected medical report is generated, and storage of the updated report header data is facilitated.

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Kevin Lyman