AI-based image analysis for the detection of normal images

1. A computer-implemented method for identifying an abnormal medical image, the method comprising:

receiving a medical image;

segmenting an anatomical structure from the medical image to define a segmented dataset;

registering the segmented dataset to a baseline dataset defining a normal anatomical structure;

classifying, by an abnormality classifier, whether the anatomical structure within the medical image as either abnormal or normal, wherein the abnormality classifier comprises a machine learning algorithm trained to distinguish between normal and abnormal versions of the anatomical structure in medical images; and

based on whether the anatomical structure can be segmented from the medical image, whether the segmented dataset can be registered to the baseline dataset, or a classification associated with the medical image output by the abnormality classifier, flagging the medical image as either normal or abnormal; and

based on whether the medical image has been flagged as normal or abnormal, controlling whether the medical image is in a worklist associated with a radiologist, wherein the worklist is associated with a software platform, the software platform comprising at least one of a picture archiving and communication system, a radiology information system, or a clinical information system.

2. The method of claim 1 , further comprising:

classifying, by a lesion-specific classifier, whether the anatomical structure exhibits a lesion, wherein the lesion-specific classifier comprises a second machine learning algorithm trained to identify the lesion in medical images; and

wherein flagging the medical image as either normal or abnormal is further based on whether the lesion-specific classifier has identified the lesion in the medical image.

3. The method of claim 2 , wherein the lesion-specific classifier comprises at least one of a support vector machine, a decisions tree, a neural network, or a Bayes' model.

4. The method of claim 1 , wherein the medical image comprises at least one of a computed tomography (CT) image, a magnetic resonance imaging image, a positron emission tomography image, a single-photon emission CT image, an X-ray image, an ultrasound image, an optical coherence tomography image, or a photoacoustic image.

5. The method of claim 1 , wherein the abnormality classifier comprises at least one of a support vector machine, a decisions tree, a neural network, or a Bayes' model.

6. A computer system for identifying an abnormal medical image, the computer system comprising:

a processor; and

a memory coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computer system to:

receive a medical image,

segment an anatomical structure from the medical image to define a segmented dataset,

register the segmented dataset to a baseline dataset defining a normal anatomical structure,

classify, by an abnormality classifier, whether the anatomical structure within the medical image as either abnormal or normal, wherein the abnormality classifier comprises a machine learning algorithm trained to distinguish between normal and abnormal versions of the anatomical structure in medical images, and

based on whether the anatomical structure can be segmented from the medical image, whether the segmented dataset can be registered to the baseline dataset, or a classification associated with the medical image output by the abnormality classifier, flagging the medical image as either normal or abnormal; and

wherein the memory stores further instructions that, when executed by the processor, cause the computer system to:

based on whether the medical image has been flagged as normal or abnormal, remove the medical image from a worklist associated with a radiologist, wherein the worklist is associated with a software platform, the software platform comprising at least one of a picture archiving and communication system, a radiology information system, or a clinical information system.

7. The computer system of claim 6 , further comprising an imaging machine, wherein the medical image is received from the imaging machine.

8. The computer system of claim 6 , wherein the abnormality classifier comprises at least one of a support vector machine, a decisions tree, a neural network, or a Bayes' model.

9. The computer system of claim 6 , wherein the memory stores further instructions that, when executed by the processor, cause the computer system to:

classify, by a lesion-specific classifier, whether the anatomical structure exhibits a lesion, wherein the lesion-specific classifier comprises a second machine learning algorithm trained to identify the lesion in medical images; and

wherein flagging the medical image as either normal or abnormal is further based on whether the lesion-specific classifier has identified the lesion in the medical image.

10. The computer system of claim 9 , wherein the lesion-specific classifier comprises at least one of a support vector machine, a decisions tree, a neural network, or a Bayes' model.

11. The computer system of claim 6 , wherein the medical image comprises at least one of a computed tomography (CT) image, a magnetic resonance imaging image, a positron emission tomography image, a single-photon emission CT image, an X-ray image, an ultrasound image, an optical coherence tomography image, or a photoacoustic image.