Method for predicting lung cancer development based on artificial intelligence model, and analysis device therefor

1. A method for determining a probability of developing lung cancer by using an artificial intelligence model, the method comprising:

receiving, by an analysis device, a chest F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) image for a sample;

inputting, by the analysis device, the chest F-18 FDG PET/CT image to a first classification neural network and outputting prediction information related to development of the lung cancer for the sample; and

predicting, by the analysis device, a probability of developing lung cancer for the sample based on the prediction information,

wherein the first classification neural network is trained using a chest F-18 FDG PET/CT image for a healthy person and a training image excluding a lung cancer region from a chest F-18 FDG PET/CT image for a lung cancer patient.

2. The method of claim 1 , wherein the first classification neural network is a convolution neural network (CNN) and outputs the prediction information for classifying the sample into a low-risk group or a high-risk group.

3. The method of claim 1 , further comprising generating an image including information on the probability of developing lung cancer by inputting, by the analysis device, feature information output from the first classification neural network to a decoder neural network.

4. The method of claim 1 , further comprising inputting, by the analysis device, the chest F-18 FDG PET/CT image to a detection neural network to detect a lung region in the F-18 FDG PET/CT image,

wherein the analysis device inputs the detected lung region to the first classification neural network.

5. The method of claim 1 , wherein the training image is generated by inputting the chest F-18 FDG PET/CT image for the lung cancer patient to a separate neural network and extracting a region excluding the lung cancer region from the chest F-18 FDG PET/CT image for the lung cancer patient.

6. The method of claim 1 , further comprising:

receiving, by the analysis device, omics data for the sample; and

inputting, by the analysis device, the omics data to a second classification neural network and outputting second prediction information on the probability of developing lung cancer for the sample,

wherein the analysis device predicts the probability of developing lung cancer for the sample based on the prediction information output to the first classification neural network and the second prediction information output from the second classification neural network.

7. The method of claim 1 , further comprising receiving, by the analysis device, omics data for the sample,

wherein the analysis device further inputs the omics data to the first classification neural network and outputs the prediction information, and

the first classification neural network changes a coefficient or a weight of a filter for at least one of a plurality of layers based on the omics data for the sample.

8. An analysis device comprising:

an input device configured to receive a chest F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) image for a sample;

a storage device configured to store a first classification neural network that generates prediction information on a probability of developing lung cancer using the chest F-18 FDG PET/CT image; and

a computing device configured to input the received chest F-18 FDG PET/CT image to the first classification neural network to predict a probability of a subject developing lung cancer,

wherein the first classification neural network is trained using a chest F-18 FDG PET/CT image for a healthy person and a training image excluding a lung cancer region from a chest F-18 FDG PET/CT image for a lung cancer patient.

9. The analysis device of claim 8 , wherein the first classification neural network is trained using a PET/CT image labeled with F-18 FDG.

10. The analysis device of claim 8 , wherein the first classification neural network is a convolution neural network (CNN) and outputs the prediction information for classifying the sample into a low-risk group or a high-risk group.

11. The analysis device of claim 8 , wherein the storage device further stores a detection neural network for detecting a lung region in the chest F-18 FDG PET/CT image, and

the computing device inputs the chest F-18 FDG PET/CT image to the detection neural network to detect a lung region in the chest F-18 FDG PET/CT image and inputs the detected lung region to the first classification neural network.

12. The analysis device of claim 8 , wherein the storage device further stores a decoder neural network that receives feature information output from the first classification neural network and generates an image including information on the probability of developing lung cancer, and

the computing device inputs classification information and the feature information output from the first classification neural network to the decoder neural network to generate an image including the classification information.

13. The analysis device of claim 8 , wherein the training image is generated by inputting the chest F-18 FDG PET/CT image for the lung cancer patient to a separate neural network and extracting a region excluding the lung cancer region from the chest F-18 FDG PET/CT image for the lung cancer patient.

14. The analysis device of claim 8 , wherein the input device further receives omics data for the sample,

the storage device further stores a second classification neural network that receives the omics data and outputs second prediction information on the probability of developing lung cancer for the sample, and

the computing device predicts the probability of developing lung cancer for the sample based on the prediction information output to the first classification neural network and the second prediction information output from the second classification neural network.

15. The analysis device of claim 8 , wherein the input device further receives omics data for the sample,

the computing device further inputs the omics data to the first classification neural network and outputs the prediction information, and

the first classification neural network changes a coefficient or a weight of a filter for at least one of a plurality of layers based on the omics data for the sample.

16. A method for determining a probability of developing lung cancer by using an artificial intelligence model, the method comprising:

inputting, by an analysis device, a chest F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) image for a sample to a first classification neural network and outputting first prediction information related to the development of lung cancer for the sample;

inputting, by the analysis device, a chest computed tomography (CT) image for the sample to a second classification neural network and outputting second prediction information related to the development of lung cancer for the sample; and

predicting, by the analysis device, a probability of developing lung cancer for the sample based on the first prediction information and the second prediction information,

wherein the first classification neural network is trained using a chest F-18 FDG PET image for a healthy person and a first training image excluding a lung cancer region from a chest F-18 FDG PET image for a lung cancer patient, and

the second classification neural network is trained using a chest CT image for a healthy person and a second training image excluding the lung cancer region from a chest CT image for the lung cancer patient.

17. The method of claim 16 , further comprising inputting, by the analysis device, the chest F-18 FDG PET image to a first detection neural network to detect a lung region in the chest F-18 FDG PET image, the analysis device inputting the detected lung region to the first classification neural network, and

inputting, by the analysis device, the chest CT image to a second detection neural network to detect the lung region in the chest CT image, the analysis device inputting the detected lung region in the chest CT image to the second classification neural network.

18. The method of claim 16 , wherein the first training image is generated by inputting the chest F-18 FDG PET image for the lung cancer patient to a separate neural network and extracting a region excluding the lung cancer region from the chest F-18 FDG PET image for the lung cancer patient, and

the second training image is generated by inputting the chest CT image for the lung cancer patient to a separate neural network and extracting a region excluding the lung cancer region from the chest CT image for the lung cancer patient.

19. The method of claim 16 , further comprising receiving, by the analysis device, omics data for the sample,

wherein the analysis device further inputs the omics data to the first classification neural network to output the first prediction information and further inputs the omics data to the second classification neural network to output the second prediction information.