Compute system with head ailment diagnostic mechanism and method of operation thereof
A method of operation of a compute system includes: receiving patient images by an head ailment artificial intelligence (AI) including a multi-task decoder block; concurrently generating a scalp segmentation and a hair loss heat map from the multi-task decoder block of the head ailment AI; generating a composite hair loss image based on the hair loss heat map and a quadrant computation of the scalp segmentation; and generating a SALT score based the hair loss heat map and the composite hair loss image for displaying on a device.
1. A method of operation of a compute system comprising:
receiving patient images by a head ailment artificial intelligence (AI);
concurrently generating a scalp segmentation and a hair loss heat map by analyzing pixels of the patient images;
generating a composite hair loss image based on the hair loss heat map and a quadrant computation of the scalp segmentation, wherein the composite hair loss image extends across a border of two or more of a left side image, a right side image, a top image, and a back image; and
generating a Severity of Alopecia Tool (SALT) score based the hair loss heat map and the composite hair loss image for displaying on a device.
2. The method as claimed in claim 1 wherein receiving the patient image includes receiving the left side image, the right side image, the top image, and the back image.
3. The method as claimed in claim 1 wherein generating the SALT score includes adding a weighted percentage of a scalp area identified by the scalp segmentation.
4. The method as claimed in claim 1 wherein generating the scalp segmentation includes providing a scalp segmentation stream to be processed by analyzing the pixels in the scalp segmentation stream to identify hair loss.
5. The method as claimed in claim 1 wherein generating a hair loss heat map includes providing a heat map stream by processing four of the patient images to generate the percent hair loss of a whole head.
6. The method as claimed in claim 1 further comprising identifying a hair loss factor and scalp area from a pixel group by the head ailment AI already trained.
7. The method as claimed in claim 1 further comprising identifying a hair loss factor to differentiate four levels as normal hair with 0% hair loss, a first stage with 1-33% hair loss, a second stage with 34-66% hair loss, and a final stage with 67-100% hair loss by the head ailment AI already trained.
8. A compute system comprising:
a control circuit, including a processor, configured to:
receive patient images by a head ailment artificial intelligence (AI);
concurrently generate a scalp segmentation and a hair loss heat map by analysis of pixels of the patient images;
generate a composite hair loss image based on the hair loss heat map and a quadrant computation of the scalp segmentation, wherein the composite hair loss image extends across a border of two or more of a left side image, a right side image, a top image and a back image; and
generate a Severity of Alopecia Tool (SALT) score based the hair loss heat map and the composite hair loss image for displaying on a device.
9. The system as claimed in claim 8 wherein the control circuit configured to receive the patient image includes the left side image, the right side image, the top image, and the back image received.
10. The system as claimed in claim 8 wherein the control circuit is configured to generate the SALT score includes adding a weighted percentage of a scalp surface identified by the scalp segmentation.
11. The system as claimed in claim 8 wherein the control circuit is configured to generate the scalp segmentation includes a scalp segmentation stream provided to be processed by analyzing the pixels in the scalp segmentation stream to identify hair loss.
12. The system as claimed in claim 8 wherein the control circuit is configured to generate the hair loss heat map includes a heat map stream provided to be processed by processing four of the patient images to generate the percent hair loss of a whole head.
13. The system as claimed in claim 8 wherein the control circuit is further configured to identify a hair loss factor and scalp area from a pixel group by the head ailment AI already trained.
14. The system as claimed in claim 8 wherein the control circuit is further configured to identify a hair loss factor to differentiate four levels as normal hair with 0% hair loss, a first stage with 1-33% hair loss, a second stage with 34-66% hair loss, and a final stage with 67-100% hair loss by the head ailment AI already trained.
15. A non-transitory computer readable medium including instructions executable by a control circuit for a compute system performing functions comprising:
receiving patient images by a head ailment intelligence (AI);
concurrently generating a scalp segmentation and a hair loss heat map by analyzing pixels of the patient images;
generating a composite hair loss image based on the hair loss heat map and a quadrant computation of the scalp segmentation, wherein the composite hair loss image extends across a border of two or more of a left side image, a right side image, a top image, and a back image; and
generating a Severity of Alopecia Tool (SALT) score based the hair loss heat map and the composite hair loss image for displaying on a device.
16. The non-transitory computer readable medium as claimed in claim 15 wherein receiving the patient image includes receiving the left side image, the right side image, the top image, and the back image.
17. The non-transitory computer readable medium as claimed in claim 15 wherein generating the SALT score includes adding a weighted percentage of a scalp surface identified by the scalp segmentation.
18. The non-transitory computer readable medium as claimed in claim 15 wherein generating the scalp segmentation includes providing a scalp segmentation stream to be processed by analyzing the pixels in the scalp segmentation stream to identify hair loss.
19. The non-transitory computer readable medium as claimed in claim 15 further comprising identifying a hair loss factor and scalp area from a pixel group by the head ailment AI already trained.
20. The non-transitory computer readable medium as claimed in claim 15 further comprising identifying a hair loss factor to differentiate four levels as normal hair with 0% hair loss, a first stage with 1-33% hair loss, a second stage with 34-66% hair loss, and a final stage with 67-100% hair loss by the head ailment AI already trained.