To be honest, I haven't heard much about applying artificial intelligence to ultrasonography.
I used to work as a clinician in hospital. I think Abdominal echo is a device that is a little more difficult for doctors to access when compared to upper gastrointestinal endoscopy.
In hospitals, ultrasound technicians have the highest level of skill in ultrasound examinations. Doctors are not very skilled. So I think the study of ultrasound should be promoted by ultrasound technicians. In addition, abdominal ultrasound is a test that varies greatly from person to person. It depends on the condition of the patient and also on the skill of the examiner. The objective evaluation is not easy compared with CT.
I have tried to apply YOLOv3 to the image of upper gastrointestinal endoscopy before. However, I have never tried abdominal echo.
I don't know much about algorithms, so it's difficult to answer your question. However, the most difficult part of a machine learning project is creating datasets, not choosing algorithms. If you already have a good dataset, that's great. If you already have a data set of abdominal echo images, you can try the algorithm in order.
I want to know your position. Are you a doctor? Are you a clinical laboratory technician? Are you a programmer or an engineer? Or an abdominal echo developer? Or a hobbyist? I hope for your success anyway.
Thank you for your good wishes to me
I am a PhD student in computer science
If you know, the student reads and collects many ideas
Some of them are subject to implementation, and some are not acceptable or rejected by the supervisor
So I'm trying to gather information about the new algorithms and read about them
About the medical data set not long ago, and I go to medical centers to collect pictures, but unfortunately most of them send me to government hospitals
I am in a bureaucratic republic. I need a lot and a lot of time at this point for that
Of course, in the event that I obtain a set of medical data, I will register it and upload it on the Internet
thank you again
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Interesting.
To be honest, I haven't heard much about applying artificial intelligence to ultrasonography.
I used to work as a clinician in hospital. I think Abdominal echo is a device that is a little more difficult for doctors to access when compared to upper gastrointestinal endoscopy.
In hospitals, ultrasound technicians have the highest level of skill in ultrasound examinations. Doctors are not very skilled. So I think the study of ultrasound should be promoted by ultrasound technicians. In addition, abdominal ultrasound is a test that varies greatly from person to person. It depends on the condition of the patient and also on the skill of the examiner. The objective evaluation is not easy compared with CT.
I have tried to apply YOLOv3 to the image of upper gastrointestinal endoscopy before. However, I have never tried abdominal echo.
I don't know much about algorithms, so it's difficult to answer your question. However, the most difficult part of a machine learning project is creating datasets, not choosing algorithms. If you already have a good dataset, that's great. If you already have a data set of abdominal echo images, you can try the algorithm in order.
I want to know your position. Are you a doctor? Are you a clinical laboratory technician? Are you a programmer or an engineer? Or an abdominal echo developer? Or a hobbyist? I hope for your success anyway.
Thank you for your good wishes to me
I am a PhD student in computer science
If you know, the student reads and collects many ideas
Some of them are subject to implementation, and some are not acceptable or rejected by the supervisor
So I'm trying to gather information about the new algorithms and read about them
About the medical data set not long ago, and I go to medical centers to collect pictures, but unfortunately most of them send me to government hospitals
I am in a bureaucratic republic. I need a lot and a lot of time at this point for that
Of course, in the event that I obtain a set of medical data, I will register it and upload it on the Internet
thank you again