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Clinical application in radiology

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Dr Rati Parwani
Dr Rati Parwani is a Practising Professional BHMS Doctor having experience of 8 years in the medical field. She is a good homeopathic doctor.Her approach towards each and every patient is the utmost professional with high standards of homoeopathic practice. She has nurtured her writing skills and proves it as an asset to her professionalism. She has experience in content writing and likes her writing ethical and scientific-based Her expertise in curing chronic cases of osteoarthritis,, endocrinological disorders, lifestyle disorders, Female health problems such as acne PCOS, uterine fibroids and endometriosis, skin problems such as psoriasis and eczema, GIT troubles, Respiratory issues and other ailments . Her expertise lies in treating chronic cases. Medical Education Bachelor of homoeopathic medicine, bachelor of surgery - BHMS Medicine A+ Padmashree Dr D. Y. Patil Medical College, Hospital and Research Centre, Pune

Clinical applications of Machine learning in Radiology

It is important to diagnose the patient correctly as the medical part of the treatment in medical science. One of the most promising areas of health innovation is the application of artificial intelligence in medical imaging. The scientific evaluation of the treatment and symptoms is a must. Once we engage in the interpretation of symptoms analysis is important thereby giving the case utmost care and serious thought over. Today Diagnostic measures are gaining importance as far as treatment of the patient is concerned. Correct diagnosis can lead to correct treatment fulfilling the outcome and result of the treatment. Xray, Magnetic resonance imaging and computed tomography collectively are the most accurate forms of imaging in medical science to diagnose the internal problem.

Radiology is now moving from subjective skill to objective science. Yet they will not be replaced because radiology includes communication of diagnosis, consideration of patient’s values and preferences, medical judgement, quality assurance and education with interventional procedures. Prompted by recent development in machine learning, radiology has been given special preference in the clinical aspect of the disease.

Remember Radiologist will not be replaced by machines. The core task of radiology involves image classification, a demonstrated strength of machine learning. Diagnosing Alzheimer’s disease using the PET SCAN is essential in the application of radiology and machine learning. Interpreting images automatically initially shakes the core of the radiology profession. There is growing anxiety in the medical field that much work performed by the radiologist will be carried out more quickly and more accurately and at a lower cost and soon replacing the manpower with the machine. On the contrary machine learning is more likely to complement rather than a substitute for the radiologist.

The reason is simple; while machine learning has proven its ability to match or exceed the performance of radiologist on some tasks, these tasks resemble only a small portion of responsibilities of the radiologist. Though machine learning will have its impact on radiology in future several important questions remain unanswered.
Because machine learning will enable the radiologists to read images faster and effectively market forces are likely to drive the adoption of machine learning. the use of machine learning in radiology will save lives. Comparison of reports over the period is essential on a pathway of the success and prognosis of treatment.

Machine learning has captivated the healthcare industry as these innovative strategies become more accurate and applicable to a variety of tasks. Medical imaging is one of the richest sources of information about patients and often one of the more complex. Combining the X-ray CT SCAN MRIs and other testing modalities through extremely high-resolution images can be very challenging for the clinical professional radiologist. Machine learning is by far more accurate and is already proven to be more accurate for radiologist and pathologist looking to accelerate the prognosis of the case.

Identifying cardiovascular abnormalities.

Measuring the various structures of the heart can reveal an individual risk of cardiovascular diseases or identify the problems that maybe need to addressed through surgery or pharmacological management. For example, when the patient enters into the emergency department with a complaint of shortness of breath or chest pain, the chest radiograph is often the first imaging study that is available along with ECG.

Detecting fractures and other musculoskeletal injuries

Accidents and injuries are quite common which may lead to dislocation and fracture of the bone or joint. Injuries such as hip fractures in many old patients are difficult to analyse which leads to a reduction in mobility. Using machine learning to identify hard to see fractures, dislocations or soft tissue injuries could allow surgeons and specialist to be more confident in treatment choices.Without inquiring how the joint replacement is taken place or the bone fusion is done internally its difficult to analyse the structures and the case of the patient. If the joint replacement does not get adapted or heal in the patient its important to go for revision surgery in that case. An Xray or MRI study is equally important in the interpretation of the case.

Aiding in the diagnosis of neurological diseases

Neurological cases need special attention in a case to have a good outcome and disease diagnosis. Degenerative neurological diseases such as Parkinsonism in which the hippocampus memory is affected can be devastating diagnosis for patients, while there is currently no cure for Parkinson’s as the only syncope is the conventional line of treatment to take care of the neurotransmitters the shrinkage of the brain size through MRI study would give the accurate diagnosis of the case. and many similar neurological conditions, the accurate diagnosis could help individuals understand their highly likely outcomes and plan for long term care.

Analysing thoracic complications

Any kind of Chronic Obstructive Pulmonary Disease (COPD) and Pneumonia or Tuberculosis are two conditions that require quick interpretation and interventions from the specialist and the health care providers. Radiological images are often used to diagnose COPD and the tuberculosis of the lung and accordingly checking the accurate form of clinical study based on which the medicines can be prescribed

Screening for common cancers

Medical imaging is often used in routine, preventive screenings for cancers such as breast cancer and ovarian cancer. It is important to check whether its benign or malignant for the conclusion of the diagnosis of disease. MRI interpretation can provide accurate tools in cases of suspicious breast masses and endometriosis or ovarian cancer or PCOD (polycystic ovarian disease )

Analytics in spine study

Scoliosis and lumbar and cervical spondylosis are difficult to treat as the back pain is immense. To know the root cause it is essential to do an X-ray or MRI study which gives the exact cause and the compression of nerve root which causes pain in the lower back in case of lumbar spondylosis or pain in the nape of the neck in case of cervical spondylosis
With rapidly evolving therapeutics in surgery, it is essential need to develop better decision making in the surgical and pharmacological intervention.
Radiologist are under increasing work pressure as far as medical outcomes are concerned to help the specialist in a particular outcome of diseases and provide pharmacological and surgical intervention. There is a big impact of medical imaging in radiology to diagnose the disease with future of the imaging and thereby learn the root cause in treating the disease and rectify the prognosis and the likely outcome of the disease

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