Neuroimaging (EEG and fMRI) data obtained for 26 years at NPİstanbul Hospital were analyzed at the application and research centers of Üsküdar University and the BraiNP/NP Model was created. The model, in which Artificial Intelligence (AI) algorithms are used, provides preliminary diagnosis of different psychiatric diseases.BraiNP's Prof. Dr. Stating that it was developed under the consultancy of Nevzat Tarhan and made available via the web interface at npmodel.com, Head of Software Engineering Department Prof. Dr. Türker Tekin Ergüzel said, “BraiNP in its current form provides high accuracy with Transcranial magnetic stimulation (TMS) response prediction models in Obsessive Compulsive Disorder (OCD), healthy control, unipolar – bipolar and depression.”Üsküdar University Rector Advisor, Faculty of Engineering and Natural Sciences (MDBF) Software Engineering Department Head Prof. Dr. Turker Tekin Ergüzel, Prof. Dr. He gave information about the BraiNP/NP Model developed under the consultancy of Nevzat Tarhan.Neuroimaging data collected since 1998 classified with artificial intelligenceProf. Dr. Türker Tekin Ergüzel gave information about the system called BraiNP or NP Model and said: “NP Model has been used in the application and research of Üsküdar University with its international knowledge in the diagnosis and treatment of psychiatric diseases since its establishment in 1998, using the neuroimaging (EEG and fMRI) data collected at NPİstanbul Hospital. "It is a model with high predictive ability, developed by analyzing in centers and using Artificial Intelligence (AI) algorithms in all processes, for the preliminary diagnosis classification of different psychiatric diseases or prediction of treatment outcome."Aim; Feeding the collected data into the healthcare systemProf. Dr. Ergüzel stated the goal of the model as follows: "This model aims to ensure that the prediction models previously carried out within NPİstanbul and Üsküdar University are not limited to scientific publications, and that the collected data are brought back into the health system and that physician, client and health system resources are used effectively in the early diagnosis and treatment outcome prediction processes of diseases." he explained.“The basis of the developments is the increasing resolution of the data collected.”Stating that in the last three years, there has been a significant development in classical artificial intelligence (AI) algorithms in classifying diseases using biological markers, Ergüzel said that the basis of these developments is the increasing resolution of the collected data, the diversification of patient data sets and especially the widespread use of deep learning algorithms. He noted that new generation learning algorithms can successfully extract distinctive features in raw data in classification processes, especially, zamWith data such as EEG with high temporal resolution,zamExplaining that data such as fMRI with high spatial resolution are obtained from patients or healthy control groups, it is purified from noise with pre-processing steps, Ergüzel said, and then, thanks to the developed algorithms, these cleaned data are used by GPU computers on the Cloud to perform feature extraction. noted that it was carried out.International patent application filedProf. NP Modelin within the framework of a project supported by Üsküdar University's Scientific Research Projects. Dr. Stating that it was developed under the consultancy of Nevzat Tarhan and made available via the web interface at npmodel.com, Prof. Dr. Türker Tekin Ergüzel continued: “In its current form, BraiNP provides high accuracy with Transcranial magnetic stimulation (TMS) response prediction models in Obsessive Compulsive Disorder (OCD), healthy control, unipolar - bipolar and depression. In addition, the system is designed to make more stable predictions with new data. The model, which was developed with a preliminary diagnostic capacity in the classification of common psychiatric diseases such as depression, OCD, ADHD, bipolar disorder, trichotillomania and addiction, was designed together with the neurologist and psychiatrist at NPİstanbul Hospital, neuroscience experts and software engineers at Üsküdar University. An international patent application has been made for the model. "Patent registration is a registration of the potential and original and innovative skill of the application and is made available to NPİstanbul Hospital physicians."7 basic contributions will be made for the patient, physician and healthcare systemProf. also stated that in this way, 7 basic contributions will be made for the patient, physician and healthcare system in the short and long term. Dr. Türker Tekin Ergüzel listed them as follows: “Early Intervention: Early detection of mental health problems allows for rapid intervention and treatment that can prevent the condition from getting worse. Early intervention is generally associated with better treatment outcomes and better prognosis.Preventing Complications: Detecting mental health disorders at an early stage helps prevent the development of complications such as comorbid conditions, substance abuse or self-harming behaviors.Reduced Pain: ZamPrompt diagnosis ensures individuals receive appropriate support and treatment, reducing their suffering and improving their quality of life. It can relieve symptoms and help individuals cope better with their condition.Personalized Treatment Plans: Preliminary diagnosis provides a basis for developing personalized treatment plans tailored to the individual's specific needs and circumstances. This approach increases the likelihood of treatment effectiveness and patient satisfaction.Resource Allocation: Early diagnosis enables better allocation of resources within the healthcare system. It reduces the burden on emergency services and prevents unnecessary hospitalizations by ensuring patients receive the appropriate level of care.Training and Support: Knowing the diagnosis early allows individuals and their families to access relevant education and support services. This allows them to better understand the situation, learn coping strategies, and access community resources for ongoing support. Improved Prognosis: With early diagnosis and intervention, there is a greater chance of effectively managing symptoms and improving long-term prognosis. “It can also minimize the risk of recurrence of the disease and facilitate recovery.”“Brain-computer interfaces may be useful for post-stroke rehabilitation”Stating that in health informatics, students are provided with application and clinical opportunities on subjects such as brain stimulation, neuro-imaging laboratories and health physics, as well as BCI (Brain-Computer Interfaces) and artificial intelligence studies. Dr. Türker Tekin Ergüzel continued: “Brain-computer interfaces receive brain signals, analyze them and convert them into commands sent to output devices that perform the desired actions. The primary function of BCI is to replace or restore useful functions in patients with disabilities due to neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. Brain-computer interfaces may also be useful for rehabilitation after stroke and other disorders. Our neuroscience research, which is at the center of developments, offers researchers the opportunity to develop applications through Neuroscience Master's and PhD programs in our graduate programs.
