Artificial Intelligence Powered Innovation: A New Era in Psychiatric Diagnosis with BraiNP

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NPİSTANBUL Hospital has pioneered a groundbreaking innovation in the diagnosis and treatment of psychiatric disorders with the artificial intelligence powered model BraiNP, developed in collaboration with Üsküdar University. This internationally developed model aims to accelerate clinical decision-making processes and increase patient satisfaction through personalized treatments.

The artificial intelligence based “BraiNP” model, developed through the scientific collaboration of NPİSTANBUL Hospital and Üsküdar University, marks a major advancement in the diagnosis and treatment of psychiatric conditions. With international patent application filed, this innovative model provides high-accuracy preliminary diagnoses and treatment predictions, contributing significantly to both the healthcare system and individualized treatment processes.

What is BraiNP?

“BraiNP” is an advanced model created using the psychiatric data accumulated by the NP Model since 1998. It analyzes neuroimaging data such as EEG and fMRI through artificial intelligence algorithms. Resulting from scientific studies carried out at Üsküdar University’s application and research centers, BraiNP demonstrates high performance in diagnostic classification and treatment response prediction for many psychiatric disorders.

The main goal in developing the model is to integrate predictive algorithms, which often remain limited to academic publications, into the healthcare system to enable early diagnosis, effective treatment planning, and efficient use of healthcare resources.

Which Disorders Is It Used For?

The BraiNP model is capable of processing data related to a wide range of psychiatric disorders, including:

  • Alcohol Addiction
  • Polysubstance Addiction
  • Nicotine Addiction
  • Gambling Addiction
  • Behavioral Addictions
  • Psychosis
  • Anxiety
  • Post-Traumatic Stress Disorder (PTSD)
  • Obsessive-Compulsive Disorder (OCD)
  • Depression
  • Bipolar Disorder
  • Anorexia
  • Autism

Additionally, the system contributes to the development of new classification models by instantly analyzing data diagnosed by physicians in the field.

How Does the BraiNP Artificial Intelligence Model Work?

Whereas classical artificial intelligence algorithms generally rely on commonly used biomarkers for diagnosis, BraiNP enhances data resolution and significantly improves model performance through new-generation deep learning methods.

The functioning of the model involves the following stages:

  • Data Collection and Preprocessing: EEG (high temporal resolution) and fMRI (high spatial resolution) data are collected from patients or healthy individuals and are denoised through preprocessing.
  • Feature Extraction: The developed algorithm processes the data using GPU-supported cloud-based computing systems to extract distinguishing features.
  • Model Development: Classification models are developed using these features.
  • Performance Measurement: Models are evaluated using various statistical metrics.
  • Clinical Validation: External tests conducted with double-blind data demonstrate the model’s clinical applicability.

Although new-generation models tend to have low explainability, they are supported by visual tools such as heat maps to make clinical interpretation possible.

International Patent Application and Its Benefits

The BraiNP model has achieved high accuracy in distinguishing between OCD and healthy individuals, classifying unipolar and bipolar disorders, and predicting response to transcranial magnetic stimulation (TMS) in depression.

The international patent application for the model has been completed, thereby ensuring its global recognition and protection. The patent registration not only documents the model’s originality and innovation but also enables its routine use by physicians at NPİSTANBUL Hospital.

Seven Core Contributions to the Healthcare System

The BraiNP application offers the following key benefits to the healthcare system in both the short and long term:

  • Early Intervention: Mental disorders can be detected early, enabling fast and effective intervention.
  • Prevention of Complications: The development of comorbid conditions and risky behaviors can be prevented.
  • Reduced Suffering and Improved Quality of Life: Timely diagnosis helps alleviate the individual’s suffering.
  • Personalized Treatment Plans: Unique therapeutic approaches can be developed for each patient.
  • Efficient Use of Resources: Reduces emergency service load and prevents unnecessary hospitalizations.
  • Family Education and Psychosocial Support: Families can be educated early in the process.
  • Improved Prognosis: Ensures long-term recovery and lower relapse rates during the treatment process.

Scientific Advancements from Üsküdar University in Brain-Computer Interfaces and Artificial Intelligence

Üsküdar University trains specialists at the postgraduate level in brain-computer interfaces (BCI), artificial intelligence, and neuroimaging, while also conducting active research and development projects.

BCI systems enable individuals who have lost neuromuscular functions (such as ALS patients, individuals with paralysis or spinal cord injuries) to interact with computers or mechanical devices. Using signal sources like EEG, MEG, and fMRI, robotic arms, prosthetics, and wheelchairs can be controlled.

Next-Generation Big Data Application with Facial Emotion Recognition (FER) Model

Another artificial intelligence project developed within the university is the “Facial Emotion Recognition” (FER) model, which analyzes an individual’s dominant emotion in real time through facial imagery.

This system, which detects basic emotions such as happiness, sadness, anger, surprise, fear, and disgust, offers multi-functional applications in health, education, marketing, and security sectors.

Prof. Türker Tekin Ergüzel stated that despite challenges such as cultural differences, visual obstructions (such as glasses or beards), and ethical concerns, they are working on integrating the model with voice data to develop a multimodal prediction architecture.

 

 

Üsküdar News Agency (ÜNA)