Artificial Intelligence (AI) is an often divisive topic, however, it is now undeniable that this tool has become extremely powerful and valuable to humans. In fact, AI is now being used in areas such as medicine, especially for the diagnosis of diseases and the personalization of drug therapies, always accompanied by the supervision of medical personnel. Thanks to this technology, numerous disease diagnostics have been successfully carried out in the fields of orthopedics and oncology. Currently, the use of AI, combined with 3D printing, is also expanding to mental disorders such as schizophrenia.
A recent study conducted an in-depth investigation into how AI and 3D printing, can help in the treatment of diseases such as schizophrenia. According to the World Health Organization, there are about 21 million people in the world with schizophrenia. It is a disease that arises between the ages of 18 for women and 25 for men and whose symptoms include delusions, hallucinations and cognitive impairment. AI therefore could help physicians not only make early diagnosis of schizophrenia on high-risk individuals, but also personalize their treatment and medication.
The major difficulty related to the diagnosis of schizophrenia is that, unlike areas such as orthopedics and oncology, the symptomatology of this disease is heterogeneous and can therefore manifest itself in different forms for each patient. In addition, schizophrenia cannot be diagnosed by serving biological indicators, but only through the clinical symptoms established by the DSM, i.e., The Diagnostic and Statistical Manual of Mental Disorders, and the ICD, International Classification of Diseases, which all the way are not always sufficient to recognize the less classical symptoms of the disease.
Use of AI for the Treatment of Schizophrenia
That said, AI could offer medical personnel new methods of disease investigation and treatment. How? Well, it is a technology that is able to process and analyze extremely complex data. This would enable early diagnosis of disease in high-risk populations. In fact, through the integration of Machine Learning, AI can analyze data collected from patients and process it with clinical models, limiting human error derived from manual calculation of data.
In addition, AI is a technology that has self-learning and self-optimization capabilities: when enriched with new data, it can produce increasingly accurate algorithms that enable improved medical predictions. This would make it an extremely useful tool in the diagnosis of diseases with heterogeneous symptomatology, such as schizophrenia.
Furthermore, AI could detect the onset of disease symptoms in patients through analysis of their language. Indeed, this disease can affect the language faculties of those with the disease, causing changes in their tone of voice or elocution. Unfortunately, during the treatment of schizophrenia, depressive symptoms may emerge in the patient, and antidepressant treatment is often neglected because the symptoms are not recognized early during the progression of the disease.
Thankfully, it has been shown that AI, trained according to multimodal behavioral features and implemented with Machine Learning, can then determine whether the patient is healthy, suffering from schizophrenia or a major depression disorder. Through language analysis, AI enables early identification of behavior change in patients with schizophrenia, to then support them with appropriate drug treatment.
The diagram illustrates the application of AI for schizophrenia for diagnosis, treatment and prognosis
Finally, AI is also useful in creating personalized drug therapies. By collecting patient data and creating clinical models, AI can make predictions about personalized drug dosages for each patient. Not to mention, in addition to calculating doses, AI technology can identify key factors that influence drug metabolism and response, allowing clinicians to understand how each patient responds to therapy. As a result, AI is also able to develop personalized therapies for patients, optimizing not only dosing but also drug selection.
3D Printed Electronic Devices for Research
For the past few years, new AI-enhanced approaches for the treatment of schizophrenia have been experimented with that include the use of various technologies, including 3D printing. As reported in the research, a recent study used 3D printing to create an electronic neural interface device, which falls under the field of BCI, or Brain-computer interface, allowing it to decode neurophysiological signals associated with brain activity in mice. Thanks to this 3D-printed implantable and biocompatible device, it was possible to accurately monitor the activity of the prefrontal cortex of mice.
The goal of the research was to analyze the effects of therapies for neuropsychiatric disorders on neural activity. This neural prosthetic interface was designed to record, modulate and classify electrophysiological biomarkers associated with these disorders. Research results have shown that the prosthesis can effectively monitor and modulate brain activity in both healthy and impaired mice, offering a new tool for personalized treatment of neuropsychiatric disorders.
As reported by research, AI has, undoubtedly, created unprecedented possibilities for the diagnosis and treatment of schizophrenia. Still, the technology is still subject to limitations and serves more as an auxiliary tool for clinicians in diagnosis and treatment rather than a true substitute.
Although AI is “fueled” by data collection and becomes increasingly efficient because of this, current AI-based applications experience shortcomings when it comes to applying actual clinical therapies. The difficulties increase when dealing with medical data such as those of schizophrenia that differ greatly from one another in diagnostic criteria. Moreover, at present, many existing studies have focused mainly on retrospective analyses, making use of historical data collected solely for algorithm training and testing.
Despite the current difficulties in the use of AI in the treatment of schizophrenia, the speed with which this technology is evolving makes it possible to speculate that current problems will soon be solved and that it will be implemented more for the treatment of psychiatric illnesses. What is certain is that human-computer interaction is bringing numerous innovations to the medical field that will be worth investigating in the future as well. To learn more about how AI can help physicians in the treatment of schizophrenia, click HERE.
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*All Photo Credits: Nature
