The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.

This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment. The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising. The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future research and advancements in this area.

Posterior Communicating Artery Aneurysm With Referred Tooth Pain: A Case Report.

Oculomotor nerve (CN III) palsy (ONP) has multiple etiologies, with aneurysms and ischemic injury being the two leading causes. The presentations of these conditions differ, as aneurysms commonly manifest with pupillary involvement, while ischemic-related ONP often leads to a pupil-sparing presentation. We present a 63-year-old African American male with a history of sickle cell trait, ocular sickle cell disease, and untreated hypertension that develops "down and out" left eye with a mid-dilated pupil unresponsive to light. However, the patient developed severe left upper tooth pain after the onset of the eye pain, which progressed to ONP. The patient's dental and radiographic evaluation did not indicate any obvious source for his tooth pain. Magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA) of the head revealed a 7-mm saccular aneurysm with a 2-mm neck arising from the left posterior communicating artery (PCOM) aneurysm, and neurovascular surgical intervention was initiated. This case highlights the potential of referred tooth pain as an early symptom in patients with PCOM aneurysm, which physicians should be vigilant about and consider as a potential indicator of the condition. Therefore, collaboration between different specialties, including ophthalmology, neurology, neurosurgery, and dental care, is necessary to formulate a comprehensive treatment plan that effectively addresses the patient's specific needs and challenges.

Cat Eye Syndrome with a Unique Liver and Dermatological Presentation.

Cat eye syndrome (CES), also known as Schmid-Fraccaro syndrome, is a complex genetic syndrome with a highly variable phenotype that includes ocular coloboma, anal atresia, preauricular skin tags and pits, heart defects, kidney malformations, dysmorphic facial features, and mild to moderate intellectual disability. We describe a case of a 23-year-old male with a past medical history of CES with short stature, mild learning disability, and some dysmorphic facial features who presented with recurrent pruritus and rashes and had mild liver dysfunction. Furthermore, the patient did not have the classic presentation of CES but a clinically milder expression of the phenotypes. Abnormalities in the abdominal ultrasound prompted an ultrasound-guided liver biopsy, which showed bile ductular proliferation with mild portal inflammation composed of lymphocytes and plasma cells, and bridging fibrosis. The patient's labs showed elevated immunoglobulins with the highest increase observed in IgG, along with negative antinuclear antibodies (ANA), negative anti-mitochondrial antibody, and negative hepatitis A/B/C but a weak positive anti-smooth muscle antibody (ASMA). These findings indicated that the patient most likely had autoimmune hepatitis (AIH) or an overlap syndrome with primary sclerosing cholangitis (PSC). The patient was initially treated with steroids and antihistamines for pruritus, which led to some clinical improvement. After dermatological evaluation, the patient was diagnosed with atopic dermatitis and was recently started on a dupilumab 600 mg loading dose and would continue with biweekly dupilumab 300 mg injections. This dermatological finding may require additional examination and can be a unique presentation in patients with CES. This case illustrates that even patients with milder CES expression can experience intense dermatological complications if not effectively managed. CES is a multifactorial disease that requires intervention from multiple specialists. Therefore, primary care physicians must be aware of the potential complications of CES and make adequate referrals to closely monitor patients' symptoms.