I'm really glad I took this mail-order medical-grade microbiome shotgun test to look for warning signs of health conditions. All products featured on WIRED are independently selected by our editors. However, when you buy something through our retail links, we may earn an affiliate commission. Medical-grade shotgun test is the gold standard. "Show the work," so you can see which studies it's referencing. Results can be confusing or conflicting. Need a doctor to understand some of the results. We hear a lot about the microbiome, also known as the zoo of different bacteria living in your digestive system. We know some are good and some are bad.

In recent years, there has been a notable advancement in the integration of healthcare and technology, particularly evident in the field of medical image analysis. This paper introduces a pioneering approach in dermatology, presenting a robust method for the detection of hair and scalp diseases using state-of-the-art deep learning techniques. Our methodology relies on Convolutional Neural Networks (CNNs), well-known for their efficacy in image recognition, to meticulously analyze images for various dermatological conditions affecting the hair and scalp. Our proposed system represents a significant advancement in dermatological diagnostics, offering a non-invasive and highly efficient means of early detection and diagnosis. By leveraging the capabilities of CNNs, our model holds the potential to revolutionize dermatology, providing accessible and timely healthcare solutions. Furthermore, the seamless integration of our trained model into a web-based platform developed with the Django framework ensures broad accessibility and usability, democratizing advanced medical diagnostics. The integration of machine learning algorithms into web applications marks a pivotal moment in healthcare delivery, promising empowerment for both healthcare providers and patients. Through the synergy between technology and healthcare, our paper outlines the meticulous methodology, technical intricacies, and promising future prospects of our system. With a steadfast commitment to advancing healthcare frontiers, our goal is to significantly contribute to leveraging technology for improved healthcare outcomes globally. This endeavor underscores the profound impact of technological innovation in shaping the future of healthcare delivery and patient care, highlighting the transformative potential of our approach.

The morbidity of scalp diseases is minuscule compared to other diseases, but the impact on the patient's life is enormous. It is common for people to experience scalp problems that include Dandruff, Psoriasis, Tinea-Capitis, Alopecia and Atopic-Dermatitis. In accordance with WHO research, approximately 70% of adults have problems with their scalp. It has been demonstrated in descriptive research that hair quality is impaired by impaired scalp, but these impacts are reversible with early diagnosis and treatment. Deep Learning advances have demonstrated the effectiveness of CNN paired with FCN in diagnosing scalp and skin disorders. In one proposed Deep-Learning-based scalp inspection and diagnosis system, an imaging microscope and a trained model are combined with an app that classifies scalp disorders accurately with an average precision of 97.41%- 99.09%. Another research dealt with classifying the Psoriasis using the CNN with an accuracy of 82.9%. As part of another study, an ML based algorithm was also employed. It accurately classified the healthy scalp and alopecia areata with 91.4% and 88.9% accuracy with SVM and KNN algorithms. Using deep learning models to diagnose scalp related diseases has improved due to advancements i computation capabilities and computer vision, but there remains a wide horizon for further improvements.