This paper investigates spatial computing, which is a pathological transformational modern technology that integrates the physical and digital realms and has the potential to revolutionize pathology healthcare. Pathology as a medical specialist plays a crucial role in patient care by providing essential information for diagnosis, treatment planning, and disease monitoring. It studies and diagnoses diseases by examining tissues, organs, bodily fluids, and cells. Pathology is a broad field with three main branches: Anatomic pathology, Clinical pathology, and Molecular pathology. This study investigates the possibilities of spatial computing in radiography and clinical pathology with emphasis on diagnosis accuracy, medical education, workflow efficiency, and the outcomes in the patients. Augmented Reality (AR) medical devices guide pathologists in real-time during diagnostics procedures. The digital reproduction of tissue samples to allow pathologists to examine specimens in three dimensions is a significant utilization of spatial computing in virtual microscopy. This process allows remote collaboration between pathologists and laboratories, provides health informatics as seen in electronic health records (EHRs), improves diagnosis, and presents a platform with learning experiences in the medical field. Patients can interact with three-dimensional simulations of their anatomy, which helps them make more educated treatment decisions provided via the pathology findings and treatment alternatives in an immersive format. As this technology advances, its potential to transform pathology practice and improve patient care remains high. This review describes technological perspectives and discusses the statistical methods, clinical applications, potential obstacles, and directions of spatial computing in clinical pathology.
tient’s symptoms and phenotypic abnormalities. Obviously, arrival at the correct diagnosis is a function of the examining physician and pathologist (fund of knowledge, experience, alertness), the prevalence of the disease in question in the particular patient (age, race, sex, site), and the sensitivity/specificity of the screening tests used (physical exam, vital signs, blood solutes, tissue stains, genetic assays). The pathologic diagnosis represents the best estimate currently possible of the disease entity affecting the patient and is the basis for downstream follow-up and treatment decisions. The diagnosis implies a natural history (course of disease, including chronicity, functional impairment, and survival) that most patients with this disease are expected to follow. Be aware that not all patients with a given disease will naturally follow the same disease course, so differences in patient outcome do not necessarily correspond to incorrect diagnosis. Variables that independently correlate with clinical outcome differences are called independent prognostic variables and are routinely assessed in an effort to predict the natural history of the disease in the patient. It is also important to note that medical therapies for specific diseases do not always work. Variables that independently correlate with (predict) responses to therapy are called independent predictive variables.
Diagnosis of a disease and development of an effective therapy for that disease do not require knowledge of the underlying etiology or pathogenesis. For example, granulomatous polyangiitis (née Wegener granulomatosis) was understood by morphology and outcomes to be a lethal disease without treatment, yet responsive to cyclophosphamide and corticosteroids, before it was found to be an autoimmune disease targeting neutrophil cytoplasmic protein PR3 ( Fig. 11.1
). However, understanding the molecular and cellular pathogenesis of a disease allows development of screening methods to determine r
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