Complete Blood Count Analysis & Parameters: The Essential Guide to Blood Diagnostics

I. Introduction

The Complete Blood Count (CBC) remains one of the most fundamental and widely ordered laboratory tests in modern healthcare, serving as a critical gateway to understanding patient health status across virtually every medical discipline. From emergency departments assessing acute infections to oncology units monitoring chemotherapy effects, the CBC provides essential quantitative and qualitative information about circulating blood cells that no clinician can afford to overlook.

For over a century, CBC analysis has evolved dramatically. What began in the 1850s as manual microscopic counting—a laborious process requiring exceptional skill and consuming hours per specimen—has transformed into a sophisticated, automated discipline. The evolution progressed from impedance-based cell counting in the 1950s to flow cytometry in the 1970s, and now enters an unprecedented era of artificial intelligence integration combined with advanced optical morphology analysis.

Modern AI-powered blood analyser machines represent a paradigm shift in diagnostic capability. These systems transcend simple cell enumeration, incorporating machine learning algorithms trained on over 40 million clinical samples to deliver comprehensive cell morphology analysis alongside precise quantitative measurements. This comprehensive article addresses healthcare professionals, laboratory managers, clinical educators, and medical students seeking to understand CBC parameters, their clinical interpretation, and the revolutionary technology enabling next-generation blood diagnostics.

II. CBC Fundamentals

Definition and Clinical Significance

The Complete Blood Count is a panel of automated measurements quantifying the cellular components of whole blood—specifically red blood cells, white blood cells, and platelets. Beyond these three fundamental cell lines, the CBC includes derived indices and differential analysis providing insight into cell morphology, maturity status, and functional implications.

Clinical significance derives from the CBC’s ability to: detect acute and chronic infections through white blood cell abnormalities, diagnose anemias through hemoglobin, hematocrit, and red cell indices analysis, identify hematologic malignancies via abnormal cell morphology, assess bone marrow function through reticulocyte counts and immature cell populations, monitor therapeutic efficacy and drug toxicity during long-term treatment, and provide prognostic information regarding disease severity and mortality risk.

When CBC Testing is Ordered

CBC testing is among the most frequently ordered laboratory tests, ordered in diverse clinical contexts. Routine health screening incorporates CBC as part of comprehensive metabolic assessment. Emergency departments order stat CBCs for undifferentiated patients with fever, sepsis concerns, or unexplained hemodynamic instability. Oncology clinics use serial CBC monitoring to assess chemotherapy tolerance and detect treatment complications. Infectious disease specialists order CBCs to differentiate bacterial from viral infections and assess immune response adequacy. Primary care physicians order CBCs to evaluate fatigue, assess medication side effects, and screen asymptomatic patients during health maintenance visits.

Role in Diagnostic Medicine

The CBC occupies a unique position as both a diagnostic tool and a prognostic indicator. Its role transcends simple disease detection—it provides information essential for clinical decision-making regarding treatment intensity, antimicrobial selection, transfusion requirements, and hospitalization necessity. The comprehensive nature of modern CBC analysis, particularly with advanced cell morphology detection, enables earlier recognition of serious conditions that might otherwise be missed by traditional analysis methods.

III. CBC Parameters Detailed: The Core Section

Understanding CBC parameters requires systematic organization into three complementary categories: white blood cell measurements, red blood cell assessments, and platelet analysis. Each category provides distinct clinical insights while also contributing to integrated clinical interpretation.

WBC Parameters: Infection Assessment and Immune Function

Total WBC Count forms the foundation of white blood cell assessment. The normal range in healthy adults spans 3.5 to 9.5 × 10⁹/L. Elevation above this range—termed leukocytosis—suggests acute infection, inflammatory response, leukemic processes, or medication effects (particularly corticosteroids). Conversely, leukopenia (counts below 3.5 × 10⁹/L) indicates bone marrow suppression, immune system dysfunction, medication toxicity, or overwhelming sepsis with bone marrow exhaustion.

Five-Part Differential measurement categorizes white blood cells into: neutrophils (also called polymorphonuclear leukocytes or PMNs), lymphocytes, monocytes, eosinophils, and basophils. Each cell type reflects distinct physiologic functions and disease states.

Neutrophils comprise 40-75% of total white blood cells, serving as the body’s primary bacterial defense mechanism through phagocytosis and antimicrobial granule release. Elevated neutrophils (neutrophilia) typically indicate bacterial infection, acute inflammation, or stress response. These mature neutrophils—termed segmented neutrophils (NSG)—represent cells that have completed normal bone marrow maturation and constitute the most abundant circulating leukocyte population.

Lymphocytes represent 20-40% of white blood cells, encompassing T cells, B cells, and natural killer cells mediating immune surveillance and antibody production. Lymphocytosis (elevated lymphocyte percentage) frequently accompanies viral infections, autoimmune diseases, and chronic inflammatory conditions. Conversely, lymphocytopenia indicates viral immunosuppression (particularly HIV infection), autoimmune lymphocyte destruction, or corticosteroid effects.

Monocytes constitute 2-8% of white blood cells and represent the precursors to tissue macrophages. These cells provide additional bacterial defense through phagocytosis and serve prominent roles in chronic inflammation and tissue remodeling. Monocytosis correlates with chronic bacterial infections (tuberculosis), fungal infections, and certain autoimmune conditions.

Eosinophils normally comprise 1-4% of white blood cells but increase dramatically during parasitic infections, allergic reactions, and certain hematologic malignancies. The recognition of significant eosinophilia should prompt investigation for parasitic exposure, atopic disease, or malignancy.

Basophils represent fewer than 1% of circulating white blood cells. Basophilia is uncommon but may indicate myeloproliferative disorders or severe allergic reactions.

Advanced 7-Part Differential extends traditional five-part analysis through recognition of immature and abnormal cell populations. Immature Stab Neutrophils (NST) represent band forms and earlier precursors indicating bone marrow stress and enhanced hematopoietic activity—a pattern termed “left shift.” NST elevation specifically suggests acute bacterial infection, overwhelming inflammatory response, or chronic myeloproliferative conditions. The detection of NST provides early warning of severe infection requiring antimicrobial escalation.

Hypersegmented Neutrophils (NSH) display more than the standard five nuclear lobes, indicating dysregulated cell maturation potentially associated with megaloblastic anemia (vitamin B12/folate deficiency) or certain myelodysplastic syndromes.

Atypical Lymphocytes (ALY) emerge during viral infections—particularly infectious mononucleosis and cytomegalovirus infection—characterized by larger cell size and abundant cytoplasm. Recognition of ALY helps differentiate viral from bacterial infection.

Reticulocytes (RET) are immature red blood cells containing residual ribosomal RNA, identifiable using supravital staining or fluorescence-based flow cytometric techniques. Reference ranges for RET counts vary by analyzer platform and methodology but are typically expressed as either an absolute count or a percentage of total erythrocytes.

Reticulocyte measurements provide valuable insight into bone marrow erythropoietic activity in the evaluation of anemia. An increased RET count generally reflects an appropriate marrow response to anemia caused by blood loss or hemolysis, whereas a relatively low or inappropriately normal RET count in the presence of anemia suggests impaired or insufficient erythropoiesis, warranting further etiologic investigation. Interpretation should consider clinical context and, when appropriate, corrected indices such as the reticulocyte production index (RPI).

The Immature Reticulocyte Fraction (IRF) reflects the proportion of newly released reticulocytes and serves as an early indicator of changes in erythropoietic activity. IRF values and clinical thresholds are method-dependent and should be interpreted relative to laboratory-specific reference ranges. Trends in IRF may assist in monitoring bone marrow response to therapy rather than serving as an isolated determinant of treatment efficacy.

Nucleated red blood cells (NRBCs) and platelet aggregates (PAg) are abnormal findings in peripheral blood. Their presence may be associated with severe stress states such as hemolytic anemia, hypoxia, sepsis, bone marrow infiltration, or leukoerythroblastic reactions, and may also be influenced by pre-analytical or anticoagulant-related factors. Detection of NRBCs or PAg warrants careful clinical correlation and, when indicated, further diagnostic evaluation.

RBC Parameters: Anemia Diagnosis and Morphology

Typical adult reference ranges are approximately 4.3–5.9 × 10¹²/L in men and 3.9–5.2 × 10¹²/L in women, noting that values vary by laboratory, population, and analytical methodology. Low RBC counts indicate anemia from various causes, while elevations (polycythemia) may reflect high-altitude adaptation, chronic hypoxemia, or myeloproliferative disorders.

Hemoglobin (HGB) quantifies oxygen-carrying protein within red blood cells, with normal values of 13-17 g/dL in men and 12-16 g/dL in women. Hemoglobin reduction represents the hallmark of anemia and triggers investigation into etiology. Hemoglobin concentration is a key parameter informing transfusion decisions, which must be made in conjunction with clinical symptoms, bleeding status, and patient-specific risk factors.

Hematocrit (HCT) expresses red blood cell volume as a percentage of total blood volume. Normal values range from 40-55% in men and 36-48% in women. Hematocrit provides supportive information regarding anemia severity but should be interpreted cautiously in acute hemorrhage, where values may not immediately reflect blood loss.

Mean Corpuscular Volume (MCV) reflects the average size of circulating red blood cells and is reported in femtoliters (fL). Reference ranges may vary by laboratory and analytical methodology, but MCV is commonly used to classify anemia into microcytic, normocytic, and macrocytic patterns.

Microcytic anemia is most frequently associated with iron deficiency and thalassemia, while macrocytosis may be observed in conditions such as vitamin B12 or folate deficiency, reticulocytosis, liver disease, alcohol use, or bone marrow disorders. Normocytic anemia encompasses a broad range of etiologies, including acute blood loss, hemolysis, anemia of chronic disease, renal insufficiency, and bone marrow suppression.

MCV-based classification provides an initial framework for anemia evaluation and supports differential diagnosis; however, definitive etiologic assessment requires integration with additional red blood cell indices, reticulocyte parameters, biochemical testing, and clinical context.

Mean Corpuscular Hemoglobin (MCH) quantifies average hemoglobin content per red blood cell in picograms (pg), with normal range 27-31 pg/cell. Reduced MCH (hypochromic cells) accompanies iron deficiency, while elevated MCH appears in macrocytic anemias.

Mean Corpuscular Hemoglobin Concentration (MCHC) calculates hemoglobin density per unit red blood cell volume (g/dL or percentage), with normal values of 32-36 g/dL or 316-354 g/L. MCHC distinguishes true hemoglobin deficiency from cell volume abnormalities—a crucial distinction when interpreting anemia etiology.

Red Cell Distribution Width (RDW) quantifies variation in red blood cell size (anisocytosis) expressed as percentage, with normal values of 11.5-14.5%. Elevated RDW indicates heterogeneous red blood cell populations, commonly seen in iron deficiency (which produces progressively smaller cells as iron depletion progresses). In contrast, thalassemia minor produces microcytosis with normal RDW, a distinction enabling differentiation between these iron metabolism disorders versus genetic hemoglobinopathies.

RDW-SD represents standard deviation of red blood cell distribution in femtoliters, with normal values of 37-50 fL. This parameter provides an alternative expression of erythrocyte size distribution particularly useful when RDW-CV produces ambiguous results.

Red Blood Cell Morphology Analysis encompasses visual assessment of red blood cell shape abnormalities providing crucial diagnostic clues. Schistocytes (fragmented cells) indicate mechanical red blood cell destruction from microangiopathic hemolytic anemia, thrombotic thrombocytopenic purpura, or disseminated intravascular coagulation. Spherocytes (densely staining, small cells lacking central pallor) characterize hereditary spherocytosis or autoimmune hemolytic anemia. Teardrop cells suggest myelofibrosis, while echinocytes (spiculated cells) appear in uremia or phosphate depletion. Echinocytes may also result from specimen handling or storage artifacts.

Platelet Parameters: Hemostasis Assessment

Platelet Count measures circulating thrombocytes, with normal range 150-400 × 10⁹/L. Thrombocytopenia (counts <150 × 10⁹/L) increases bleeding risk, while thrombocytosis (>400 × 10⁹/L) may indicate reactive inflammation, iron deficiency, or myeloproliferative disease.

Mean Platelet Volume (MPV) quantifies average platelet size in femtoliters, with normal range 7.6-9.3 fL. Elevated MPV indicates immature platelet release from bone marrow, common in immune thrombocytopenia or myeloproliferative disorders. Conversely, low MPV suggests impaired platelet production or consumption disorders. MPV trends over time provide prognostic information regarding disease severity and response to therapy.

Platelet Distribution Width (PDW) expresses variation in platelet size, with normal range 9.4-16%. Elevated PDW indicates heterogeneous platelet populations, often accompanying immature platelet release. PDW has been investigated as a potential marker of platelet activation and inflammation in various clinical settings,—making it a valuable prognostic indicator beyond simple platelet counting.

Plateletcrit (PCT) reflects total platelet mass as a percentage of blood volume, with normal range 0.1-0.28%. This derived parameter provides rapid assessment of platelet contribution to blood composition and correlates with platelet count and size characteristics.

Platelet Large Cell Ratio (P_LCR) represents the proportion of circulating platelets exceeding a defined volume threshold (commonly >12 fL), reflecting the relative presence of larger platelets within the total platelet population. Increased P_LCR may be observed in conditions associated with enhanced platelet turnover or increased release of younger platelets from the bone marrow. Conversely, lower P_LCR values reflect a predominance of smaller platelets.

P_LCR is a descriptive platelet volume parameter and should be interpreted in conjunction with platelet count, MPV, additional laboratory findings, and clinical context. It does not independently establish specific clinical diagnoses.

Platelet Large Cell Count (P_LCC) represents the absolute number of large platelets in circulation, derived from platelet count and size distribution data. This parameter provides a quantitative complement to P_LCR by expressing large platelets as an absolute value rather than a percentage.

P_LCC supports the assessment of platelet size dynamics but does not independently indicate underlying clinical syndromes. Interpretation should be integrated with other platelet indices and relevant clinical and laboratory information.

IV. Advanced AI-Powered Technology

Modern Analyser Capabilities and Cell Morphology Analysis

Contemporary blood analyser machines equipped with artificial intelligence represent fundamentally different technology compared to traditional hematology analyzers. Rather than relying exclusively on impedance measurements (cell size estimation based on electrical resistance) or flow cytometry (laser light scatter analysis), modern AI-powered systems integrate high-definition optical microscopy with sophisticated machine learning algorithms trained on over 40 million clinical samples.

The optical imaging pathway begins with fully automated sample processing involving liquid-based staining using Wright-Giemsa methodology—preserving superior color dimension and cellular detail. Samples flow through a customized optical chamber equipped with Swiss optical lenses providing 4-megapixel resolution at 50 frames-per-second capture rate. Patented Z-stack technology captures three-dimensional cell images, transcending traditional two-dimensional microscopy limitations. This multi-spectral imaging methodology—combining visible imaging with narrowband multispectral imaging—creates high-dimensional input data for downstream analysis.

High-speed full-field scanning captures complete cellular architecture at oil-immersion resolution, achieving positional repeatability accuracy <1 micrometer through fully automated mechanical arms. This precision ensures consistent sample positioning eliminating the variability inherent in manual microscopy. The resulting image database contains unprecedented cellular detail enabling sophisticated morphological pattern recognition impossible with traditional methods.

Machine Learning Integration

The artificial intelligence recognition engine represents the culmination of training on over 40 million de-identified clinical samples annotated by expert pathologists. This deep learning foundation enables convolutional neural network (CNN) algorithms to recognize blood cell morphology with accuracy matching or exceeding experienced human pathologists—achieving >97% classification accuracy across diverse cell types including normal populations, immature forms, and pathological abnormalities.

The algorithm architecture comprises multiple specialized layers: initial image enhancement using CNN-powered super-resolution imaging transcending optical diffraction limits; multi-dimensional feature extraction capturing morphological characteristics including size, shape, nuclear patterns, and cytoplasmic features; pooling operations synthesizing feature maps across hierarchical levels; and fully connected layers generating final cell classification with confidence probabilities. This sophisticated processing pipeline generates 37+ diagnostic parameters including WBC differentials, RBC indices, platelet measurements, reticulocyte identification, and detection of rare abnormal cells including blasts, dysplastic forms, and infectious organisms.

Continuous learning capabilities ensure the system improves with accumulating clinical data. Integration with large language models enables diagnostic reasoning—correlating cell findings with clinical presentation to generate AI-assisted diagnostic recommendations. The system was recognized at the 2022 World Artificial Intelligence Conference (WAIC), validating its clinical significance.

Lab-Grade Accuracy Metrics and Quality Assurance

Independent validation studies demonstrate exceptional analytical performance. Correlation coefficients comparing automated analysis with reference laboratory methods exceed 0.98 across critical parameters: WBC correlation r² = 0.9962; RBC r² = 0.9787; hemoglobin r² = 0.9867; platelets r² = 0.9834. These correlation values exceed acceptance criteria for clinical laboratory testing and demonstrate reliable accuracy across the full pathologic spectrum from severe anemia through extreme leukocytosis.

Quality assurance mechanisms include automatic flagging of unusual findings, morphological abnormality alerts, multi-parameter validation ensuring results meet diagnostic criteria simultaneously, and built-in quality control protocols. Multi-center clinical evaluations across Asia-Pacific, Latin America, and Middle East regions confirm consistent performance regardless of geographic population variation or disease prevalence differences. Published studies demonstrate AI morphology accuracy of 97%+ matching expert pathologist analysis while eliminating operator variability and reducing manual review requirements by 20% or more.

V. Clinical Applications

Infection Assessment and Differentiation

CBC parameters provide supportive information for infection assessment by reflecting host immune response patterns. Bacterial infections are commonly associated with neutrophilia and the presence of immature neutrophil forms (left shift), reflecting increased bone marrow granulopoiesis in response to inflammatory stimuli. When interpreted alongside clinical signs and additional laboratory markers, elevated absolute neutrophil count and immature neutrophil indices may support suspicion of bacterial infection.

Viral infections more frequently present with relative lymphocytosis, often accompanied by normal or mildly reduced total leukocyte counts. The presence of atypical lymphocytes may be observed in certain viral infections, such as infectious mononucleosis or cytomegalovirus infection. These hematologic patterns assist clinicians in contextualizing immune response but do not independently establish infectious etiology.

In critically ill patients, abnormal leukocyte patterns—including marked leukocytosis, leukopenia, or significant left shift—may raise concern for severe systemic infection. CBC findings contribute to early clinical assessment and risk stratification but must be interpreted in conjunction with clinical presentation, vital signs, inflammatory biomarkers, and established sepsis evaluation criteria.

Anemia Diagnosis and Classification

Microcytic Anemia (low MCV with low MCH and MCHC) most commonly reflects iron deficiency—presenting with elevated RDW as iron depletion produces progressively smaller red blood cells. Iron studies (serum ferritin, iron saturation) confirm diagnosis. Alternatively, thalassemia minor produces microcytosis with normal or reduced RDW, a distinction preserved through careful index analysis that differentiates genetic hemoglobinopathies from acquired iron metabolism disorders.

Macrocytic Anemia (elevated MCV with elevated MCH) suggests vitamin B12 or folate deficiency creating megaloblastic erythropoiesis with dysmaturation of nuclear-to-cytoplasmic ratios. Alternatively, reticulocytosis from compensatory response to hemolysis or blood loss produces macrocytosis reflecting immature red blood cell predominance. Reticulocyte count elevation clarifies etiology and distinguishes regenerative from non-regenerative processes.

Normocytic Anemia (normal MCV and MCH) accompanied by elevated reticulocytes indicates effective bone marrow response to hemolysis or acute blood loss. Conversely, normocytic anemia with low reticulocytes suggests bone marrow failure, chronic kidney disease from erythropoietin deficiency, or anemia of chronic disease. The combination of CBC results with reticulocyte data enables clinically meaningful classification guiding specific therapeutic interventions.

Hematologic Disorder Identification

Acute Leukemia detection depends critically on CBC recognition of circulating blasts—immature myeloid or lymphoid cells appearing abnormally in peripheral blood. While leukemia diagnosis requires bone marrow biopsy confirmation, initial CBC identification of unusual immature populations triggers urgent hematology referral. Modern AI-powered analyzers excel at detecting blast morphology, significantly improving early leukemia recognition and reducing diagnostic delay.

Chronic Myeloproliferative Disorders produce characteristic CBC patterns: chronic myeloid leukemia presents with marked leukocytosis with left shift; polycythemia vera produces erythrocytosis and thrombocytosis; essential thrombocythemia shows severe thrombocytosis with normal hemoglobin; primary myelofibrosis displays leukoerythroblastic picture with teardrop cells and immature cell release. These distinctive patterns enable disease classification and prognostic assessment.

Immune Thrombocytopenia manifests as isolated thrombocytopenia with elevated MPV (immature platelet release) while WBC and RBC remain normal—a pattern distinguishing immune destruction from bone marrow failure and guiding specific therapeutic approaches.

VI. Rapid Diagnosis & Decision Support

Point-of-Care Applications

The emergence of portable, fully automated CBC analyzers has democratized access to laboratory-grade hematology testing in point-of-care settings. Primary care clinics, urgent care facilities, pharmacies with clinical services, and mobile health initiatives can now provide same-visit CBC results enabling immediate clinical decisions rather than requiring reference laboratory send-out with 24-48 hour turnaround delays.

Capillary sampling from fingerstick collection eliminates venipuncture discomfort for pediatric and geriatric patients while requiring minimal blood volume (30-40 microliters). Six-minute result turnaround transforms clinical workflows, replacing delayed diagnosis with immediate assessment and treatment decisions during the patient encounter. Point-of-care CBC enables same-visit diagnosis and treatment initiation—transforming patient experience and clinical outcomes.

AI-Assisted Clinical Recommendations

Advanced blood analyser systems integrate diagnostic support through large language model integration correlating CBC findings with clinical presentation. AI algorithms analyze numerical results alongside morphological abnormalities to generate diagnostic reasoning: elevated NST with fever and clinical infection signs supports bacterial infection diagnosis; lymphocytosis with atypical lymphocytes and pharyngitis suggests viral infection (particularly mononucleosis); microcytosis with low MCH and elevated RDW indicates iron deficiency anemia considerations; thrombocytopenia with elevated MPV and normal WBC/RBC suggests immune thrombocytopenia.

These AI recommendations provide clinical context and differential diagnosis options, supporting clinician decision-making while explicitly indicating that final diagnosis remains dependent on clinical judgment and comprehensive patient evaluation. The system generates confidence probabilities for each diagnostic consideration, enabling clinician assessment of recommendation reliability.

Integration with Patient Management Systems

Modern blood analysers integrate seamlessly with laboratory information systems (LIS) and hospital information systems (HIS) through standardized interfaces, enabling result transmission, automatic quality assurance protocols, and longitudinal tracking. Cloud-based platforms enable remote result access, trending analysis, and population health surveillance. Smart IoT connectivity allows device management, remote troubleshooting, and automatic software updates ensuring systems remain current with latest diagnostic algorithms and quality assurance protocols. These integrated systems enable data-driven clinical decision support and practice improvement initiatives.

VII. Conclusion & Future Directions

O Complete Blood Count has remained an essential diagnostic tool for over a century through relentless evolution—from manual microscopy to automated impedance analysis to flow cytometry, and now to AI-powered morphology assessment. This latest technological revolution transcends incremental improvement; it represents fundamental transformation in diagnostic medicine enabling faster, more accurate diagnosis of critical hematologic conditions.

AI-powered blood analysers equipped with complete blood morphology (CBM) analysis deliver unprecedented capability: laboratory-grade accuracy with 6-minute turnaround, 37+ diagnostic parameters exceeding traditional five-part differentials, and point-of-care deployment enabling same-visit diagnosis. The convergence of high-definition optical imaging, sophisticated machine learning trained on 40 million clinical samples, and fully automated sample processing creates systems matching expert pathologist performance while eliminating manual variability and accelerating diagnosis.

For healthcare professionals, these advances support earlier clinical assessment of critical conditions by providing rapid, high-quality hematologic data at the point of care. Timely availability of CBC and morphology results contributes to early risk stratification in patients with suspected severe infection, supports prompt recognition of abnormal leukocyte populations warranting further hematologic evaluation, and facilitates initial classification of anemia during the first patient encounter.

For laboratory managers, AI-enabled systems help optimize operational efficiency by reducing hands-on workload, simplifying analyzer operation, and improving result standardization across operators and testing sites. Point-of-care deployment extends access to laboratory-grade hematology testing beyond centralized laboratories, supporting primary care and underserved settings that have traditionally relied on delayed reference laboratory workflows.

As healthcare systems worldwide prioritize diagnostic efficiency, accuracy, and accessibility, AI-powered blood analysers have transitioned from innovative technology to essential infrastructure for modern clinical practice. The future of hematology diagnostics belongs to intelligent systems that combine human expertise with machine precision—delivering the rapid, accurate blood analysis that contemporary medicine demands. For more information about next-generation blood analyser technology and comprehensive diagnostic solutions, visit https://ozellemed.com/en/