Initial Situation

A 52-year-old man, leading an active and healthy lifestyle, had experienced progressively worsening chronic fatigue over a period of six years. He maintained a balanced Mediterranean diet, exercised regularly, and completely abstained from alcohol. His health parameters — height, weight, laboratory values, and routine clinical assessments — consistently remained within normal ranges.

Over the years, the patient consulted seven different specialists. Each proposed their own hypothesis, ranging from sleep disorders to thyroid dysfunction. One cardiologist recommended the implantation of a pacemaker, which was carried out; however, it did not lead to any improvement in his condition. Despite extensive testing, no definitive diagnosis was established. The symptoms gradually worsened, and a clear clinical explanation remained elusive.


Use of Aima Diagnostics
Amid growing uncertainty and distrust in standard diagnostic approaches, the patient decided to use the Aima Diagnostics platform. He uploaded all laboratory results and medical documentation accumulated over the years.

The AI algorithm analyzed the data and issued a clear recommendation: to undergo a coronary artery calcium (CAC) scan immediately. The system indicated a high probability (85–90%) of significant coronary artery blockage and the likely need for quadruple or quintuple bypass surgery. It also emphasized an extremely high risk of myocardial infarction or stroke in the coming months.

The patient insisted on additional testing, which fully confirmed the AI model’s prediction.


Results
Less than 48 hours after receiving the Aima Diagnostics recommendation, the patient was hospitalized and successfully underwent coronary artery bypass grafting (CABG), during which five bypasses were placed. Imaging confirmed 88% coronary artery blockage. According to the medical team, the risk of a fatal cardiac event in the short term would have been extremely high without timely intervention.

The postoperative period was smooth, without complications. Within two months, the patient had fully returned to his usual active lifestyle. The previously implanted pacemaker proved to be unnecessary and unrelated to the true cause of his symptoms.


Conclusion
Aima Diagnostics enabled the detection of a critical cardiovascular condition that had gone unnoticed for six years using standard clinical methods. By analyzing medical data comprehensively, the AI algorithms were able to suggest a diagnostic direction that none of the seven consulted specialists had considered.

This case illustrates the potential of Aima Diagnostics as a clinical decision support tool capable of significantly reducing the time to diagnosis and improving diagnostic accuracy in complex or atypical clinical scenarios.

Aima Diagnostics does not replace physicians — it enhances their capabilities, supporting medical decision-making through deep data analytics rather than relying solely on clinical intuition.

Can Laboratory Tests Really Reveal Such a Condition?
This is an excellent question — and it’s precisely what makes this case so significant.
The short answer is: yes, in many situations, the combined patterns within routine laboratory data can provide strong indirect signals of underlying cardiovascular disease — even when all individual values appear “normal.”


Why Lab Tests Can Indicate Cardiovascular Problems
Modern AI models (including those used by Aima Diagnostics) do not analyze single values in isolation. Instead, they examine complex multidimensional relationships between dozens of biomarkers, their dynamics over time, and subtle shifts that typically go unnoticed in conventional clinical practice.

For example, in cases of coronary artery disease or chronic ischemia, AI can detect patterns such as:

• Low-level but persistent elevations in inflammatory markers (e.g., hs-CRP, fibrinogen). Individually, these values remain within the reference range, but together they form a characteristic pattern of vascular wall inflammation.
• Minor but consistent lipid profile imbalances, such as altered ApoB/ApoA1 ratios or a triglyceride–HDL imbalance. These may not meet classical thresholds for intervention but indicate early atherosclerotic changes.
• Subtle disturbances in glucose metabolism or early insulin resistance, which don’t fit the diagnostic criteria for diabetes but signal elevated cardiovascular risk.
• Combinations of mild abnormalities in coagulation parameters, ferritin, liver enzymes, or electrolytes, which may seem clinically insignificant on their own, but collectively form a distinct diagnostic “fingerprint” recognizable to an AI model trained on large datasets.

Traditional Clinical Evaluation vs. AI Analysis
A physician typically evaluates lab results one by one, asking: “Is this value outside the normal range?”
AI, by contrast, evaluates the entire multidimensional pattern, comparing it against millions of patient profiles with known outcomes.

What looks like an “unremarkable lab report” to a human clinician may represent a high-probability signature of early ischemic heart disease to an algorithm — especially when longitudinal data over several years is available.



Why This Matters in Such Cases
The patient described in the case had no obvious risk factors. Each individual test looked fine.
But when analyzed as a whole, his long-term lab data showed consistent, subtle shifts in lipid markers, inflammatory indicators, and metabolic parameters. This was enough for the model to flag a high likelihood of significant coronary artery disease and to recommend CAC scanning — which ultimately confirmed the diagnosis.



Key Insight
Laboratory data does not replace imaging techniques like CT or angiography.
However, when analyzed intelligently and comprehensively, it can serve as a highly accurate early risk indicator, often years before symptoms become specific or before imaging would normally be ordered.

Humans typically cannot detect these nuanced patterns. Machines can.



Case Author: Olga Miller, Aima Diagnostics
Source: Patient correspondence, adapted for clarity and brevity. Published with consent.