Don't Overlook It as "Due to Age"—AI Searching for Treatable Cognitive Decline, What 92% Sensitivity Means

A family member's stride has shortened. They take longer to think than before, and there are more instances of not making it to the bathroom in time.

Each of these changes might seem like a result of aging. However, behind such changes, there might be a condition called "normal pressure hydrocephalus." With proper diagnosis and treatment, improvement in symptoms can be expected.

As a technology to aid in the discovery of this disease, research on brain image analysis using artificial intelligence (AI) is progressing. On October 17, 2026, Germany's AD HOC NEWS introduced the use of CT images for identifying normal pressure hydrocephalus and the results of related dementia AI research.

The headline figure is "92% sensitivity." While this result is promising, to understand its true meaning, it's necessary to know what the AI measures and under what conditions it was evaluated.

What is Normal Pressure Hydrocephalus—Looking Beyond Memory Loss for Clues

Normal pressure hydrocephalus is a condition where abnormalities in the circulation and absorption of cerebrospinal fluid lead to the enlargement of the brain's ventricles, causing impairments in walking and cognitive functions. It can occur following events like subarachnoid hemorrhage or without a clear cause, known as "idiopathic."

The three main symptoms are gait disturbance, cognitive dysfunction, and urinary incontinence. However, not everyone exhibits all three.

In terms of gait, changes such as taking small steps, dragging feet, and instability when turning are observed. Cognitive functions may include not just memory loss but also difficulty maintaining attention and slower thinking or working speeds. For urinary issues, symptoms like sudden strong urges, frequent bathroom visits, and incontinence are clues.

These symptoms can also occur with other diseases. Therefore, self-diagnosis based solely on symptom combinations is not possible, but it's important not to dismiss discussions of walking or urinary issues as unrelated just because the main concern is memory loss.

What AI Searches for Are Small Reference Points for Accurate Brain Imaging

In the research introduced this time by the University of Minnesota and others, a mechanism was developed to automatically identify small structures called "anterior commissure" and "posterior commissure" within CT images.

Although it may be difficult to understand with just technical terms, their role is akin to markers for measuring the shape of the brain according to a consistent standard. Even if the head's tilt varies when the image is taken, evaluating the shape and size of the ventricles according to a common standard can help reduce comparison variability.

The research used 427 CT images from 340 individuals, with internal evaluation showing an average positional deviation of 1.64 mm for the anterior commissure and 1.49 mm for the posterior commissure. The performance of position identification was also verified with two other data sets.

In other words, the main achievement is that AI can find reference points within images and use their positions to measure features that aid in disease identification. It was not presented as a technology to completely replace human diagnosis.

"92% Sensitivity" Does Not Mean 92% of People Will Recover

For the evaluation distinguishing normal pressure hydrocephalus from Alzheimer's disease, post-traumatic brain volume reduction, and headache patient groups, images from 238 people were used.

In this evaluation, the sensitivity for identifying normal pressure hydrocephalus from other groups was 92%, specificity was 87%, and accuracy was 89%.

Sensitivity is an indicator of how well the presence of the target disease is detected as positive. Considering 100 patients under the same conditions, about 92 would be found, and about 8 would be missed. On the other hand, a specificity of 87% means that out of 100 people without the target disease, about 87 would be judged as negative, while about 13 would be incorrectly judged as positive.

Therefore, it cannot be interpreted as "92% chance of having normal pressure hydrocephalus if positive" or "92% will recover if treated."

Moreover, performance varies depending on the comparison target. In the paper, the sensitivity when comparing only normal pressure hydrocephalus and Alzheimer's disease was 83%. The figure of 92% summarizing multiple comparison targets does not represent the difficulty of all differential diagnoses.

The position identification of reference points confirmed with external data and the disease identification evaluated with 238 people are separate verifications. It cannot be generalized as "normal pressure hydrocephalus was found with 92% sensitivity at other hospitals."

In Another Dementia AI, Performance Declined with External Data

The original article also introduces another AI study targeting Alzheimer's disease, among others, in addition to the normal pressure hydrocephalus research. These are not the same systems, and the diseases and challenges being investigated differ.

One of them, "ConvNeXt–KAN," is a study classifying stages of dementia from brain MRI images. In internal tests, the accuracy was 94.5%, but when evaluated using different image data without additional adjustments, it dropped to 61.6%.

Furthermore, the paper acknowledges the possibility that multiple image sections of the same person could span the training and evaluation datasets, which could lead to an overestimation of the ability to diagnose unknown patients.

When seeing high figures, it's important to consider not just "what percentage it was," but also "whether it could be reproduced with other patients not used in training" and "whether it could be used with images from other facilities."

This result does not prove that the AI for normal pressure hydrocephalus will similarly decline in performance. However, it highlights the necessity of external validation in evaluating medical AI in general.

Even When Combining Images and Clinical Information, There Are Pitfalls in Evaluation

Another study, "MEMOIR-VLM," integrates MRI images with information such as cognitive function tests.

In internal evaluations distinguishing between people with preserved cognitive function and those with dementia, a balanced accuracy of 91.3% was reported. However, in a three-category classification including mild cognitive impairment, it was 68.2%. Increasing the categories and trying to distinguish ambiguous states makes the task more challenging.

This study also examined the issue of apparent performance improvement due to diagnostic information being inadvertently included in the input. This is akin to checking whether clues to the answers are mixed into the test questions.

The ability of AI to provide fluent explanations and its ability to accurately evaluate unknown patients are separate matters. In medicine, what's needed is not just plausible answers but verification of what information supports those answers and under what conditions they can be trusted.

What Is Seen on SNS Is Confusion About Diagnosis and Hope for Treatment

There were not enough direct SNS reactions to this research report of "92% sensitivity" to confirm. Therefore, here we introduce posts from families of patients publicly available regarding normal pressure hydrocephalus. These are not reactions to the current research or indicative of overall public opinion.

In the Reddit hydrocephalus community, a family whose father's MRI suggested the possibility of normal pressure hydrocephalus sought advice on "what to do next in a situation where there is no definitive diagnosis."

The poster was confused about whether the cognitive changes were due to the disease, medication effects, or mood issues. Responses included advice to consult a specialist familiar with adult hydrocephalus.

In the same thread, another family, whose father's symptoms had persisted for a long time, expressed both hope and anxiety about finding a treatable cause before seeing a specialist.

These are not cases verified by medical records but personal experiences. Nevertheless, it conveys how the time from being told "there is a possibility" to determining a diagnosis and treatment plan can be a significant burden for families.

For AI discovery support to be helpful to patients, a system that not only indicates suspicion but also leads to subsequent professional evaluation is necessary.

In Japan, It's Important to Communicate Changes Without Waiting for AI to Become Widespread

When normal pressure hydrocephalus is suspected, symptoms' progression and examination findings are evaluated in combination with image information from CT or MRI.

If necessary, a "tap test," which involves removing a certain amount of cerebrospinal fluid from the waist to see if there are changes in walking, is conducted. Based on the results, the suitability for a shunt operation to divert cerebrospinal fluid to another location is considered.

The term "treatable" does not guarantee that anyone who undergoes surgery will be completely restored. Having characteristic images and being suitable for surgery are not the same, and evaluation by a specialist is required.

When families consult, sharing not only memory loss but also changes in walking, tendency to fall, changes in bathroom habits, and when symptoms started can make it easier to share the progression. It's also advisable to have a list of current medications ready for consultation.

Rather than searching for hospitals that can use AI under research, first communicating changes to your primary care doctor and connecting to evaluations by neurology or neurosurgery if necessary is a realistic first step.

The Value of AI Lies in Reducing Overlooked Treatable Conditions

The study demonstrated the potential to automatically extract information useful for disease evaluation from CT images taken in routine medical practice.

However, there are still stages to confirm between research-level identification performance and actual improvement in patients' lives. Whether it can be used in different medical settings, whether it doesn't increase unnecessary tests, and whether it can expedite access to appropriate treatment—such verifications are necessary.

"92% sensitivity" is a promising research result. At the same time, the importance of carefully listening to the symptoms of the person in front of you and considering other causes in diagnosis remains unchanged.

When changes in walking or urination accompany memory loss, it's important not to explain it away as merely due to age and to consult a medical institution. The question is how much AI can be utilized as a tool to support such awareness.

Sources and References

1. AD HOC NEWS:
   Introduction to CT analysis research on normal pressure hydrocephalus and multiple dementia AI studies.
   https://www.ad-hoc-news.de/wissenschaft/normaldruckhydrozephalus-ki-erkennt-behandelbare-demenzform-mit-92-prozent-sensitivitaet/70351721

2. Frontiers in Neuroinformatics: Original paper on automatic identification of reference points in CT images
   Research subjects, external position identification evaluation, 92% sensitivity and 87% specificity for normal pressure hydrocephalus, results by comparison target confirmed.frontiersin.org
   https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2026.1853412/full

3. Tokyo Teishin Hospital: Explanation of Idiopathic Normal Pressure Hydrocephalus
   Refer to symptoms of walking, cognition, urination, image tests, tap test, and the flow of specialized medical care.Tokyo Teishin Hospital
   https://www.hospital.japanpost.jp/tokyo/shinryo/shinnai/inph.html

4. Frontiers in Artificial Intelligence: Original paper on ConvNeXt–KAN
   Performance differences between internal and external evaluations, limitations regarding patient unit data division confirmed.frontiersin.org
   https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1903281/full

5. Frontiers in Computational Neuroscience: Original paper on MEMOIR-VLM
   Refer to the performance of models integrating images and clinical information, evaluation challenges such as diagnostic information contamination.frontiersin.org
   https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2026.1902258/full

6. Reddit: Family's Consultation About a Father Suspected of Having Normal Pressure Hydrocephalus
   Source of SNS section. Summary of confusion about diagnosis, advice on consulting a specialist, and expectations for treatment. Not a direct reaction to the current AI research.help? : r/Hydrocephalus
   https://www.reddit.com/r/Hydrocephalus/comments/17xtgk1/father_may_have_nph_help/