Ischemic heart disease, typified by myocardial infarction and angina pectoris,
is a serious group of conditions in which stenosis or occlusion of the coronary
arteries impedes blood flow to the myocardium,
causing chest pain and myocardial injury.
Plaque that forms in the vessel wall as atherosclerosis progresses is the
principal cause of this stenosis: as plaque enlarges,
the vessel lumen narrows and the blood supply to the myocardium is reduced.
Such patients are treated with coronary artery bypass grafting or percutaneous
coronary intervention (PCI); of these, PCI---a catheter-based,
minimally invasive treatment---has become widely established.

However, PCI carries the risk of complications known as the no-reflow phenomenon
and slow flow.
No-reflow refers to a state in which, although the stenosis itself is resolved
by stent placement or similar procedures,
peripheral blood flow is completely obstructed because plaque contents or
thrombus released during dilation of the lesion occlude the distal
microvasculature (distal microembolization).
Slow flow is a state in which blood flow is not completely obstructed but is
markedly delayed.
No-reflow and slow-flow phenomena have been reported in 17.1\% and 12.9\% of
primary PCI patients in one study \citep{babapoor2022},
and in approximately 10\% of primary PCI patients in another \citep{wu2011}.
Because both cut off the myocardial oxygen supply,
they necessitate additional interventions,
prolonging procedure time and worsening patient outcomes.
Against this background, establishing a preventive strategy that forestalls the
onset of these complications altogether---rather than addressing them after they
occur---has become a critical challenge.

When the risk of no-reflow or slow flow is found to be high before the
procedure, clinical practice takes preventive measures such as placing a distal
protection device distal to the lesion before stent deployment,
or administering vasodilators intracoronarily in advance.
While distal protection devices are effective for high-risk lesions,
applying them to every case is not realistic given the burden of preparation,
making accurate pre-procedural risk stratification essential.
Intracoronary vasodilator administration also carries the drawback of
systemic side effects such as hypotension \citep{rezkalla2017}.
We therefore focused on intravascular ultrasound (IVUS),
which can confirm the condition of the vessel wall and plaque during the PCI
procedure \citep{hibi2013}.
However, interpreting IVUS images requires specialized expertise,
and because multiple factors---such as vessel diameter and plaque
composition---interact in complex ways, high-precision risk prediction by visual
assessment alone is difficult, creating a need for methods that quantitatively
assess risk from IVUS images.

In AMI patients, a significant difference in the amount of attenuated plaque
(quantified via a manual attenuation scoring system) has been reported between
patients whose post-PCI angiographic flow met a composite no-reflow/slow-flow
definition (TIMI flow grade $\leq$2) and those who did not \citep{wu2011}.
The mere presence of attenuated plaque did not differ significantly between
the two groups; only its extent did.
This suggests that a greater amount of attenuated plaque may make thrombus
formation and microvascular occlusion more likely,
thereby increasing the risk of complications.
However, this scoring remains a manual assessment performed by physicians,
motivating the need for an automated and objective predictive approach.

Information useful for predicting no-reflow and slow flow is thought to lie at
the intersection of the IVUS images themselves,
the patient's clinical variables, and the morphology of the lesion.
At the same time, because these complications are relatively rare in themselves,
and positive cases are even more limited among the cases whose outcomes are
labeled, it is difficult to learn all of this information in an end-to-end
manner.

Building on the above, this study proposes KSCNet,
an architecture that integrally handles IVUS images,
patient clinical information, and lesion plaque features.
KSCNet is a redesign, adapted to the structure of IVUS data,
of a 3D architecture originally developed for kidney stone classification
\citep{wang2026}.
Because an IVUS pullback is not an isotropic 3D volume like a CT scan but rather
an anisotropic sequence of 2D cross-sections arranged along a time series,
we adopt, in place of 3D convolution, a weight-shared,
per-frame 2D encoder together with sequence modeling via a Transformer across
frames.
Specifically, it integrates three elements: (1) a mechanism that injects a
feature vector---combining patient-level clinical information and plaque
features (such as attenuated-plaque area)---as an additional token into the
frame sequence, allowing it to interact with the image representation;
(2) a mechanism that uses segmentation of the vessel lumen and vessel wall as an
auxiliary task and aggregates case-level features via cross-attention,
guided by those features; and
(3) a multi-task loss that dynamically weights the segmentation loss and the
classification loss.
This allows us to verify whether combining IVUS images, clinical information,
and lesion features contributes to improved predictive performance,
compared with methods that use only images,
tabular methods that use only clinical information,
and existing attenuated-plaque scoring methods.