The purpose of this work is to define fiducial points in the seismocardiogram (SCG) and to correlate them with physiological events identified in ultrasound images. For 45 healthy subjects the SCG and the electrocardiogram (ECG) were recorded simultaneously at rest. Immediately following the SCG and ECG recordings ultrasound images of the heart were also obtained at rest. For all subjects a mean SCG signal was calculated and all fiducial points  were identified and labeled in the same way across all signals. Eight physiologic events, including the valve openings and closings, were annotated from ultrasound as well and the fiducial points were correlated with those physiologic events. A total of 42 SCG signals were used in the data analysis. The smallest mean differences (±SD) between the eight events found in the ultrasound images and the fiducial points, together with their correlation coefficients (r) were: atrial systolic onset: −2 (±16) ms, r = 0.75 (p < 0.001); peak atrial inflow: 13 (±19) ms, r = 0.63 (p < 0.001); mitral valve closure: 4 (±11) ms, r = 0.71 (p < 0.01); aortic valve opening: −3 (±11) ms, r = 0.60 (p < 0.001); peak systolic inflow: 13 (±23) ms, r = 0.42 (p < 0.01); aortic valve closure: −5 (±12) ms, r = 0.94 (p < 0.001); mitral valve opening: −7 (±19) ms, r = 0.87 (p < 0.001) and peak early ventricular filling: −18 (±28 ms), r = 0.79 (p < 0.001). In conclusion eight physiologic events characterizeing the cardiac cycle, are associated with reproducible, well-defined fiducial points in the SCG. The first use of the word “seismocardiography” was in 1961 by Bozhenko3 and later by Baevski in 19644 who investigated the use of SCG in relation to space flights, as an alternative to BCG, the latter being a measure of the recoil forces of the body as response to the intermittent blood flow5 and which, at the time, could not be used in space6.Seismocardiography (SCG) is a technique of measuring the vibrations produced by the beating heart, where those vibrations are recorded from the chest using an accelerometer. The technique was introduced by Mounsey in 19578, but even though SCG promised to provide measures of cardiac contractivity and timing of events in the cardiac cycle, the technique never made it to the clinic and was again largely abandoned throughout the 1990s9. This was in part due to the advances in echocardiography and other medical imaging techniques9 and to the fact that the accelerometers used were large and heavy (up to 1 kg10) and thus not easy to use.After a period of relative silence, in 1990 Zanetti and Salerno introduced the technology in the USA11. Wearable systems have been studied in relation to changes in the SCG related to posture, walking and the assessment of heart failure. In 2016 De Rienzo et al. found that the pre-ejection period (PEP) and left ventricular ejection time (LVET), estimated from the SCG, were significantly different between supine and standing positions12 with recordings from the MagIC-SCG vest developed in 201011.The emergence of micro electro mechanical systems (MEMS) technology has made it possible to produce much smaller and lighter accelerometers, capable of recording with a high sensitivity, which transformed SCG from a measuring method mainly used in supine position, to a wearable technology14, although there is some disagreement as well16. Accurate determination of events, including AO, could make SCG a diagnostic tool for cardiac disease, comparable with the ECG. In addition, SCG could become an important tool in e.g., arterial pulse transit time (PTT) analysis for the assessment of vascular status20.SCG is thus being revised as an easy to use, non-invasive measuring technique of the cardiac performance and health status. The signal is deterministic and even though it is more detailed than, for instance, the electrocardiogram (ECG), the SCG fiducial points related to characteristic events in the cardiac cycle can be found among subjects. Especially the Aortic Valve Opening (AO) point has been investigated extensively and is consistent among most studies21 and, even though this imaging technique is limited in temporal resolution, echocardiography is indeed a helpful tool to determine the occurrence of cardiac events.However, the relation between SCG fiducial points and the physiological events in the cardiac cycle still lack sufficient evidence. Moreover, the literature is inconsistent on the location and even the definition of the fiducial points, which limits the use of the SCG. In some studies echocardiography was used to correlate some features in the SCG with cardiac eventsThe aims of this study were to define all waves in the SCG that are common among normal subjects, to define the fiducial points related to those waves, and to relate fiducial points and intervals between fiducial points to events in the cardiac cycle as observed in echocardiograms of the same subjects.In this study we collected SCGs and ultrasound data from a total of 45 healthy subjects without known heart diseases and correlated the fiducial points in the SCG with physiological events from the ultrasound images. The following sections describe the approach in detail.The subjects were recruited via posters and flyers placed at Aalborg University (Denmark), and in local shopping malls in the city of Aalborg.Diagnosed with any cardiovascular diseaseReceiving cardiovascular related medications such as medication for hypertension and hyperlipidaemiaInability to co-operate during the trial.Forty-five subjects were recruited. Inclusion criteria were: Healthy male and females at the age of 20–80 years. Exclusion criteria were:The study protocol was approved by the scientific ethical committee of Northern Jutland (N-20120069). Subjects signed a written inform consent before participating in the study. All methods were performed in accordance with relevant guidelines and regulations.For each subject basic demographic data  was recorded before the trial started. The subject was placed in a supine position and two accelerometers were attached to the subject’s skin with double adhesive tape. The accelerometers were Silicon Designs 1521 placed in a 3D-printed plastic housing .Three-lead ECG recording was performed with four electrodes, placed on the left and right shoulder and on the left and right iliac crests. The accelerometers and ECG were connected to an iWorx 228 data acquisition system and the signals were sampled at 5000 Hz to a PC using LabScribe software. Ultrasound images were recorded with a Vivid E9 . The ECG electrodes from the Vivid E9 were placed in the same locations as the electrodes used to obtain ECG via the iWorx 228. This ensured alignment of the data recorded via iWorx and the echocardiograph using the ECG signals recorded on the two devices. The Vivid E9 ultrasound system was tested for a potential delay between the ECG signal and the image. This delay was found to be less than one frame (with an acquisition rate of 70 frames per second).The subject lay supine for three minutes while recording baseline measurements of ECG and SCG. One accelerometer was placed in the xiphoid process for recording of SCG signal while one was placed in the inter costal space 4 (IC4). As described later, the signal recorded in IC4 was used in the signal processing step as a proxy for heart sound. Afterwards the three ultrasound images were recorded: Apical 4 and 5-chamber view with Pulsed Wave Doppler (PWD) and M-mode Tissue Doppler Image (TDI) of the mitral valve leaflets. During the echocardiography the iWorx system was still recording. Following the echocardiographic procedure data was recorded for 5 more minutes before the trial ended.et al.22. This method uses ECG lead I or II to divide the signal into individual heart beats based on the ECG R-peak. A mean SCG beat was calculated as well as a mean ECG beat, based on these individual beats, using the approach described in the following.All recorded data from the iWorx system was exported and processed in MATLAB . The signals were segmented into beats using the method described by Jensen st order low-pass Butterworth filter with a cutoff frequency of 90 Hz and then forward-backward filtered with a 3rd order high-pass Butterworth filter at 0.05 Hz. Using the algorithm described in22 allowed for alignment of the beats used to calculate the mean beat with respect to either the R-peak of the ECG or the diastolic complex. Annotation of fiducial points in the systolic complex was thus done with the beats aligned to the R-peak of the ECG. For fiducial points in the diastolic complex the SCG signals were aligned in time to second heart sound (S2). The heart sounds were obtained by filtering the acceleration signal from IC4 with a Butterworth bandpass filter of 1st order and a cutoff frequency of 50 and 500 Hz. S2 was located as the second largest peak in the autocorrelation of the median heart sound.For the annotation process a mean SCG beat for each subject was calculated, where the systolic part and the diastolic part were separately aligned and averaged to consider the variable duration of the systole. The SCG signals were first filtered forward-backward with a 122.The lag between the individual beats and the peak of the envelope was calculated using the cross correlation and by aligning the beats with respect to the median heart sound. A new median beat was then computed as well as the envelope to find the peak of the S2 peak again and to realign the individual beats once more using the cross correlation. This ensured that all beats were aligned to a median heart sound, that was not influenced by the initial alignment to the R-peak22. This ensured that the mean beat is composed of beats with similar characteristics.As part of the segmentation method noisy beats were rejectedBecause of the variability of the S1-S2 interval this procedure gave significantly different averages as compared with alignment based on the R-peak. The autocorrelation alignment thus ensured that no events were neglected to be marked: see Fig. All SCG signals were manually annotated to ensure that fiducial points of the same characteristics were labeled the same way. During the labeling of the fiducial points the operator was blinded for the corresponding ultrasound images. A custom tool was developed in MATLAB for this purpose. The SCG signals were annotated in a process of two steps. First, all significant peaks and valleys in all mean beats were marked. In the second step the different events in the SCG signals were labeled using a simple naming scheme of the letters from A to M followed by subscript to indicate the systolic (s) or the diastolic (d) complex. Thus, As is fiducial point A in the systolic complex and Ad is fiducial point A in the diastolic complex. In this step fiducial points of similar characteristics were labeled with the same letter across the subjects. Thus, after this step the mean beats for some subjects could have fewer points labeled than for other subjects, depending on how many points could be identified in each subject. To a large extent the annotation process was iterative, and the operator went through the mean beats multiple times. Figure t = 0. This negative peak was labeled Fs .In three cases a shoulder near the peak of the systolic complex was labeled as fiducial point Gs was labeled Js. The last valley marking the end of the systolic period was labeled Ms. The peak and valley before Ms were labeled Ls and Ks respectively.The peak following the fiducial point Id. This point was not always present as a distinct valley in which case the onset of the positive acceleration in the diastolic complex was labeled Ad, see Fig. In the diastolic complex, the first valley was labeled Ad. This fiducial point was in some cases present as the shoulder of a steep negative acceleration towards the valley labeled Cd , MC, AO, aortic valve closure (AC) and mitral valve opening (MO), in accordance withDuring the process of measuring the timings the operators were blinded for the corresponding SCG signals. Thus, operator bias was minimized.Onset of the A-wave in the 4-chamber PWD Images and Atrial Systole found in the TDI ImagesOnset of the aortic flow profile found in the 5-chamber view PWD versus the AO from TDIEnd systolic flow profile from the 5-chamber view and the AC from the TDIOnset of the E-wave from the 4-chamber PWD Images and the Mitral Valve Opening from TDI.The Pulsed Wave Doppler images and the Tissue Doppler images were analyzed by two different operators respectively. To ensure that the operators agreed on where to place the markers for the different events, the following points were compared:Differences in timing >15 ms between the two images were reevaluated by the operators, although a reevaluation of a particular point did not always yield in a better agreement. For each of these four physiological events a mean time was calculated between the two image modalities to mitigate potential errors. Thus, each time point for e.g. Aortic Valve Opening consists of the mean of the times from both the TDI and PWD images.All the time points of the fiducial points in the SCG and of physiological events in the ultrasound images were tested for normality by visualizing the histogram and probability plot of the data points. As normal distribution was observed among all data points Pearson’s Linear Correlation Coefficient was used to calculate the correlation between the fiducial points in the SCG and the timing of the physiological events found in the ultrasound images. All events from the systolic complex of the SCG signal were correlated with all physiological events during that complex and likewise for the diastolic complex. P-values < 0.05 was considered significant for rejecting the null hypothesis of no correlation between fiducial points and physiological events.Besides the correlation, the mean differences between the fiducial points from the SCG and their corresponding time points from the ultrasound images were calculated together with their standard deviations. The time difference in combination with the correlation should give an indication as to which fiducial point from the SCG best corresponds to a certain physiological event found in the ultrasound images. For these points the beta coefficients for the linear regression is presented.A total of 45 subjects participated in the study. One subject from the original 45 subjects decided to withdraw. Two subjects were excluded due to abnormal SCG signals with unidentifiable fiducial points. Thus, a total of 42 SCG signals were included in the study. For one subject the signal recording prior to the ultrasound scanning was interrupted by noise thus the signal recording following the ultrasound scanning was used instead. The mean age for the included subjects was 46.8 (±17.4) years with a mean weight of 72.9 (±12) kg and mean height of 174.9 (±8) cm and 52% of the subjects were female. The mean RR-interval was 950 (±177) ms.Table s and point Hs.Table s with a mean difference of −2 ms (±16 ms). The correlation between the SCG fiducial point and the ultrasound image time location is 0.75 with p < 0.001.The lowest difference between A-wave-onset and AS in the echocardiograms and any fiducial point in the SCG was found for point Bs with a mean difference of 13 ms (±19 ms) and a correlation of 0.63 (p < 0.001).The shortest time difference between the peak of the A-wave and the fiducial points was found for point Cs with a mean difference = 4 ms (±11 ms). The correlation for this fiducial point is 0.71 with a p-value < 0.001.The mean difference between the fiducial points and the MC event in the images is lowest for fiducial point Es has the lowest mean and absolute mean time difference to the mean time point (mean difference = −3 ms (±11 ms)). The correlation is 0.60; p < 0.001. Figure s and Aortic Valve Opening is 0.70 instead of 0.60 – still with a p < 0.001.The mean time point from the onset of the systolic profile in the tissue Doppler images and the aortic valve opening found on the pulsed Doppler flow images were correlated with the fiducial points from the systolic complex of the SCG. Fiducial point Gs has the shortest mean time difference of 13 ms (±23 ms) to the event from the ultrasound image. The correlation is 0.42 (p < 0.01).The fiducial point KTable d. The correlation between the fiducial point and time point is 0.94 (p < 0.001).The time point for AC was found in both the tissue Doppler image and the pulsed wave Doppler as the end of systolic flow. The shortest time difference between the fiducial point of the SCG and the time point for aortic valve closure is −5 ms (±12 ms) and is located at point Bd. The correlation is 0.87 (p < 0.001).For the MO time point a combination of the tissue Doppler image and the onset of the E-wave in the pulsed wave Doppler was used. The shortest mean time difference of −7 ms (±19 ms) is to the fiducial point Ss with a correlation of 0.79 (p < 0.001).For the peak of the E-wave the fiducial point with the shortest time difference (mean difference = −18 ms (±28 ms)) was UAll the points evaluated above with a short time interval between a particular fiducial point and the corresponding physiologic event in the ultrasound image, are listed in Table s, Cs, Ds, Es, Fs, Gs, Ks, Bd, Fd and Hd were used as fixed points in a time warping approach to define one overall mean SCG and ECG signal. The signals were segmented into parts based on the aforementioned fiducial points. A segment from signal start to fiducial point Bs and a segment from Hd to the end of the signal was also created, making a total of 11 segments across those 34 signals that contained all the fiducial points. For each segment the mean length was calculated, and all segments were resampled to fit this length with Matlab’s “spline” function. Thus, following this step all signal parts of the same segment were of the same length. Using the original length of each signal part and the new length of the same part a “resampling factor” was calculated and used to reposition the location of the ultrasound event to the new length of the signal parts. An overall mean SCG signal was computed based on the parts of signals, see Fig. The fiducial points BIn this work we propose a nomenclature for the various waves and wave complexes in the normal seismocardiogram and we have identified the main physiological events in the cardiac cycle relative to those waves. A total of 42 SCG signals were manually annotated and the times of occurrence of the fiducial points were correlated to the occurrences of the physiological events in ultrasound images.21. Only the original SCG paper describes cardiac events taking place on the slopes of the signal and not at the fiducial points1. Our results points towards the same as Mounsey’s: the physiologic event does not always occur at the fiducial point but before or after.Current literature visualizes cardiac events as occurring at the local extrema of the SCG signals is related to the atrial systole according to our results, see Table s marks the beginning of the negative slope describing the atrial systole in Mounseys paper. Likewise, the following positive slope in Mounseys paper is the same as the positive slope from our results. This corresponds to the positive slope following fiducial point Cs. According to the ultrasound images the onset of the echocardiographic A-wave  is just prior to Bs whereas the beginning of the slope following fiducial point Cs marks the peak of the A-wave . The acceleration deflection corresponds to an initial outwards deflection in displacement followed by an inwards deflection  ms after the R-peak, thus not at the same location as suggested by Mounsey.Fiducial point Es is the isovolumetric contraction segment. Before the aortic valve opens the heart start to contract to build enough pressure in the left ventricle to exceed the pressure in the aorta. The acceleration is first negative, then positive. The corresponding displacement signal is an outward deflection. This could be due to a rounding of the ventricles as they contract, and pressure is built. The outward displacement deflection stops as the acceleration deflection is at the valley prior to fiducial point Gs. From this point the positive acceleration describes the inward displacement, possibly due to the blood being forced out of the ventricles thus causing the heart to flatten. This corresponds with the finding by Mounsey.Following Es is caused by an inward displacement during what Mounsey calls “reduced ejection” period1.The positive acceleration starting at fiducial point Kd is associated with the closing of the aortic valve1. Just before this event a positive deflection of displacement starts. This deflection continues to fiducial point Ed. The outward and following inward displacement could be due to the forces created by the closure of the aortic valve. Blood in the aorta forces the valves to close causing a movement of the heart towards the apex of the heart. Mounsey explains this further by comparing two recordings of the SCG, one from the upper sternal area and one from the apical area. The deflections in the two recordings are opposite to each other. In the upper sternal area the displacement was upward instead of downward, resulting from an inward displacement over the area of the valves1.Fiducial point Bd is located in the interval containing of the mitral valve opening. The positive acceleration leading up to this fiducial point is associated with an inward displacement deflection. Mounsey explains these changes with a possible change in position of the heart in this period.Fiducial Point Fd is associated with early ventricular filling. This positive deflection in acceleration is associated with a positive displacement possibly caused by blood filling the ventricles as the mitral valve and tricuspid valves opens. Mounsey proposes the same explanation for this positive displacement deflection.The last fiducial point HFor the three locations peak atrial inflow, peak systolic inflow and peak early ventricular filling the difference between these physiologic events and the closest fiducial points were significantly different from zero.With regard to accurately determining the events in the ultrasound images it should be noted that this process is not perfect. The quality of the profile of the flow or tissue movement was varying, such that an accurate determination of the occurrence of the events still is a subjective task. In this study, the operator was blinded for the corresponding SCG signal when determining the events in the ultrasound image. This eliminates the bias towards selecting events that could fit any hypothesis the operator might have with regard to where to locate echocardiographic events relative to the SCG.et al.27. This would result in a relation between the SCG signal and the evolution of the shape of the contractions, instead of the locations defined in this paper.A different approach for a study like this, could be to use homologous time points as described by Piras The main limitation in this study is the low temporal resolution of the ultrasound modalities compared to that of the SCG signal. When analyzing the ultrasound images, the low resolution limits the operator’s ability to accurately mark the events. Moreover, the ultrasound images consist of only 1–4 consecutive beats whereas the SCG signals are composed of more than 20 beats, thus a more general representation of the SCG signal from a subject compared to the ultrasound image. This could lead to inconsistency between the SCGs and the ultrasound images due to variations of the heartbeat, for instance introduced by the respiration component of the signals.We have defined fiducial points in the SCG and associated them with physiologic events obtained from cardiac ultrasound images and we found a consistent relationship between SCG fiducial points and the events. Furthermore, this study introduces a new location for the MO event.All SCG Signals from Dataset


Chiari‐like malformation (CM) is a complex malformation of the skull and cranial cervical vertebrae that potentially results in pain and secondary syringomyelia (SM). Chiari‐like malformation‐associated pain (CM‐P) can be challenging to diagnose. We propose a machine learning approach to characterize morphological changes in dogs that may or may not be apparent to human observers. This data‐driven approach can remove potential bias (or blindness) that may be produced by a hypothesis‐driven expert observer approach.To understand neuromorphological change and to identify image‐based biomarkers in dogs with CM‐P and symptomatic SM (SM‐S) using a novel machine learning approach, with the aim of increasing the understanding of these disorders.Thirty‐two client‐owned Cavalier King Charles Spaniels .Retrospective study using T2‐weighted midsagittal Digital Imaging and Communications in Medicine (DICOM) anonymized images, which then were mapped to images of an average clinically normal CKCS reference using Demons image registration. Key deformation features were automatically selected from the resulting deformation maps. A kernelized support vector machine was used for classifying characteristic localized changes in morphology.Candidate biomarkers were identified with receiver operating characteristic curves with area under the curve (AUC) of 0.78  for the CM‐P biomarkers collectively and an AUC of 0.82  for the SM‐S biomarkers, collectively.Machine learning techniques can assist CM/SM diagnosis and facilitate understanding of abnormal morphology location with the potential to be applied to a variety of breeds and conformational diseases. AUCarea under the curveCKCSCavalier King Charles SpanielCMChiari‐like malformationCM‐Ncontrol dogs: no SM no CM pain; 4 years of age and olderCM‐Ppain associated with Chiari‐like malformationCSFcerebrospinal fluidDICOMDigital Imaging and Communications in MedicineFPRfalse‐positive rate ICCintraclass correlation coefficientMRImagnetic resonance imagingPCAprincipal component analysisROCreceiver operating characteristicSFFSsequential floating forward selectionSMsyringomyeliaSM‐Ssyringomyelia and associated clinical signsSVMsupport vector machineTPRtrue‐positive rate 1Syringomyelia (SM) is characterized by the development of fluid‐filled cavities within the spinal cord. The pathogenesis of SM is debated, but there is consensus that it is associated with obstruction of cerebrospinal fluid (CSF) channels, especially when that obstruction is at the craniocervical junction and foramen magnum. In dogs, SM most commonly is associated with Chiari‐like malformation (CM),Depending on the site and extent of spinal cord damage, SM may result in behavioral signs of pain, phantom (fictive) scratching, scoliosis, weakness, and sensory deficits although some dogs may be asymptomatic, especially if the syrinx is narrow and centrally located.Diagnosis of CM and SM requires magnetic resonance imaging (MRI). Whereas identification of SM is straightforward, assessment of CM is more difficult. Originally described in 2000 as a small‐volume caudal fossa with cerebellar herniation,Traditional morphometric studies also have the potential disadvantage that they are hypothesis‐driven . Our proposed method is based on a purely data‐driven approach. In other words, we propose to let the data “speak” in an unbiased way. This is facilitated by using machine learning methods to discover underlying patterns in the image data, which may or may not be immediately obvious, and that are not guided by human intervention . Thus, our aim was to identify potential imaging biomarkers of SM‐ and CM‐associated pain within the entire cranium.2The methodology was divided into 4 main sections, namely image preprocessing, feature extraction, biomarker identification using machine learning, and biomarker mapping Figure . This piThe method starts by using an image registration step. Image registration is a process sometimes referred to as mapping, morphing, or warping to align the subject in an image of interest onto another image. Furthermore, performing this step accommodates differences in head size that may occur as a consequence of sex, neuter status, and age. In the methodology described here, we selected an average control subject as our reference image and then registered dogs either in our diseased group or in the control group onto this reference image. The registration process produces a warp field or deformation image, which illustrates the displacement of each pixel in our clinically affected or control images when these are mapped or registered onto our reference subject Figure . We thenWe then can map these features back to the original images to allow us to infer the key locations in the brain that are deformed in diseased dogs compared with our control dogs, in order to identify the locations of potential image‐based biomarkers for the target disease.CM‐N (control): no SM, no CM‐P; ≥4 years of age (n = 10);CM‐P: no SM with clinical and behavioral signs or both of pain associated with CM‐PSM‐S: clinically severe SM with syrinx transverse diameter ≥4 mmThe investigation was carried out on a data set compiled from midsagittal T2‐weighted head and cranial cervical MRI obtained from 32 CKCS dogs, imaged using the same MRI machine. Demographical data of the total cohort are  summarized as follows:The diagnosis of CM‐P is suggested in predisposed breeds presenting with multiple signs suggesting pain such as a history of vocalization without obvious trigger, when shifting position, when recumbent, and when being lifted under the sternum to a height; spinal pain; head and ear rubbing or scratching; refusal or difficulty to jump or use stairs; exercise intolerance or decreased activity; sleep disruption; or behavioral change described as becoming more anxious, aggressive, or withdrawn.The value of ≥4 mm as a clinically relevant syrinx was decided on the basis of a previous study that identified this measurement as the cutoff for SM‐specific clinical signs.2.1The process is initiated by identifying a control reference dog  by using all of the imaging features of the control group extracted in previous studies.2.2In order to spatially align each image into a single coordinate space, an initial affine image registration step was used . This provides an approximate alignment, eliminates volumetric differences caused by use of different scanners, and minimizes any effects caused by setup errors during the MRI acquisition process.After affine registration, the morphological differences between each dog and the clinically unaffected reference dog were obtained using an adaptation of the Demons nonrigid registration method.2.3Feature selection is a “long‐listing” step that makes a first pass to decrease the list of potential features that might be used to separate the 2 cohorts. In this case, we used features associated with the deformation field to identify the morphological differences between a query dog and the reference dog. These deformation fields then can be grouped or clustered into 2 distinct subject types: a control group and a group with abnormal morphology. This was carried out using different features extracted from the images. The features used were the individual polar coordinates  of each of the MR image pixels that represent the deformation field, and the determinant of the Jacobian of the deformation field.To overcome any residual misalignments during the image acquisition process or associated with the image registration process, the deformation field was down sampled onto a coarser voxel grid before the feature extraction step. Because the degree of any potential misalignment was unknown, 3 different levels of downsampling were applied to the deformation field images: resampling at 3 × 3 pixels, 3.75 × 3.75 pixels and 5 × 5 pixels. This produced a smoothing effect on the deformation field, resulting in 3 downsampled data sets for CM‐P and 3 data sets for SM‐S. These were subsequently analyzed to understand the consistency of any features that might be found to separate the 2 types of subject  morphology present.2.4x,y coordinates) or polar coordinates. We chose to use the polar coordinate system, which represents the distortions or displacements of individual pixels as the magnitude of the displacement and the direction of displacement for each pixel, as opposed to its component deformation in the x‐ and y‐directions.The movement or displacement of a given pixel in the query image when registering to the reference image can be expressed in Cartesian  was used to identify the most salient components in the data, facilitating the removal of less important components, such as noise or anomalies. Principal component analysis identifies combinations of feature components from the data that represent the major sources of variance and ranks these in order of saliency.2.7Initial identification of a broad set of potential candidate biomarkers was carried out using a machine learning technique known as feature selection. In the first experiment, we attempted to make an initial separation of dogs with CM‐P from those that do not have a history of behavior suggesting pain, wherein clinical examination by a veterinary neurologist did not find any evidence of spinal or head pain and where spinal MRI was reported as normal (CM‐N). In a second experiment, we attempted to distinguish between dogs that have clinically severe SM (SM‐S) and the controls (CM‐N).2.8The feature selection technique used in our study is known as sequential floating forward selection (SFFS)Figure This process was repeated 250 times using different random orderings of features to account for any potential effects of ordering selection. Thus a set of candidate biomarkers was identified that contained the top 5 scoring features using SFFS.2.9The final step in the methodology is classification, which attempts to optimally separate, or classify, the 2 groups of dogs . After the “long‐listing” step of feature selection to identify the most promising candidate features, we then used a state‐of‐the‐art classification algorithm to refine the way these features can be used to best separate the 2 cohorts of dogs. In this case, a kernelized support vector machineIn order to assess the quality of the separation or classification method, ROC curves were calculated using the clinically defined labels  to estimate the true‐positive rate  and false‐positive rate  for the top 5 features (collectively).In order to understand the potential variation of the neuromorphological patterns within the data, a process of cross‐validation was used. As part of this process, the data are split into 2 groups, a training set in which the SVM learns how best to separate control from SM/CM‐affected subjects, and a test set of previously unseen subjects from the original data set (same breed) that are used to test the performance of the classification process. In the first experiment , 5 randomly selected subjects from the entire data set of 26 cases were removed and considered as the test set. Thus, testing was undertaken on a set of images that the machine had not seen before. The SVM then was trained on the remaining 19 cases. The 5 test cases then were classified as part of the assessment. This process was repeated with >1000 combinations of training: test selections. The mean TPR and FPR were plotted for each, and the area under the curve (AUC) was computed. The variance also was recorded at each point to estimate the error. The same approach was taken for the second experiment , with 5 subjects of the entire 18 cases excluded from the training process.2.10The entire feature extraction and biomarker identification pipeline Figure  was appl33.1The pipeline was first applied to an experimental data set consisting of 2 classes: a control group with CM (CM‐N) and a group diagnosed with CM‐P (CM‐P). The set of features that best separated morphological pathologies from CM‐N  was mapped back onto the reference image identifying their anatomical locations. The 5 most relevant features identified by the SFFS algorithm, at 3 different resampling rates, are shown in Figure Figure 3.2Using the same approach as explained above, the following results were achieved for the data set consisting of 2 classes: CM‐N and SM‐S. Figure 4We used a novel machine learning approach to produce a data‐driven analysis for identifying characteristic patterns associated with CM‐P and SM‐S in a midsagittal MRI slice. The advantage of this approach is that subtle characteristics in the image data can be identified that may not be immediately apparent on observation. The methodology developed here used a combination of image registration to identify neuromorphological differences associated with particular disease states, and machine learning to understand the location of the most important traits derived from the registration patterns, and identify when pathology was present or not. Although the method could be used in a variety of disorders, we have used CM‐P and SM‐S to illustrate the approach.To allow for uncertainties and errors associated with variations in setup during MRI examination, as well as localized errors in the registration process, the associated deformation fields were downsampled to a coarser resolution, or grid, to smooth over minor inconsistencies. Thus, the analysis was undertaken over several levels of downsampling. Nonetheless, we demonstrated that the locations of many of the resulting key features (candidate biomarkers) were consistent, suggesting that the impact of downsampling was minimal. The discriminatory performance of these candidate biomarkers was assessed by ROC analysis. Anatomical features of interest can be observed in Figure 4.1The AUC for the ROC curves that were plotted to validate CM‐P features is shown in Figure In Figure A cluster in the region of presphenoid bone and the optic chiasm immediately rostral to the sella turcica is clinically consistent. Early closure of the basispheno‐presphenoid synchondrosis and therefore a shortened presphenoid bone has been associated with brachycephaly and airorhynchy. Early fusion of the basisphenoid presphenoid synchondrosis is a feature in mouse models of Crouzon syndrome, which is a complex craniosynostosis syndrome that can be associated with Chiari type I malformation and which has some similarities to CM in dogs.Features of the corpus callosum and third ventricle may be related to the ventriculomegaly that often is associated with CM.4.2The features related to SM were consistent over the 3 different resampling rates. Figure From the comparison of the locations of the top scoring features related to CM‐P with those related to SM, it is evident that both conditions are associated with a common feature of the presphenoid bone. Other CM markers in the brain are dorsal to this region, and there is also a marker common to CM and SM in the soft palate ventral to this bone.In the case of SM classification, for the ROCs produced by combining the results of the top 5 features into the classifier, shown in Figure 4.3The image registration process may be prone to errors because the mid‐sagittal slices of each MRI scan were not aligned because of differences in orientation of the dog within the MRI scanner. Furthermore, the machine learning techniques used in our study were chosen to suit a smaller data set, but a larger data set of dogs may allow the use of more sophisticated techniques and provide further insight into CM‐P and SM. Another limitation was the small number of normal dogs and the fact that the reference average subject was used as normal for all comparisons. It is not known whether this subject would reflect the average for the entire population.4.4Future directions for research should aim at improving the registration process in order to address the difference in the dog's orientation during image acquisition so as to produce a more robust pipeline. The clinical implications of our findings should be explored further to try to understand the underlying mechanisms of CM associated pain and SM. As part of a future study, it would be interesting to compare the discriminability of the identified markers to that of traditional markers of CM and SM, as well as to test the discriminability of these markers on dogs from different pedigrees and origin.The methodology described here was developed using in‐house software based on MATLAB—a specialist software development environment for scientific research purposes. Because ours was a proof‐of‐concept study, the associated software is not currently suitable for routine use, but the techniques described here potentially could form part of a commercial product after further development.5We evaluated a fully automated pipeline for the identification of potential biomarkers in CKCS dogs with SM and CM associated pain, with the aim of expanding the knowledge base of these disorders. From the comparison of dogs with CM‐P with the control group, the main regions identified as diagnostically relevant candidate biomarkers for this condition are the floor of the third ventricle and closely associated neural tissue, and the region in sphenoid bone (presphenoid and basisphenoid) around the sella turcica. There are also lesser areas of interest in caudal nasal cavity close to the dorsal cribriform plate, between the soft palate and the tongue, on the tip of the odontoid process.The main regions identified as diagnostically relevant biomarkers when comparing dogs with a specific diagnosis of SM to a control group were the presphenoid bone and the region between the soft palate and the tongue. Both experiments have yielded biomarkers in the presphenoid bone and the area between the soft palate and the tongue, which indicates both conditions being strongly related to changes within this area. Further work is needed to explore the further development of the image registration process, which in turn is expected to improve the AUC values.We were able to successfully discriminate between CM‐P subjects and those without, and for those with SM‐S, and those without, using a set of biomarkers discovered using machine learning. This work can be used as a basis to build a clinical diagnostic test based on registering these key locations in a query subject back to the equivalent landmarks on a reference subject free of such pathology. Further analysis could be done to investigate whether a link exists between disease severity and the magnitude of the neuromorphological distortion indicated by these biomarkers.Authors declare no conflict of interest.Authors declare no off‐label use of antimicrobials.This retrospective study analyzed Digital Imaging and Communications in Medicine (DICOM) data obtained from dogs that underwent MRI either for diagnostic purposes for assessment of CM/SM status before breeding or for diagnostic investigation of neurological signs, pain, or both.Authors declare human ethics approval was not needed for this study.