Spiral Drawing Research Articles

Architecture-Aware Augmentation: A Hybrid Deep Learning and Machine Learning Approach for Enhanced Parkinson’s Disease Detection

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide. Early detection is crucial for improving patient outcomes. Spiral drawing analysis has emerged as a non-invasive tool to detect early motor impairments associated with PD. This study examines the performance of hybrid deep learning and machine learning models in detecting PD using spiral drawings, with a focus on the impact of data augmentation techniques. We compare the accuracy of Vision Transformer (ViT) with K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN) with Support Vector Machines (SVM), and Residual Neural Networks (ResNet-50) with Logistic Regression, evaluating their performance on both augmented and non-augmented data. Our findings reveal that ViT with KNN, initially achieving 96.77% accuracy on unaugmented data, experienced a notable decline across all augmentation techniques, suggesting it relies heavily on global patterns in spiral drawings. In contrast, ResNet-50 with Logistic Regression showed consistent improvement with data augmentation, reaching 93.55% accuracy when rotation and flipping techniques were applied. These results highlight that hybrid models respond differently to augmentation, and careful selection of augmentation strategies is necessary for optimizing model performance. Our study provides important insights into the development of reliable diagnostic tools for early PD detection, emphasizing the need for appropriate augmentation techniques in medical image analysis.

Recognition analysis of spiral and straight-line drawings in tremor assessment.

No standard, objective diagnostic procedure exists for most neurological diseases causing tremors. Therefore, drawing tests have been widely analyzed to support diagnostic procedures. In this study, we examine the comparison of Archimedean spiral and line drawings, the possibilities of their joint application, and the relevance of displaying pressure on the drawings to recognize Parkinsonism and cerebellar dysfunction. We further attempted to use an automatic processing and evaluation system. Digital images were developed from raw data by adding or omitting pressure data. Pre-trained (MobileNet, Xception, ResNet50) models and a Baseline (from scratch) model were applied for binary classification with a fold cross-validation procedure. Predictions were analyzed separately by drawing tasks and in combination. The neurological diseases presented here can be recognized with a significantly higher macro f1 score from the spiral drawing task (up to 95.7 %) than lines (up to 84.3 %). A significant improvement can be achieved if the spiral is supplemented with line drawing. The pressure inclusion in the images did not result in significant information gain. The spiral drawing has a robust recognition power and can be supplemented with a line drawing task to increase the correct recognition. Moreover, X and Y coordinates appeared sufficient without pressure with this methodology.

Utilizing deep learning models in an intelligent spiral drawing classification system for Parkinson's disease classification.

Parkinson's disease (PD) is a neurodegenerative illness that impairs normal human movement. The primary cause of PD is the deficiency of dopamine in the human brain. PD also leads to several other challenges, including insomnia, eating disturbances, excessive sleepiness, fluctuations in blood pressure, sexual dysfunction, and other issues. The suggested system is an extremely promising technological strategy that may help medical professionals provide accurate and unbiased disease diagnoses. This is accomplished by utilizing significant and unique traits taken from spiral drawings connected to Parkinson's disease. While PD cannot be cured, early administration of drugs may significantly improve the condition of a patient with PD. An expeditious and accurate clinical classification of PD ensures that efficacious therapeutic interventions can commence promptly, potentially impeding the advancement of the disease and enhancing the quality of life for both patients and their caregivers. Transfer learning models have been applied to diagnose PD by analyzing important and distinctive characteristics extracted from hand-drawn spirals. The studies were carried out in conjunction with a comparison analysis employing 102 spiral drawings. This work enhances current research by analyzing the effectiveness of transfer learning models, including VGG19, InceptionV3, ResNet50v2, and DenseNet169, for identifying PD using hand-drawn spirals. Transfer machine learning models demonstrate highly encouraging outcomes in providing a precise and reliable classification of PD. Actual results demonstrate that the InceptionV3 model achieved a high accuracy of 89% when learning from spiral drawing images and had a superior receiver operating characteristic (ROC) curve value of 95%. The comparison results suggest that PD identification using these models is currently at the forefront of PD research. The dataset will be enlarged, transfer learning strategies will be investigated, and the system's integration into a comprehensive Parkinson's monitoring and evaluation platform will be looked into as future research areas. The results of this study could lead to a better quality of life for Parkinson's sufferers, individualized treatment, and an early classification.

An Algorithm for Automated Measurement of Kinetic Tremor Magnitude Using Digital Spiral Drawings

Introduction: Essential tremor is a common movement disorder. Numerous validated clinical rating scales exist to quantify essential tremor severity by employing rater-dependent visual observation but have limitations, including the need for trained human raters and the lack of precision and sensitivity compared to technology-based objective measures. Other continuous objective methods to quantify tremor amplitude have been developed, but frequently provide unitless measures (e.g., tremor power), limiting real-world interpretability. We propose a novel algorithm to measure kinetic tremor amplitude using digital spiral drawings, applying the V3 framework (sensor verification, analytical validation, and clinical validation) to establish reliability and clinical utility. Methods: Archimedes spiral drawings were recorded on a digitizing tablet from participants (n = 7) enrolled in a randomized placebo control double-blinded crossover pilot trial evaluating the efficacy of oral cannabinoids in reducing essential tremor. We developed an algorithm to calculate the mean and maximum tremor amplitude derived from the spiral tracings. We compared the digitally measured tremor amplitudes to manual measurement to evaluate sensor reliability, determined the test-retest reliability of the digital output across two short-interval repeated measures, and compared the digital measure to kinetic tremor severity graded using The Essential Tremor Rating Assessment Scale (TETRAS) score for spiral drawings. Results: This algorithm for automated assessment of kinetic tremor amplitude from digital spiral tracings demonstrated a high correlation with manual spot measures of tremor amplitude, excellent test-retest reliability, and a high correlation with human ratings of the TETRAS score for spiral drawing severity when the tremor severity was rated “slight tremor” or worse. Discussion: This digital measure provides a simple and clinically relevant evaluation of kinetic tremor amplitude that shows promise as a potential future endpoint for use in clinical trials of essential tremor.

Estimating Disability in Patients with Essential Tremor: Comparison of Tremor Rating Scale, Spiral Drawing, and Accelerometric Tremor Power.

Although performance rating scales, spiral drawing, water pouring, and accelerometry are commonly used to assess tremor severity, the extent to which their results correlate with impairment in activities of daily living (ADL) remains unclear. The aim was to identify the most effective predictors of ADL in essential tremor (ET). Forty ET patients were examined using The Essential Tremor Rating Assessment Scale (TETRAS), spiral drawing, volume of water spilled, and accelerometric tremor power. Root-mean-square error, R2, and F-test were calculated for models predicting TETRAS ADL subscore. TETRAS Performance Subscale explained the variability in TETRAS ADL with an R2 value of 0.686. Models incorporating spiral rating and accelerometric tremor power (R2 = 0.731) and water spillage volume (R2 = 0.756) were not statistically superior. TETRAS performance subscore predicted nearly 70% ADL impairment in ET patients. Incorporating the spiral rating, accelerometric tremor power, and water pouring test did not enhance ADL estimation.

Cognitive status correlates of subclinical action tremor in female carriers of FMR1 premutation.

There is evidence for a significant excess of kinetic upper limb tremor in non-FXTAS female FMR1 premutation carriers. The present study explores the possibility that this tremor is associated with various other features reminiscent of those occurring in syndromic FXTAS. This study analyzed the data from an Australian cohort of 48 asymptomatic premutation women. We utilized spiral drawings from CRST, representing action tremor; the CRST total tremor; and ICARS- kinetic tremors/cerebellar ataxia scales. Cognitive tests (involving executive functioning) included SDMT, TMT, two subtests of the WAIS-III: MR and Similarities. Spearman Rank correlations assessed the relationships between the above measures, and the Chi-square tested hypothesis about the association between the white matter hyperintensities (wmhs) in the splenium of corpus callosum assessed from MR images and spiral drawings scores. The spiral drawing scores were significantly correlated with all three non-verbal cognitive test scores, and with the CRST scores; the latter correlated with all four cognitive test measures. Similarities (verbal) scores correlated with CRST, ICARS, and with the remaining cognitive scores. Ordered spiral scores' categories were significantly associated with the degree of splenium involvement. This study showed that, in non-FXTAS premutation female carriers, sub-symptomatic forms of kinetic tremor were associated with a broader motor, and cognitive (especially executive) dysfunction.

A Machine Learning Approach for Early Identification of Prodromal Parkinson's Disease.

Parkinson's disease (PD) affects approximately 6 million people worldwide. Data analysis of early PD symptoms using machine learning (ML) models may provide an inexpensive, non-invasive, and simple method for the remote diagnosis of early PD. The aim of this project was to analyze voice, computer keystrokes, spiral drawings, and gait data involving PD patients and controls available in public databases using ML models and identify early PD characteristics that are more pronounced than others. An ML model was developed using Random Forest to analyze existing clinical data for PD patients, prodromal PD patients with REM (rapid eye movement) sleep behavior disorder (RBD) symptoms, and non-PD healthy controls. We reviewed and collected data from the UCI (University of California Irvine) Machine Learning Repository, PPMI (Parkinson's Progression Markers Initiative), and Kaggle databases. ML analysis was carried out on voice samples in PD and RBD patients, computer keystroke data, spiral drawings, and gait datasets. The ML prediction model developed may be helpful in improving risk prediction for PD, enabling early intervention and resource prioritization. The ML study suggests that voice analysis is the most robust test, followed by computer keystroke data, spiral drawings, and gait analysis, in that order. Voice is affected even in RBD patients, revealing that it is a sensitive and early measure of prodromal PD. The low accuracy of the analysis indicates that several PD-positive samples may remain undetected and unclassified. Combining all four features, that is, voice analysis, computer keystroke data, spiral drawings, and gait analysis, may improve the overall accuracy.

Parkinson’s Detection Using Voice Features and Spiral Drawings

Parkinson's is a dynamic neurodegenerative disease that presents multiple symptoms that advance over time. Our project proposes an innovative Parkinson's discovery machine learning model that combines both voice examination and spiral drawings assessments to capture numerous angles of the disease's symptomatology. Our approach looks for developing a comprehensive Parkinson’s detection model over different stages and symptoms of the disease. By integrating voice analysis techniques to discern subtle changes in speech patterns and spiral drawing assessments to evaluate motor function, our method aims to provide a more holistic assessment of PD symptoms. By leveraging the complementary strengths of voice analysis and spiral drawing assessments, our proposed PD detection project aims to overcome the limitations of existing approaches and provide clinicians with a more comprehensive model for early detection, diagnosis and monitoring of Parkinson's Disease. Ultimately, this initiative strives to enhance patient outcomes, improve treatment efficacy, and advance our understanding of PD progression.

Early Detection of Parkinson’s Disease Using Machine Learning

A hallmark of Parkinson's disease is the degeneration of dopaminergic neurons in the midbrain's substantia nigra pars compacta. However, machine learning is needed to design and implement early Parkinson's disease detection. by using machine learning methods such as CNN and SVM, which can reliably identify voice signals and spiral images to identify early indications of Parkinson's disease. Using machine learning on handwriting, tremor, and gait datasets, the method addresses the shortcomings of individual analyses for a more complete diagnosis solution by investigating relationships between symptoms. This increases accuracy. When voice and spiral drawing data were combined, Parkinson's disease diagnosis accuracy showed promise, with the machine learning model successfully differentiating between unaffected patients and those who were affected. This observation suggests a viable path for precise Parkinson's disease diagnosis: an integrated method that combines machine learning techniques with data from spiral drawings and voice.

Subthalamic stimulation modulates context-dependent effects of beta bursts during fine motor control

Increasing evidence suggests a considerable role of pre-movement beta bursts for motor control and its impairment in Parkinson’s disease. However, whether beta bursts occur during precise and prolonged movements and if they affect fine motor control remains unclear. To investigate the role of within-movement beta bursts for fine motor control, we here combine invasive electrophysiological recordings and clinical deep brain stimulation in the subthalamic nucleus in 19 patients with Parkinson’s disease performing a context-varying task that comprised template-guided and free spiral drawing. We determined beta bursts in narrow frequency bands around patient-specific peaks and assessed burst amplitude, duration, and their immediate impact on drawing speed. We reveal that beta bursts occur during the execution of drawing movements with reduced duration and amplitude in comparison to rest. Exclusively when drawing freely, they parallel reductions in acceleration. Deep brain stimulation increases the acceleration around beta bursts in addition to a general increase in drawing velocity and improvements of clinical function. These results provide evidence for a diverse and task-specific role of subthalamic beta bursts for fine motor control in Parkinson’s disease; suggesting that pathological beta bursts act in a context dependent manner, which can be targeted by clinical deep brain stimulation.

Handwriting Movement Abnormalities in Older Adults with Silent Cerebral Small Vessel Disease-A Preliminary Study.

The features of cerebral small vessel disease (CSVD) range from occurrence of asymptomatic radiological markers to symptomatic characteristics that include cognitive deficits and gait decline. The aim of the present study was to examine whether handwriting movement is abnormal in older people with CSVD through handwriting and drawing tasks using digitized handwriting kinematic assessment technology. Older subjects (n = 60) were grouped according to Fazekas score, with 16 in the Severe CSVD group, 12 in the Non-severe group and 32 in the Healthy group. Kinematic data were recorded and analyzed during handwriting and drawing tasks: signature; writing of Chinese characters ("" and ""); and Archimedes' spiral drawing. The Severe CSVD group showed lower velocity and higher tortuosity during signature writing, lower velocity of stroke #4 of "" and vertical size of "" than did the Non-severe and Healthy groups. Both Severe CSVD and Non-severe CSVD subjects displayed higher average normalized jerk than did the Healthy group. Partial correlation analysis adjusting for age, gender, education, and mini-mental state evaluation (MMSE) showed that CSVD burden was positively associated with tortuosity of signature and average normalized jerk of Archimedes' spiral, and was negatively associated with velocity of strokes #3 and #4 of "", as well as vertical size of "". Older adults with CSVD showed abnormal handwriting movement. And the handwriting abnormalities captured by digitized handwriting analysis were correlated with CSVD severity in users of simplified Chinese characters.

Early diagnosis of Parkinson disorder based on spiral drawing image modalities using hybrid transfer learning architecture

The Spiral Drawing Test (SDT) has become a prominent clinical marker for the early diagnosis of Parkinson’s Disorder (PD) by capturing tremor symptoms. The integration of AI algorithms into a PD diagnosis system has proven to be a breakthrough objective assessment that aids professionals in decision-making. However, there is a need for improvisation of the workflow architectures of AI models to optimize the diagnosis system by reducing the misdiagnosis rate. The proposed system presents PD prediction using a Spiral Drawing Test (SDT) image modality integrated with an Artificial Intelligence (AI) algorithm. The proposed study presents three hybrid workflow architectures formed by integrating three core layers: a data augmentation layer, Transfer Layer (TL)-based feature extraction layer, and Deep Learning (DL)-based classification layer. The results were analyzed by conducting 18 experiments based on the hyperparameter values and workflow architectures. The highest accuracy obtained by the proposed study is 98% for Hybrid Workflow Architecture II.

Modeling and diagnosis Parkinson disease by using hand drawing: deep learning model

<abstract> <p>Patients with Parkinson's disease (PD) often manifest motor dysfunction symptoms, including tremors and stiffness. The presence of these symptoms may significantly impact the handwriting and sketching abilities of individuals during the initial phases of the condition. Currently, the diagnosis of PD depends on several clinical investigations conducted inside a hospital setting. One potential approach for facilitating the early identification of PD within home settings involves the use of hand-written drawings inside an automated PD detection system for recognition purposes. In this study, the PD Spiral Drawings public dataset was used for the investigation and diagnosis of PD. The experiments were conducted alongside a comparative analysis using 204 spiral and wave PD drawings. This study contributes by conducting deep learning models, namely DenseNet201 and VGG16, to detect PD. The empirical findings indicate that the DenseNet201 model attained a classification accuracy of 94% when trained on spiral drawing images. Moreover, the model exhibited a receiver operating characteristic (ROC) value of 99%. When comparing the performance of the VGG16 model, it was observed that it attained a better accuracy of 90% and exhibited a ROC value of 98% when trained on wave images. The comparative findings indicate that the outcomes of the proposed PD system are superior to existing PD systems using the same dataset. The proposed system is a very promising technological approach that has the potential to aid physicians in delivering objective and dependable diagnoses of diseases. This is achieved by leveraging important and distinctive characteristics extracted from spiral and wave drawings associated with PD.</p> </abstract>

Assessing parkinsonism & cerebellar dysfunction with spiral & line drawings

Background abd purpose – Recognising neurological diseases is challenging without accurate diagnostic tools. Therefore, many approaches have been taken to recognise and evaluate these diseases through speech, movement, or drawing modalities. The purpose of the study is to compare the recognition of Parkinson’s and cerebellar symptoms using spiral and line drawings recorded from the same subjects. We also investigate the importance of pin pressure in classification. Furthermore, an attempt is made to use the two types of drawings together for more accurate classification. Methods – Images were generated from the raw data with and without pressure data. We then performed classification with the help of pre-trained and own deep learning feature extraction models. Mann-Whitney U test is used to test the significance of the results with a 0.05 significance level. Results – The results showed that spiral drawings significantly performed better than lines (p-value: 0.001). Furthermore, combining the two types of drawings improves recognition when pressure is available (p-value: 0.017). However, no performance degradation can be expected without pressure data using one drawing task (p-value: 0.507). Conclusion – The spiral is recommended as the primary drawing, but combining multiple drawings can contribute to a more confident recognition. By excluding pressure, no significant decrease is expected in the model’s performance.

Enhancing Parkinson's Disease Recognition through Multimodal Analysis of Archimedean Spiral Drawings

Parkinson's disease is one of the most common neurodegenerative diseases, which is incurable according to recent clinical knowledge. Evaluating motor symptoms across diverse modalities such as speech, handwriting, and movement composes a conventional diagnostic approach. However, concurrently utilizing multimodal datasets encompassing drawing and acceleration data remains an underexplored f ield. Our investigation involved examining drawing and movement data of 45 Parkinson's disease (PD) patients and 47 healthy individuals (HC). The PD group presented mild symptoms in the right hand. We transformed drawing data into spiral images and used visual representations of motion data, employing pre-trained models for feature extraction and classifiers. While motion representations exhibited superior performance compared to drawing images, a comprehensive evaluation with the Mann-Whitney U test at a significance level of 0.05 revealed no statistically significant difference between the efficacy of movement and drawing data in all classification scenarios. Significant improvements were made by combining the drawing data predictions with the motion data predictions. The key finding of the research is that the recognition of the disease can be improved by connecting (post-model) the two modalities. Furthermore, it can be concluded that with the present approach, neither the drawing nor the movement data produced lower results on average.

Direct Comparisons of Upper-Limb Motor Learning Performance Among Three Types of Haptic Guidance With Non-Assisted Condition in Spiral Drawing Task.

In robot-assisted rehabilitation, it is unclear which type of haptic guidance is effective for regaining motor function because of the lack of direct comparisons among multiple types of haptic guidance. The objective of this study was to investigate the effects of different types of haptic guidance on upper limb motor learning in a spiral drawing task. Healthy young participants performed two experiments in which they practiced the drawing movement using a robotic manipulandum with a virtual wall (Path guidance), running direction pushing and virtual wall (Path & Push guidance), restriction to the target movement (Target guidance), or without haptic guidance (Free guidance). Experiment 1 compared the learning effects of the four types of guidance. Experiment 2 investigated the effects of pre-learning with Path, Path & Push, or Target guidance on post-learning with Free guidance. In Experiment 1, Free guidance demonstrated the greatest learning effect, followed by Path guidance, which showed a significantly greater improvement in task performance than the other two types of guidance. In Experiment 2, the type of pre-learning did not influence post-learning with Free guidance. The results suggested that learning with Path guidance showed a slightly slower but comparable effect to Free guidance and was the most effective among the three types of haptic guidance. The superiority of Path guidance over other haptic guidance was interpreted within the framework of error-based learning, in which the intensity of sensory feedback and voluntary motor control play important roles.

94 Digitized Archimedes Spiral Drawing in the NKI-Rockland Sample

Objective:Digital cognitive assessments (DCAs) provide insight into cognition and behavior that remains inaccessible through standard assessment approaches. However, the availability of DCAs and the requisite toolkits to extract and analyze meaningful features from these datasets are largely constrained to technical specialists or through fee-for-service commercial entities. The NKI- Rockland Sample provides a large- scale lifespan data sample featuring DCAs, and also openly shares its DCA tasks through the open-source MindLogger platform along with pipelines for feature extraction and analyses. Here we present normative performance from a digital version of Archimedes Spiral Drawing.Participants and Methods:NKI-RS2 participants were largely drawn from the existing NKI-RS participant pool (n= 1,500), aged 885. The NKI-RS2 is in year 1 of data collection; here, we report on a subset of participants (n= 9) who performed a digitized version of the Archimedes Spiral Drawing task. This graphomotor task with well-established research and clinical utility in movement disordered populations was adapted for use for off-the-shelf tablet devices. The NKI-RS2 implements these tasks on an Apple iPad Pro2, sampling participant drawing at 120Hz, and featuring pixel- and millisecond- level resolution for all tasks. On the Spiral Drawing and Recall Tests participants traced five Archimedes spirals from the center outward through four windings presented on the iPad. They were then asked to replicate the spiral freehand three times. From these spiral drawings, we extracted time to completion, distance covered, speed/ speed variability, rotational smoothness, number of crossings, mean absolute error, bias, and goodness of fit to the ideal Archimedes spiral.Results:Comparing the tracing and recall conditions, participants showed significantly faster drawing speed (t[8]=5.32, p< .001), more variable drawing speed (t[8]=5.93, p< .001), reduced goodness of fit to the template t[8]=4.99, p< .002, and reduced rotational smoothness (t[8]=7.43, p< .0003) in the recall conditions. Collapsing across conditions, age predicted more variable drawing speed: t[8]= 2.77 p< .019, greater tracing error (t[8] = 2.69, p< .0227), and reduced rotational smoothness (t[8] = 2.67, p< .024). Between conditions, age predicts a greater increase in drawing speed variability (t[8] = 9.76, p< .0006).Conclusions:Using the open source MindLogger platform and off-the-shelf digital tablets, we were able to replicate classic paper and pen neuropsychological tests. By adapting these tasks to DCA, we were able to extract meaningful features that are not otherwise accessible (drawing speed, variability, etc.), or that would require additional hardware solutions (e.g., dwell time). By making these tasks and their processing pipelines available, the NKI-RS2 can facilitate the democratization of DCA and DCA analysis to a broader range of researchers and clinicians.

Parkinson Disease Detection by Analyzing Spiral and Wave Drawings

Abstract: Parkinson's disease is a ubiquitous, life altering neurological disorder that affects countless people and is often undiagnosed until its advanced stages. This delay in diagnosis means there is no time for timely intervention and better disease management. Our implementation process is set to change this narrative through the use of multimedia data processing techniques and technologies. Our system begins its mission by capturing and analyzing spiral images, which is a new method for diagnosing Parkinson's disease. It uses advanced image recognition technology to interpret specific patterns in these images, providing insight into possible early stages of the disease. Focused on the analysis of spiral images, our solutions aim to revolutionize the diagnostic process, providing people and doctors with powerful tools to diagnose Parkinson's disease earlier and more easily. This change has the potential to improve quality of life and reduce the burden of Parkinson's disease.

Spiral drawing deficits in children with prenatal alcohol exposure.

Empirical investigations reveal that, in comparison to their typically developing peers, children with histories of prenatal alcohol exposure experience deficits in writing but not drawing skills,both of which require fine motor control. This study examines drawing skills in this clinical group by assessing simple free-form spiral drawings with indices of spectral features and structural organization. Children with (n = 15) and without (n = 24) prenatal alcohol exposure used their dominant and nondominant hands to draw a series of spirals using a wireless pen stylus that either provided concurrent visual feedback in the form of a black ink trace or left no visible ink trace of each drawing. Spirals were drawn on a sheet of paper placed on a digitizing table, which facilitated online data acquisition. The data were assessed by power spectral density function analysis and sample entropy analysis. In comparison to their typically developing peers, children with prenatal alcohol exposure produced spirals with a lower mean frequency and less spectral variability. Spirals in the prenatally exposed group were also lower in complexity and structural organization than in the control group. These results occurred independently of hand dominance or the availability of visual feedback. The drawing skills of children with prenatal alcohol exposure have inherent signal characteristics that differ significantly from those produced by typically developing peers. Simple tasks requiring fine motor control may be useful in identifying individuals with fetal alcohol spectrum disorders.

Sobel edge detection for quantifying the effectiveness of focused ultrasound thalamotomy for tremor relief

Abstract Focused ultrasound on the thalamus is employed to relieve tremor in movement disorders. The treatment efficacy is currently assessed by subjective observation of patient tremor during hand movements. The purpose of this study was to create a simple quantitative method to assess tremor based on traditional pen-and-paper spiral drawing that could be employed during treatment and follow-up visits. Image processing was employed to automatically and rapidly clean scanned spiral and line drawings and to isolate the minimal part of the drawing that could provide sufficient data for tremor detection. Sobel edge detection and easily-interpretable statistics of the distribution of edge-angle orientations in the spirals relative to the spiral centres were used as tremor measures. The method was applied to scanned drawings of 122 tremor patients before treatment and in follow-up visits. Discrimination analysis of the tremor measure between drawings in the session before treatment and the follow-up sessions was carried out. The processing yielded correctly cropped and clean spirals. The tremor measure demonstrated consistent tremor reduction in the treated hand drawings in all five follow-up visits during the first year following treatment date in 76 patients from the study cohort. In this study we developed a computational method that rapidly computed tremor severity estimate in an explainable and clinically useful manner from a traditional pen-and-paper spiral drawing. This rapid quantitative and objective tremor assessment can easily be implemented during focused ultrasound treatment and replace the subjective assessment currently employed.