Identification and treatment of individuals with attentiondeficit/ hyperactivity disorder and substance use disorder: An expert consensus statement

Attention-deficit/hyperactivity disorder(ADHD)often co-occurs with substance use(SU)and/or substance use disorder(SUD).Individuals with concurrent ADHD and SU/SUD can have complex presentations that may complicate diagnosis and treatment.This can be further complicated by the context in which services are delivered.Also,when working with young people and adults with co-existing ADHD and SU/SUD,there is uncertainty among healthcare practitioners on how best to meet their needs.In February 2022,the United Kingdom ADHD Partnership hosted a meeting attended by multidisciplinary experts to address these issues.Following presentations providing attendees with an overview of the literature,group discussions were held synthesizing research evidence and clinical experience.Topics included:(1)A review of substances and reasons for use/misuse;(2)identification,assessment and treatment of illicit SU/SUD in young people and adults with ADHD presenting in community services;and(3)identification,assessment and treatment of ADHD in adults presenting in SU/SUD community and inpatient services.Discussions highlighted inter-service barriers and fragmentation of care.It was concluded that a multimodal and multi-agency approach is needed.The consensus group generated a table of practice recommendations providing guidance on:identification and assessment;pharmacological and psychological treatment;and multi-agency interventions.

Enhanced Field-Based Detection of Potato Blight in Complex Backgrounds Using Deep Learning

Rapid and automated identification of blight disease in potato will help farmers to apply timely remedies to protect their produce.Manual detection of blight disease can be cumbersome and may require trained experts.To overcome these issues,we present an automated system using the Mask Region-based convolutional neural network(Mask R-CNN)architecture,with residual network as the backbone network for detecting blight disease patches on potato leaves in field conditions.The approach uses transfer learning,which can generate good results even with small datasets.The model was trained on a dataset of 1423 images of potato leaves obtained from fields in different geographical locations and at different times of the day.The images were manually annotated to create over 6200 labeled patches covering diseased and healthy portions of the leaf.The Mask R-CNN model was able to correctly differentiate between the diseased patch on the potato leaf and the similar-looking background soil patches,which can confound the outcome of binary classification.To improve the detection performance,the original RGB dataset was then converted to HSL,HSV,LAB,XYZ,and YCrCb color spaces.A separate model was created for each color space and tested on 417 field-based test images.This yielded 81.4%mean average precision on the LAB model and 56.9%mean average recall on the HSL model,slightly outperforming the original RGB color space model.Manual analysis of the detection performance indicates an overall precision of 98%on leaf images in a field environment containing complex backgrounds.